--- library_name: transformers license: apache-2.0 pipeline_tag: text-generation base_model: Qwen/Qwen3-Coder-30B-A3B-Instruct tags: - quantized - w8a8 - llm-compressor --- ``` ██╗ ██╗ █████╗ █████╗ █████╗ ██║ ██║██╔══██╗ ██╔══██╗██╔══██╗ ██║ █╗ ██║╚█████╔╝ ███████║╚█████╔╝ ██║███╗██║██╔══██╗ ██╔══██║██╔══██╗ ╚███╔███╔╝╚█████╔╝ ██║ ██║╚█████╔╝ ╚══╝╚══╝ ╚════╝ ╚═╝ ╚═╝ ╚════╝ 🗜️ COMPRESSED & OPTIMIZED 🚀 ``` # Qwen3-Coder-30B-A3B-Instruct - W8A8 Quantized W8A8 (8-bit weights and activations) quantized version of Qwen/Qwen3-Coder-30B-A3B-Instruct using **LLM-Compressor**. - 🗜️ **Memory**: ~50% reduction vs FP16 - 🚀 **Speed**: Faster inference on supported hardware - 🔗 **Original model**: https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct - 🏗️ **Recommended architectures**: Ampere and older

Click to view compression config

```python cat Qwen3-Coder-30B-A3B-w8a8-Instruct.py from datasets import load_dataset from transformers import AutoModelForCausalLM, AutoTokenizer from llmcompressor.modifiers.quantization import GPTQModifier from llmcompressor.modifiers.smoothquant import SmoothQuantModifier from llmcompressor.transformers import oneshot from llmcompressor.utils import dispatch_for_generation from llmcompressor.modifiers.quantization import QuantizationModifier # Select model and load it. model_id = "Qwen/Qwen3-Coder-30B-A3B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype="auto", device_map="auto", low_cpu_mem_usage=True, offload_folder="./offload_tmp", # Add offload directory max_memory={0: "22GB", 1: "22GB", "cpu": "64GB"}, ) tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) # Select calibration dataset. DATASET_ID = "mit-han-lab/pile-val-backup" DATASET_SPLIT = "validation" # Select number of samples. 512 samples is a good place to start. # Increasing the number of samples can improve accuracy. NUM_CALIBRATION_SAMPLES = 256 MAX_SEQUENCE_LENGTH = 512 # Load dataset and preprocess. ds = load_dataset(DATASET_ID, split=f"{DATASET_SPLIT}[:{NUM_CALIBRATION_SAMPLES}]") ds = ds.shuffle(seed=42) def preprocess(example): return { "text": tokenizer.apply_chat_template( [{"role": "user", "content": example["text"]}], tokenize=False, ) } ds = ds.map(preprocess) # Tokenize inputs. def tokenize(sample): return tokenizer( sample["text"], padding=False, max_length=MAX_SEQUENCE_LENGTH, truncation=True, add_special_tokens=False, ) ds = ds.map(tokenize, remove_columns=ds.column_names) # Configure the quantization algorithm to run. # * quantize the activations to int8 (dynamic per token) recipe = QuantizationModifier(targets="Linear", scheme="W8A8", ignore=["lm_head", "re:.*mlp.gate$"]) # Apply algorithms. oneshot( model=model, dataset=ds, recipe=recipe, max_seq_length=MAX_SEQUENCE_LENGTH, num_calibration_samples=NUM_CALIBRATION_SAMPLES, output_dir="./Qwen/Qwen3-Coder-30B-A3B-Instruct-W8A8", # Add this line ) # Save to disk compressed. SAVE_DIR = model_id.rstrip("/").split("/")[-1] + "-W8A8" model.save_pretrained(SAVE_DIR, save_compressed=True) tokenizer.save_pretrained(SAVE_DIR) ```

--- ## 📄 Original Model README # Qwen3-Coder-30B-A3B-Instruct

## Highlights **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: - **Significant Performance** among open models on **Agentic Coding**, **Agentic Browser-Use**, and other foundational coding tasks. - **Long-context Capabilities** with native support for **256K** tokens, extendable up to **1M** tokens using Yarn, optimized for repository-scale understanding. - **Agentic Coding** supporting for most platform such as **Qwen Code**, **CLINE**, featuring a specially designed function call format. ![image/jpeg](https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-Coder/qwen3-coder-30a3-main.jpg) ## Model Overview **Qwen3-Coder-30B-A3B-Instruct** has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 30.5B in total and 3.3B activated - Number of Layers: 48 - Number of Attention Heads (GQA): 32 for Q and 4 for KV - Number of Experts: 128 - Number of Activated Experts: 8 - Context Length: **262,144 natively**. **NOTE: This model supports only non-thinking mode and does not generate ```` blocks in its output. Meanwhile, specifying `enable_thinking=False` is no longer required.** 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/). ## Quickstart We advise you to use the latest version of `transformers`. With `transformers<4.51.0`, you will encounter the following error: ``` KeyError: 'qwen3_moe' ``` The following contains a code snippet illustrating how to use the model generate content based on given inputs. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-Coder-30B-A3B-Instruct" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input prompt = "Write a quick sort algorithm." messages = [ {"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) # conduct text completion generated_ids = model.generate( **model_inputs, max_new_tokens=65536 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() content = tokenizer.decode(output_ids, skip_special_tokens=True) print("content:", content) ``` **Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as `32,768`.** For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3. ## Agentic Coding Qwen3-Coder excels in tool calling capabilities. You can simply define or use any tools as following example. ```python # Your tool implementation def square_the_number(num: float) -> dict: return num ** 2 # Define Tools tools=[ { "type":"function", "function":{ "name": "square_the_number", "description": "output the square of the number.", "parameters": { "type": "object", "required": ["input_num"], "properties": { 'input_num': { 'type': 'number', 'description': 'input_num is a number that will be squared' } }, } } } ] import OpenAI # Define LLM client = OpenAI( # Use a custom endpoint compatible with OpenAI API base_url='http://localhost:8000/v1', # api_base api_key="EMPTY" ) messages = [{'role': 'user', 'content': 'square the number 1024'}] completion = client.chat.completions.create( messages=messages, model="Qwen3-Coder-30B-A3B-Instruct", max_tokens=65536, tools=tools, ) print(completion.choice[0]) ``` ## Best Practices To achieve optimal performance, we recommend the following settings: 1. **Sampling Parameters**: - We suggest using `temperature=0.7`, `top_p=0.8`, `top_k=20`, `repetition_penalty=1.05`. 2. **Adequate Output Length**: We recommend using an output length of 65,536 tokens for most queries, which is adequate for instruct models. ### 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}, } ```