🧠

qwq-32b-preview

by qwen Model ID: hf-model--qwen--qwq-32b-preview
FNI 7.1
Top 85%

"**QwQ-32B-Preview** is an experimental research model developed by th......"

🔗 View Source
Audited 7.1 FNI Score
32.76B Params
4k Context
Hot 81.5K Downloads
H100+ ~28GB Est. VRAM

⚡ Quick Commands

đŸĻ™ Ollama Run
ollama run qwq-32b-preview
🤗 HF Download
huggingface-cli download qwen/qwq-32b-preview
đŸ“Ļ Install Lib
pip install -U transformers
📊

Engineering Specs

⚡ Hardware

Parameters
32.76B
Architecture
Qwen2ForCausalLM
Context Length
4K
Model Size
61.0GB

🧠 Lifecycle

Library
-
Precision
float16
Tokenizer
-

🌐 Identity

Source
HuggingFace
License
Open Access
💾

Est. VRAM Benchmark

~27.1GB

Analyze Hardware

* Technical estimation for FP16/Q4 weights. Does not include OS overhead or long-context batching. For Technical Reference Only.

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đŸ”ŦTechnical Deep Dive

Full Specifications [+]
---

🚀 What's Next?

⚡ Quick Commands

đŸĻ™ Ollama Run
ollama run qwq-32b-preview
🤗 HF Download
huggingface-cli download qwen/qwq-32b-preview
đŸ“Ļ Install Lib
pip install -U transformers
đŸ–Ĩī¸

Hardware Compatibility

Multi-Tier Validation Matrix

Live Sync
🎮 Compatible

RTX 3060 / 4060 Ti

Entry 8GB VRAM
🎮 Compatible

RTX 4070 Super

Mid 12GB VRAM
đŸ’ģ Compatible

RTX 4080 / Mac M3

High 16GB VRAM
🚀 Compatible

RTX 3090 / 4090

Pro 24GB VRAM
đŸ—ī¸ Compatible

RTX 6000 Ada

Workstation 48GB VRAM
🏭 Compatible

A100 / H100

Datacenter 80GB VRAM
â„šī¸

Pro Tip: Compatibility is estimated for 4-bit quantization (Q4). High-precision (FP16) or ultra-long context windows will significantly increase VRAM requirements.

README

QwQ-32B-Preview

Chat

Introduction

QwQ-32B-Preview is an experimental research model developed by the Qwen Team, focused on advancing AI reasoning capabilities. As a preview release, it demonstrates promising analytical abilities while having several important limitations:

  1. Language Mixing and Code-Switching: The model may mix languages or switch between them unexpectedly, affecting response clarity.
  2. Recursive Reasoning Loops: The model may enter circular reasoning patterns, leading to lengthy responses without a conclusive answer.
  3. Safety and Ethical Considerations: The model requires enhanced safety measures to ensure reliable and secure performance, and users should exercise caution when deploying it.
  4. Performance and Benchmark Limitations: The model excels in math and coding but has room for improvement in other areas, such as common sense reasoning and nuanced language understanding.

Specification:

  • Type: Causal Language Models
  • Training Stage: Pretraining & Post-training
  • 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 32,768 tokens

For more details, please refer to our blog. You can also check Qwen2.5 GitHub, and Documentation.

Requirements

The code of Qwen2.5 has been in the latest Hugging face transformers and 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-Preview"

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

prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"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=512
)
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]

Citation

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

@misc{qwq-32b-preview,
    title = {QwQ: Reflect Deeply on the Boundaries of the Unknown},
    url = {https://qwenlm.github.io/blog/qwq-32b-preview/},
    author = {Qwen Team},
    month = {November},
    year = {2024}
}

@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}
}
ZEN MODE â€ĸ README

QwQ-32B-Preview

Chat

Introduction

QwQ-32B-Preview is an experimental research model developed by the Qwen Team, focused on advancing AI reasoning capabilities. As a preview release, it demonstrates promising analytical abilities while having several important limitations:

  1. Language Mixing and Code-Switching: The model may mix languages or switch between them unexpectedly, affecting response clarity.
  2. Recursive Reasoning Loops: The model may enter circular reasoning patterns, leading to lengthy responses without a conclusive answer.
  3. Safety and Ethical Considerations: The model requires enhanced safety measures to ensure reliable and secure performance, and users should exercise caution when deploying it.
  4. Performance and Benchmark Limitations: The model excels in math and coding but has room for improvement in other areas, such as common sense reasoning and nuanced language understanding.

Specification:

  • Type: Causal Language Models
  • Training Stage: Pretraining & Post-training
  • 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 32,768 tokens

For more details, please refer to our blog. You can also check Qwen2.5 GitHub, and Documentation.

Requirements

The code of Qwen2.5 has been in the latest Hugging face transformers and 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-Preview"

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

prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"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=512
)
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]

Citation

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

@misc{qwq-32b-preview,
    title = {QwQ: Reflect Deeply on the Boundaries of the Unknown},
    url = {https://qwenlm.github.io/blog/qwq-32b-preview/},
    author = {Qwen Team},
    month = {November},
    year = {2024}
}

@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}
}

📝 Limitations & Considerations

  • â€ĸ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • â€ĸ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • â€ĸ FNI scores are relative rankings and may change as new models are added.
  • ⚠ License Unknown: Verify licensing terms before commercial use.
  • â€ĸ Source: Unknown
📜

Cite this model

Academic & Research Attribution

BibTeX
@misc{hf_model__qwen__qwq_32b_preview,
  author = {qwen},
  title = {undefined Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/qwen/qwq-32b-preview}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
qwen. (2026). undefined [Model]. Free2AITools. https://huggingface.co/qwen/qwq-32b-preview
🔄 Daily sync (03:00 UTC)

AI Summary: Based on Hugging Face metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseâ„šī¸ Verify with original source

đŸ›Ąī¸ Model Transparency Report

Verified data manifest for traceability and transparency.

100% Data Disclosure Active

🆔 Identity & Source

id
hf-model--qwen--qwq-32b-preview
author
qwen
tags
transformerssafetensorsqwen2text-generationchatconversationalenarxiv:2407.10671base_model:qwen/qwen2.5-32b-instructbase_model:finetune:qwen/qwen2.5-32b-instructlicense:apache-2.0text-generation-inferenceendpoints_compatibledeploy:azureregion:us

âš™ī¸ Technical Specs

architecture
Qwen2ForCausalLM
params billions
32.76
context length
4,096
vram gb
27.1
vram is estimated
true
vram formula
VRAM ≈ (params * 0.75) + 2GB (KV) + 0.5GB (OS)

📊 Engagement & Metrics

likes
1,741
downloads
81,495

Free2AITools Constitutional Data Pipeline: Curated disclosure mode active. (V15.x Standard)