Aloe-Vision-7B-AR

36
7.0B
1 language
license:cc-by-nc-sa-4.0
by
HPAI-BSC
Language Model
OTHER
7B params
New
36 downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
16GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
7GB+ RAM

Code Examples

How to Usepythontransformers
import torch
from transformers import AutoProcessor, AutoModelForVision2Seq

model_id = "HPAI-BSC/Aloe-Vision-7B-AR"

processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForVision2Seq.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "path/to/your_image.png"},
            {"type": "text", "text": "What abnormality do you observe? Be concise."}
        ]
    }
]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs = processor.process_vision_info(messages)
inputs = processor(
    text=[text],
    **image_inputs,
    return_tensors="pt"
).to(model.device)

generated = model.generate(
    **inputs,
    max_new_tokens=256,
    do_sample=False,
    eos_token_id=processor.tokenizer.eos_token_id,
)
output_text = processor.batch_decode(generated, skip_special_tokens=True)[0]
print(output_text.split(text)[-1].strip())
How to Usepythontransformers
import torch
from transformers import AutoProcessor, AutoModelForVision2Seq

model_id = "HPAI-BSC/Aloe-Vision-7B-AR"

processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForVision2Seq.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "path/to/your_image.png"},
            {"type": "text", "text": "What abnormality do you observe? Be concise."}
        ]
    }
]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs = processor.process_vision_info(messages)
inputs = processor(
    text=[text],
    **image_inputs,
    return_tensors="pt"
).to(model.device)

generated = model.generate(
    **inputs,
    max_new_tokens=256,
    do_sample=False,
    eos_token_id=processor.tokenizer.eos_token_id,
)
output_text = processor.batch_decode(generated, skip_special_tokens=True)[0]
print(output_text.split(text)[-1].strip())
How to Usepythontransformers
import torch
from transformers import AutoProcessor, AutoModelForVision2Seq

model_id = "HPAI-BSC/Aloe-Vision-7B-AR"

processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForVision2Seq.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "path/to/your_image.png"},
            {"type": "text", "text": "What abnormality do you observe? Be concise."}
        ]
    }
]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs = processor.process_vision_info(messages)
inputs = processor(
    text=[text],
    **image_inputs,
    return_tensors="pt"
).to(model.device)

generated = model.generate(
    **inputs,
    max_new_tokens=256,
    do_sample=False,
    eos_token_id=processor.tokenizer.eos_token_id,
)
output_text = processor.batch_decode(generated, skip_special_tokens=True)[0]
print(output_text.split(text)[-1].strip())

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