TowerVision-2B
94
2
2.0B
19 languages
license:cc-by-nc-sa-4.0
by
utter-project
Image Model
OTHER
2B params
New
94 downloads
Early-stage
Edge AI:
Mobile
Laptop
Server
5GB+ RAM
Mobile
Laptop
Server
Quick Summary
AI model with specialized capabilities.
Device Compatibility
Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
2GB+ RAM
Code Examples
Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Quick Start with Transformerspythontransformers
from transformers import (
LlavaNextProcessor,
LlavaNextForConditionalGeneration
)
import requests
from PIL import Image
model_id = "utter-project/TowerVision-2B" # or any other variant
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
return prompt
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")
# Prepare inputs
inputs = processor(
text=prompt, images=image, return_tensors="pt"
).to(model.device)
# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Batch Inference with Transformerspython
def prepare_prompts(queries):
prompts = []
for query in queries:
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
prompt = processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
prompts.append(prompt)
return prompts
# we recommend using "bfloat16" as torch_dtype
kwargs = {
"torch_dtype": "bfloat16",
"device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)
# Sample images and queries for batch processing
img_urls = [
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
"https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]
queries = [
"Is this person really big, or is this building just super small?",
"Where was this photo taken?"
]
# Load images
images = []
for url in img_urls[:batch_size]:
image = Image.open(requests.get(url, stream=True).raw)
images.append(image)
# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])
# Prepare batch inputs
inputs = processor(
text=prompts,
images=images,
return_tensors="pt",
padding=True
).to(model.device)
# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)
# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)
for i in range(len(images)):
input_length = inputs.input_ids[i].shape[0]
response = processor.tokenizer.decode(
gen_tokens[i][input_length:],
skip_special_tokens=True
)
print(f"Response: {response}")
print("-" * 50)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Pipeline Usagepythontransformers
from transformers import pipeline
from PIL import Image
import requests
pipe = pipeline(
model="utter-project/TowerVision-9B",
task="image-text-to-text",
device_map="auto",
dtype="bfloat16"
)
def prepare_prompt(query):
conversation = [
{
"role": "user",
"content": f"<image>\n{query}"
}
]
# Format message with the towervision chat template
return pipe.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")
outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)Deploy This Model
Production-ready deployment in minutes
Together.ai
Instant API access to this model
Production-ready inference API. Start free, scale to millions.
Try Free APIReplicate
One-click model deployment
Run models in the cloud with simple API. No DevOps required.
Deploy NowDisclosure: We may earn a commission from these partners. This helps keep LLMYourWay free.