43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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def generate_response(model_name: str, prompt: str, max_new_tokens: int = 512) -> str:
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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messages = [
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{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=max_new_tokens
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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return response
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if __name__ == "__main__":
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model_name = "BlenderLLM"
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prompt = "Please drow a cube."
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result = generate_response(model_name, prompt)
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print("Generated Response:\n", result)
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