Flux Japanese Qwen2.5 32B Instruct V1.0
877
8
32.0B
1 language
license:apache-2.0
by
flux-inc
Language Model
OTHER
32B params
New
877 downloads
Early-stage
Edge AI:
Mobile
Laptop
Server
72GB+ RAM
Mobile
Laptop
Server
Quick Summary
Flux-Japanese-Qwen2.5-32B-Instruct-V1.0 [English] [Japanese] Flux-Japanese-Qwen2.5-32B-Instruct-V1.0 is a 32 billion parameter open-weights models with strong...
Device Compatibility
Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
30GB+ RAM
Code Examples
π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]π© Quickstartpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Deep-Analysis-Research/Flux-Japanese-Qwen2.5-32B-V1.0")
prompt = "倧θ¦ζ¨‘θ¨θͺγ’γγ«γ«γ€γγ¦η°‘εγ«η΄Ήδ»γγ¦γγ γγγ"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]Deploy This Model
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