QWEN2.5-7B-Bnk-3e

Name: QWEN2.5-7B-Bnk-3e
Author: FINGU-AI

7.0B

license:mit

FINGU-AI

Language Model

OTHER

7B params

New

2 downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

16GB+ RAM

Mobile

Laptop

Server

Quick Summary

QWEN2.5-7B-Bnk-5e is a multilingual translation model based on the QWEN 2.5 architecture with 7 billion parameters. It specializes in translating multiple langu...

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

7GB+ RAM

Code Examples

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

How to Usepythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
source_text = "Hello, how are you?"
source_lang = "en"
target_lang = "ko"  # or "uz" for Uzbek

messages = [
        {"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""},
        {"role": "user", "content": f"""{source_text}"""},
    ]
# Apply chat template
input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to('cuda')

outputs = model.generate(input_ids, max_length=100)
response = outputs[0][input_ids.shape[-1]:]
translated_text = tokenizer.decode(response, skip_special_tokens=True)
print(translated_text)

Deploy This Model

Production-ready deployment in minutes

Together.ai

Instant API access to this model

Fastest API

Production-ready inference API. Start free, scale to millions.

Try Free API

Replicate

One-click model deployment

Easiest Setup

Run models in the cloud with simple API. No DevOps required.

Deploy Now

Disclosure: We may earn a commission from these partners. This helps keep LLMYourWay free.