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--- |
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library_name: transformers |
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license: apache-2.0 |
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license_link: https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct/blob/main/LICENSE |
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pipeline_tag: text-generation |
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tags: |
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- AWQ |
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- 量化修复 |
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- vLLM |
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base_model: |
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- Qwen/Qwen3-Coder-30B-A3B-Instruct |
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base_model_relation: quantized |
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--- |
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# Qwen3-Coder-30B-A3B-Instruct-AWQ |
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基础型 [Qwen3-Coder-30B-A3B-Instruct](https://www.modelscope.cn/models/Qwen3-Coder-30B-A3B-Instruct) |
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### 【❗❗提醒❗❗】 |
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该模型4比特量化损失较大，需谨慎使用 |
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### 【Vllm 启动命令】 |
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<i>注: 4卡启动该模型一定要跟`--enable-expert-parallel` ，否则其专家张量TP整除除不尽；2卡则不需要。 </i> |
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``` |
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CONTEXT_LENGTH=32768 |
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vllm serve \ |
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tclf90/Qwen3-Coder-30B-A3B-Instruct-AWQ \ |
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--served-model-name Qwen3-Coder-30B-A3B-Instruct-AWQ \ |
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--enable-expert-parallel \ |
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--swap-space 16 \ |
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--max-num-seqs 512 \ |
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--max-model-len $CONTEXT_LENGTH \ |
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--max-seq-len-to-capture $CONTEXT_LENGTH \ |
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--gpu-memory-utilization 0.9 \ |
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--tensor-parallel-size 4 \ |
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--trust-remote-code \ |
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--disable-log-requests \ |
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--host 0.0.0.0 \ |
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--port 8000 |
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``` |
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### 【依赖】 |
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``` |
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vllm==0.10.0 |
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``` |
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### 【模型更新日期】 |
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``` |
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2025-08-01 |
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1. 首次commit |
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``` |
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### 【模型列表】 |
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| 文件大小 | 最近更新时间 | |
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|--------|--------------| |
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| `16GB` | `2025-08-01` | |
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### 【模型下载】 |
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```python |
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from modelscope import snapshot_download |
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snapshot_download('tclf90/Qwen3-Coder-30B-A3B-Instruct-AWQ', cache_dir="本地路径") |
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``` |
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### 【介绍】 |
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# Qwen3-Coder-30B-A3B-Instruct |
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<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;"> |
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<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;"/> |
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</a> |
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## Highlights |
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**Qwen3-Coder** is available in multiple sizes. Today, we're excited to introduce **Qwen3-Coder-30B-A3B-Instruct**. This streamlined model maintains impressive performance and efficiency, featuring the following key enhancements: |
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- **Significant Performance** among open models on **Agentic Coding**, **Agentic Browser-Use**, and other foundational coding tasks. |
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- **Long-context Capabilities** with native support for **256K** tokens, extendable up to **1M** tokens using Yarn, optimized for repository-scale understanding. |
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- **Agentic Coding** supporting for most platform such as **Qwen Code**, **CLINE**, featuring a specially designed function call format. |
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## Model Overview |
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**Qwen3-Coder-30B-A3B-Instruct** has the following features: |
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- Type: Causal Language Models |
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- Training Stage: Pretraining & Post-training |
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- Number of Parameters: 30.5B in total and 3.3B activated |
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- Number of Layers: 48 |
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- Number of Attention Heads (GQA): 32 for Q and 4 for KV |
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- Number of Experts: 128 |
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- Number of Activated Experts: 8 |
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- Context Length: **262,144 natively**. |
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**NOTE: This model supports only non-thinking mode and does not generate ``<think></think>`` blocks in its output. Meanwhile, specifying `enable_thinking=False` is no longer required.** |
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For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3-coder/), [GitHub](https://github.com/QwenLM/Qwen3-Coder), and [Documentation](https://qwen.readthedocs.io/en/latest/). |
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## Quickstart |
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We advise you to use the latest version of `transformers`. |
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With `transformers<4.51.0`, you will encounter the following error: |
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``` |
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KeyError: 'qwen3_moe' |
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``` |
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The following contains a code snippet illustrating how to use the model generate content based on given inputs. |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "Qwen/Qwen3-Coder-30B-A3B-Instruct" |
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# load the tokenizer and the model |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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# prepare the model input |
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prompt = "Write a quick sort algorithm." |
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messages = [ |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True, |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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# conduct text completion |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=65536 |
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) |
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output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() |
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content = tokenizer.decode(output_ids, skip_special_tokens=True) |
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print("content:", content) |
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``` |
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**Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as `32,768`.** |
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For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3. |
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## Agentic Coding |
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Qwen3-Coder excels in tool calling capabilities. |
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You can simply define or use any tools as following example. |
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```python |
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# Your tool implementation |
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def square_the_number(num: float) -> dict: |
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return num ** 2 |
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# Define Tools |
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tools=[ |
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{ |
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"type":"function", |
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"function":{ |
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"name": "square_the_number", |
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"description": "output the square of the number.", |
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"parameters": { |
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"type": "object", |
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"required": ["input_num"], |
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"properties": { |
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'input_num': { |
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'type': 'number', |
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'description': 'input_num is a number that will be squared' |
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} |
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}, |
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} |
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} |
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} |
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] |
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import OpenAI |
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# Define LLM |
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client = OpenAI( |
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# Use a custom endpoint compatible with OpenAI API |
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base_url='http://localhost:8000/v1', # api_base |
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api_key="EMPTY" |
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) |
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messages = [{'role': 'user', 'content': 'square the number 1024'}] |
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completion = client.chat.completions.create( |
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messages=messages, |
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model="Qwen3-Coder-30B-A3B-Instruct", |
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max_tokens=65536, |
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tools=tools, |
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) |
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print(completion.choice[0]) |
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``` |
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## Best Practices |
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To achieve optimal performance, we recommend the following settings: |
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1. **Sampling Parameters**: |
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- We suggest using `temperature=0.7`, `top_p=0.8`, `top_k=20`, `repetition_penalty=1.05`. |
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2. **Adequate Output Length**: We recommend using an output length of 65,536 tokens for most queries, which is adequate for instruct models. |
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### Citation |
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If you find our work helpful, feel free to give us a cite. |
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``` |
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@misc{qwen3technicalreport, |
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title={Qwen3 Technical Report}, |
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author={Qwen Team}, |
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year={2025}, |
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eprint={2505.09388}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2505.09388}, |
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} |
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``` |
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