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Model: tclf90/Qwen3-Coder-30B-A3B-Instruct-AWQ Source: Original Platform
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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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