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--- |
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library_name: transformers |
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license: apache-2.0 |
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pipeline_tag: text-generation |
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base_model: Qwen/Qwen3-Coder-30B-A3B-Instruct |
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tags: |
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- quantized |
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- w8a8 |
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- llm-compressor |
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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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╚███╔███╔╝╚█████╔╝ ██║ ██║╚█████╔╝ |
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╚══╝╚══╝ ╚════╝ ╚═╝ ╚═╝ ╚════╝ |
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🗜️ COMPRESSED & OPTIMIZED 🚀 |
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``` |
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# Qwen3-Coder-30B-A3B-Instruct - W8A8 Quantized |
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W8A8 (8-bit weights and activations) quantized version of Qwen/Qwen3-Coder-30B-A3B-Instruct using **LLM-Compressor**. |
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- 🗜️ **Memory**: ~50% reduction vs FP16 |
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- 🚀 **Speed**: Faster inference on supported hardware |
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- 🔗 **Original model**: https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct |
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- 🏗️ **Recommended architectures**: Ampere and older |
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<details> |
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<summary>Click to view compression config</summary> |
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```python |
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cat Qwen3-Coder-30B-A3B-w8a8-Instruct.py |
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from datasets import load_dataset |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from llmcompressor.modifiers.quantization import GPTQModifier |
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from llmcompressor.modifiers.smoothquant import SmoothQuantModifier |
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from llmcompressor.transformers import oneshot |
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from llmcompressor.utils import dispatch_for_generation |
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from llmcompressor.modifiers.quantization import QuantizationModifier |
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# Select model and load it. |
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model_id = "Qwen/Qwen3-Coder-30B-A3B-Instruct" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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torch_dtype="auto", |
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device_map="auto", |
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low_cpu_mem_usage=True, |
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offload_folder="./offload_tmp", # Add offload directory |
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max_memory={0: "22GB", 1: "22GB", "cpu": "64GB"}, |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) |
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# Select calibration dataset. |
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DATASET_ID = "mit-han-lab/pile-val-backup" |
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DATASET_SPLIT = "validation" |
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# Select number of samples. 512 samples is a good place to start. |
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# Increasing the number of samples can improve accuracy. |
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NUM_CALIBRATION_SAMPLES = 256 |
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MAX_SEQUENCE_LENGTH = 512 |
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# Load dataset and preprocess. |
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ds = load_dataset(DATASET_ID, split=f"{DATASET_SPLIT}[:{NUM_CALIBRATION_SAMPLES}]") |
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ds = ds.shuffle(seed=42) |
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def preprocess(example): |
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return { |
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"text": tokenizer.apply_chat_template( |
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[{"role": "user", "content": example["text"]}], |
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tokenize=False, |
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) |
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} |
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ds = ds.map(preprocess) |
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# Tokenize inputs. |
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def tokenize(sample): |
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return tokenizer( |
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sample["text"], |
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padding=False, |
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max_length=MAX_SEQUENCE_LENGTH, |
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truncation=True, |
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add_special_tokens=False, |
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) |
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ds = ds.map(tokenize, remove_columns=ds.column_names) |
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# Configure the quantization algorithm to run. |
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# * quantize the activations to int8 (dynamic per token) |
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recipe = QuantizationModifier(targets="Linear", scheme="W8A8", ignore=["lm_head", "re:.*mlp.gate$"]) |
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# Apply algorithms. |
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oneshot( |
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model=model, |
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dataset=ds, |
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recipe=recipe, |
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max_seq_length=MAX_SEQUENCE_LENGTH, |
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num_calibration_samples=NUM_CALIBRATION_SAMPLES, |
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output_dir="./Qwen/Qwen3-Coder-30B-A3B-Instruct-W8A8", # Add this line |
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) |
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# Save to disk compressed. |
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SAVE_DIR = model_id.rstrip("/").split("/")[-1] + "-W8A8" |
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model.save_pretrained(SAVE_DIR, save_compressed=True) |
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tokenizer.save_pretrained(SAVE_DIR) |
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``` |
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</details> |
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|
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--- |
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## 📄 Original Model README |
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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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 |
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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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|
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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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|
## Best Practices |
|
|
|
|
|
To achieve optimal performance, we recommend the following settings: |
|
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|
|
1. **Sampling Parameters**: |
|
|
- 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 |
|
|
|
|
|
If you find our work helpful, feel free to give us a cite. |
|
|
|
|
|
``` |
|
|
@misc{qwen3technicalreport, |
|
|
title={Qwen3 Technical Report}, |
|
|
author={Qwen Team}, |
|
|
year={2025}, |
|
|
eprint={2505.09388}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.CL}, |
|
|
url={https://arxiv.org/abs/2505.09388}, |
|
|
} |
|
|
``` |
