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---
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")
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