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jiangchengchengNLP/
qwen2.5-distill-QWQ

Name: qwen2.5-distill-QWQ
Availability: InStock

amphora/QwQ-LongCoT-130K

Qwen/Qwen2.5-Coder-7B-Instruct

adapter

apache-2.0

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Model Details

This model is a distilled version of a smaller model based on Qwen/QwQ-32B-Preview, fine-tuned on 60,000 math-related data samples from amphora/QwQ-LongCoT-130K. The base model used is qwen2.5-coder-7B-Instruct, and the fine-tuning method is LORA. The tokenizer QWQ remains consistent, and the model demonstrates its best inference capabilities in English.

Uses

To use the model, follow the code below:

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("jiangchengchengNLP/qwen2.5-distill-QWQ")
base_model = AutoModelForCausalLM.from_pretrained(r"Qwen/Qwen2.5-Coder-7B-Instruct", device_map='auto', torch_dtype="bfloat16")
model = PeftModel.from_pretrained(base_model, "jiangchengchengNLP/qwen2.5-distill-QWQ")

prompt = "how many `r` in `strawberry`？"
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096,
    top_p=0.8,
    temperature=0.2
)

generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Evaluation

Thanks to the teacher(QWQ), the performance of qwen2.5-distill-QWQ on MATH exceeded that of Qwen2.5-Math-72B by 66.8%, reaching 67.01%.

Apply Simple Test Time Scaling to strengthen COT of Model

python

from huggingface_hub import snapshot_download
thinker_lora_path=snapshot_download(repo_id="jiangchengchengNLP/qwen2.5-distill-QWQ")
from vllm import LLM,SamplingParams
from vllm.lora.request import LoRARequest
from transformers import AutoTokenizer

# Decide on a token limit for thinking; As the model's max tokens is 32768, 32000 usually ensures there is enough space for the model to still answer
MAX_TOKENS_THINKING = 32000
# Decide how often to ignore end-of-thinking token
NUM_IGNORE = 1
model=LLM(model="Qwen/Qwen2.5-Coder-7B-Instruct",enable_lora=True)
model=LLM(model=model_path)
tokenizer=AutoTokenizer.from_pretrained(
    "jiangchengchengNLP/qwen2.5-distill-QWQ"
)
stop_token_ids=tokenizer("<|im_end|>")['input_ids']
sampling_params=SamplingParams(
    max_tokens=MAX_TOKENS_THINKING,
    min_tokens=0,
    stop_token_ids=stop_token_ids,
    skip_special_tokens=True,
    temperature=0.0
)
# For the math sample 
import re
pattn=re.compile("\*\*Final Answer\*\*.*",re.S)

prompts=[
    """Given positive real numbers $a$ and $b$ satisfy $a+b=1$, then $M=$ $\sqrt{1+a^{2}}+\sqrt{1+2 b}$ the integer part is ? """,
]
for i,p in enumerate(prompts):
    messages=[
        {'role':'user','content':p}
    ]
    prompt=tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        tokenize=False
    )
    o=model.generate(
        prompt,
        sampling_params=sampling_params,
        lora_request=LoRARequest("thinker_adapter", 1, thinker_lora_path)
    )
    ignore_str="Wait a minute"
    max_tokens_thinking_tmp = MAX_TOKENS_THINKING
    if max_tokens_thinking_tmp>0:
        for i in range(NUM_IGNORE):
            max_tokens_thinking_tmp-=len(o[0].outputs[0].token_ids)
            generate_text=o[0].outputs[0].text
            drop_text=pattn.findall(generate_text)
            if drop_text:
                generate_text=generate_text.replace(drop_text[0],"")
            prompt+=generate_text+ignore_str
            sampling_params = SamplingParams(
                max_tokens=max_tokens_thinking_tmp,
                min_tokens=1,
                stop_token_ids=stop_token_ids,
                skip_special_tokens=True,
                temperature=0.0,
            )
            o = model.generate(
                prompt,
                sampling_params=sampling_params,
                lora_request=LoRARequest("thinker_adapter", 1, thinker_lora_path)
            )
generate_text=o[0].outputs[0].text
drop_text=pattn.findall(generate_text)
if drop_text:
    generate_text=generate_text.replace(drop_text[0],"")
prompt+=generate_text+"\n"+"**Final Answer**"
sampling_params = SamplingParams(
                max_tokens=max_tokens_thinking_tmp,
                min_tokens=1,
                stop_token_ids=stop_token_ids,
                skip_special_tokens=False,
                temperature=0.0,
            )
o=model.generate(
        prompt,
        sampling_params=sampling_params,
        lora_request=LoRARequest("thinker_adapter", 1, thinker_lora_path)
    )
print("With budget forcing:") 
print(prompt + o[0].outputs[0].text)

If you want to combine lora weights into one model then use the following code

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("jiangchengchengNLP/qwen2.5-distill-QWQ")
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct",device_map='cpu',torch_dtype="bfloat16")
model = PeftModel.from_pretrained(base_model, "jiangchengchengNLP/qwen2.5-distill-QWQ")
mergemodel = model.merge_and_unload()
mergemodel.save_pretrained("./merge_model")
tokenizer.save_pretrained("./merge_model")
print("model have merged!")

PEFT 0.14.0