QwQ-32B-GPTQ_4bit-128g

QwQ-32B-GPTQ_4bit-128g

Thanks to the Qwen team!

<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/> </a>

Replication

Install gptqmodel:

!pip install -v gptqmodel --no-build-isolation

Quantise:

import torch
from datasets import load_dataset
from gptqmodel import GPTQModel, QuantizeConfig
from transformers import AutoTokenizer

def get_wikitext2(tokenizer, nsamples, seqlen):
    traindata = load_dataset("wikitext", "wikitext-2-v1", split="train").filter(
        lambda x: len(x["text"]) >= seqlen)

    return [tokenizer(example["text"]) for example in traindata.select(range(nsamples))]


@torch.no_grad()
def calculate_avg_ppl(model, tokenizer):
    from gptqmodel.utils import Perplexity

    ppl = Perplexity(
        model=model,
        tokenizer=tokenizer,
        dataset_path="wikitext",
        dataset_name="wikitext-2-raw-v1",
        split="train",
        text_column="text",
    )

    all = ppl.calculate(n_ctx=512, n_batch=512)

    # average ppl
    avg = sum(all) / len(all)

    return avg

pretrained_model_id = "Qwen/QwQ-32B"
quantized_model_id = "QwQ-32B-GPTQ_4bit-128g"

tokenizer = AutoTokenizer.from_pretrained(pretrained_model_id, use_fast=True)

traindataset = get_wikitext2(tokenizer, nsamples=512, seqlen=1024)

quant_config = QuantizeConfig(
    bits=4,
    group_size=128,
    desc_act=False,
    damp_percent=0.01,
    )

# load un-quantized model, the model will always be force loaded into cpu
model = GPTQModel.load(pretrained_model_id, quant_config)

# quantize model, the calibration_dataset should be list of dict whose keys can only be "input_ids" and "attention_mask"
# with value under torch.LongTensor type.
model.quantize(traindataset)

# save quantized model using safetensors
model.save(quantized_model_id)

Push to hub (Optional)

# Define the repository name where the model will be saved
repo = f"GSJL/{quantized_model_id}"

# Push the quantized model and tokenizer to the Hugging Face Hub
GPTQModel.push_to_hub(repo, quantized_model_id)

Introduction

QwQ is the reasoning model of the Qwen series. Compared with conventional instruction-tuned models, QwQ, which is capable of thinking and reasoning, can achieve significantly enhanced performance in downstream tasks, especially hard problems. QwQ-32B is the medium-sized reasoning model, which is capable of achieving competitive performance against state-of-the-art reasoning models, e.g., DeepSeek-R1, o1-mini.

<p align="center"> <img width="100%" src="figures/benchmark.jpg"> </p>

This repo contains the QwQ 32B model, which has the following features:

• Type: Causal Language Models
• Training Stage: Pretraining & Post-training (Supervised Finetuning and Reinforcement Learning)
• Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
• Number of Parameters: 32.5B
• Number of Paramaters (Non-Embedding): 31.0B
• Number of Layers: 64
• Number of Attention Heads (GQA): 40 for Q and 8 for KV
• Context Length: Full 131,072 tokens

Note: For the best experience, please review the usage guidelines before deploying QwQ models.

You can try our demo or access QwQ models via QwenChat.

For more details, please refer to our blog, GitHub, and Documentation.

Requirements

QwQ is based on Qwen2.5, whose code has been in the latest Hugging face transformers. We advise you to use the latest version of transformers.

With transformers<4.37.0, you will encounter the following error:

KeyError: 'qwen2'

Quickstart

Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qwen/QwQ-32B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "How many r's are in the word \"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=32768
)
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)

Usage Guidelines

To achieve optimal performance, we recommend the following settings:

1. Enforce Thoughtful Output: Ensure the model starts with "<think>\n" to prevent generating empty thinking content, which can degrade output quality. If you use apply_chat_template and set add_generation_prompt=True, this is already automatically implemented, but it may cause the response to lack the <think> tag at the beginning. This is normal behavior.

2. Sampling Parameters:

   • Use Temperature=0.6 and TopP=0.95 instead of Greedy decoding to avoid endless repetitions.
   • Use TopK between 20 and 40 to filter out rare token occurrences while maintaining the diversity of the generated output.

3. Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking.

   • Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
   • Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the answer field with only the choice letter, e.g.,\"answer\": \"C\"." in the prompt.

4. Handle Long Inputs: For inputs exceeding 32,768 tokens, enable YaRN to improve the model's ability to capture long-sequence information effectively.

For supported frameworks, you could add the following to config.json to enable YaRN:

{
  ...,
  "rope_scaling": {
    "factor": 4.0,
    "original_max_position_embeddings": 32768,
    "type": "yarn"
  }
}

For deployment, we recommend using vLLM. Please refer to our Documentation for usage if you are not familar with vLLM. Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. We advise adding the rope_scaling configuration only when processing long contexts is required.

Evaluation & Performance

Detailed evaluation results are reported in this 📑 blog.

For requirements on GPU memory and the respective throughput, see results here.

Citation

If you find our work helpful, feel free to give us a cite.

@misc{qwq32b,
    title = {QwQ-32B: The Power of Scaling RL},
    url = {https://qwenlm.github.io/blog/qwq-32b/},
    author = {Qwen Team},
    month = {March},
    year = {2025}
}

@article{qwen2,
      title={Qwen2 Technical Report}, 
      author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
      journal={arXiv preprint arXiv:2407.10671},
      year={2024}
}