Qwen3-32B-GPTQ-Int4

通义千问3-32B-GPTQ-Int4

基础型 Qwen/Qwen3-32B

【模型更新日期】

【依赖】

【💡新版 VLLM 注意事项💡】

• 需使用V0推理模式

启动vllm之前，先设置环境变量

【💡Qwen3-32B 注意事项💡】

• 无限循环。

该模型母体本身在推理、尤其是思考的时候容易出现无限循环，4bit量化后，情况可能会加重一点。

使用的时候，需要关注有问题的内容主题。

尽量使用非思考模式、尽量回避bad cases。

【模型列表】

文件大小 最近更新时间

• ------- --------------

19GB 2025-05-03

【模型下载】

【介绍】

Qwen3-32B

Qwen3 Highlights

Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:

• *Uniquely support of seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose dialogue) **within single model**, ensuring optimal performance across various scenarios.
• *Significantly enhancement in its reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
• *Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
• *Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
• *Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**.

Model Overview

Qwen3-32B** has the following features:

Type: Causal Language Models

Training Stage: Pretraining & Post-training

Number of Parameters: 32.8B

Number of Paramaters (Non-Embedding): 31.2B

Number of Layers: 64

Number of Attention Heads (GQA): 64 for Q and 8 for KV

Context Length: 32,768 natively and 131,072 tokens with YaRN.

For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub, and Documentation.

Best Practices

To achieve optimal performance, we recommend the following settings:

• **Sampling Parameters**:
• For thinking mode ( enable thinking=True ), use Temperature=0.6 , TopP=0.95 , TopK=20 , and MinP=0 . **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions.
• For non-thinking mode ( enable thinking=False ), we suggest using Temperature=0.7 , TopP=0.8 , TopK=20 , and MinP=0 .
• For supported frameworks, you can adjust the presence penalty parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.

• **Adequate Output Length**: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.

• **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" ."

• **No Thinking Content in History**: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.

Processing Long Texts

Qwen3 natively supports context lengths of up to 32,768 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the YaRN method.

YaRN is currently supported by several inference frameworks, e.g., transformers and llama.cpp for local use, vllm and sglang for deployment. In general, there are two approaches to enabling YaRN for supported frameworks:

Modifying the model files:

In the config.json file, add the rope scaling fields:

For llama.cpp , you need to regenerate the GGUF file after the modification.

Passing command line arguments:

For vllm , you can use

For sglang , you can use

For llama-server from llama.cpp , you can use

Citation

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

@misc{qwen3,

title = {Qwen3},

url = {https://qwenlm.github.io/blog/qwen3/},

author = {Qwen Team},

month = {April},

year = {2025}

}