---
license: apache-2.0
datasets:
- KingNish/reasoning-base-20k
language:
- en
- zh
base_model:
- Qwen/Qwen2.5-1.5B
---

## Uses

        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")