Laurie/

Qwen2.5-1.5B-O1

    from transformers import AutoModelForCausalLM, AutoTokenizer
    
    model_name = "/root/app/Reason/checkpoints"
    
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        torch_dtype="auto",
        device_map="auto"
    )
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    
    from typing import List, Dict
    def new_apply_chat_template(history:List[Dict[str, str]], add_reasoning_generation_prompt:bool=True, add_assistant_generation_prompt:bool=False):
      if add_reasoning_generation_prompt:
        return "".join([f"<|im_start|>{i['role']}\n{i['content']}<|im_end|>\n" for i in history]) + "<|im_start|><|reasoning|>\n"
      if add_assistant_generation_prompt:
        return "".join([f"<|im_start|>{i['role']}\n{i['content']}<|im_end|>\n" for i in history]) + "<|im_start|>assistant\n"
      
    
    from IPython.display import Markdown, display
    device = "cuda"
    history = []
    history.append({"role": "system", "content": "You are a helpful assistant"})
    while True:
        question = input('User：' + '\n')
        print(question)
        print('\n')
        history.append({"role": "user", "content": question})
    
        input_text = new_apply_chat_template(
                history,
                add_reasoning_generation_prompt=True
            )
        model_inputs = tokenizer([input_text], return_tensors="pt").to(device)
    
        if model_inputs.input_ids.size()[1]>32000:
            break
    
        generated_ids = model.generate(
            model_inputs.input_ids,
            max_new_tokens=3000
        )
    
        if len(generated_ids)>32000:
            break
    
        generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
    
        reasoning_response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
        history.append({"role": "<|reasoning|>", "content": reasoning_response})
        print('reasoning:\n')
        #print(response)
        display(Markdown(reasoning_response))
        print("------------")
        print('\n')
    
        input_text = new_apply_chat_template(
                history,
                add_assistant_generation_prompt=True
            )
        model_inputs = tokenizer([input_text], return_tensors="pt").to(device)
    
        if model_inputs.input_ids.size()[1]>32000:
            break
    
        generated_ids = model.generate(
            model_inputs.input_ids,
            max_new_tokens=3000
        )
    
        if len(generated_ids)>32000:
            break
    
        generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
    
        assistant_response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
        history.append({"role": "assistant", "content": assistant_response})
        print('assistant:\n')
        display(Markdown(assistant_response))
        print("------------")
    
    print("超过模型字数上线，已退出")

# 假设tokenizer编码示例
# 不加入词表:
"<|reasoning|>" -> [1234, 5678, 9012]  # 被拆分成多个基础token

# 加入词表:
"<|reasoning|>" -> [32000]  # 单个特殊token