Qwen3-Coder-30B-A3B-Instruct-W4A16

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        🗜️ COMPRESSED & OPTIMIZED 🚀

Qwen3-Coder-30B-A3B-Instruct - W4A16 Quantized

W4A16 (4-bit weights, 16-bit activations) quantized version of Qwen/Qwen3-Coder-30B-A3B-Instruct using LLM-Compressor.

• 🗜️ Memory: ~75% reduction vs FP16
• 🚀 Speed: Faster inference on supported hardware
• 🔗 Original model: [link]

<details> <summary>Click to view compression config</summary>

from datasets import load_dataset
from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor import oneshot
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model with memory management
model_stub = "Qwen/Qwen3-Coder-30B-A3B-Instruct"
model_name = model_stub.split("/")[-1]

# Use conservative parameters
num_samples = 1024
max_seq_len = 8192

print(f"Loading model: {model_stub}")
model = AutoModelForCausalLM.from_pretrained(
    model_stub,
    torch_dtype="auto",
    device_map="auto",
    max_memory={0: "22GB", 1: "22GB", "cpu": "24GB"},
)

print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(model_stub)

print("Loading calibration dataset...")
def preprocess_fn(example):
    return {"text": tokenizer.apply_chat_template(
        example["messages"], 
        add_generation_prompt=False, 
        tokenize=False
    )}

# Load dataset and preprocess
ds = load_dataset("neuralmagic/LLM_compression_calibration", split=f"train[:{num_samples}]")
ds = ds.map(preprocess_fn)
ds = ds.shuffle(seed=42)

# Tokenize the dataset
def tokenize(sample):
    return tokenizer(
        sample["text"],
        padding=False,
        max_length=max_seq_len,
        truncation=True,
        add_special_tokens=False,
    )

print("Tokenizing dataset...")
ds = ds.map(tokenize, remove_columns=ds.column_names)

# Configure GPTQ with proper Qwen3 MoE ignore patterns
print("Configuring quantization recipe...")
recipe = GPTQModifier(
    targets="Linear",
    scheme="W4A16", 
    ignore=["lm_head", "re:.*mlp.gate$"],  # Qwen3 MoE pattern (no shared experts)
    dampening_frac=0.01,
    # Remove sequential_targets - let llmcompressor handle automatically
)

# Apply quantization
print("Starting quantization process...")
oneshot(
    model=model,
    dataset=ds,
    recipe=recipe,
    max_seq_length=max_seq_len,
    num_calibration_samples=num_samples,
)

# Save quantized model
save_path = model_name + "-gptq-w4a16"
print(f"Saving model to: {save_path}")
model.save_pretrained(save_path, save_compressed=True)
tokenizer.save_pretrained(save_path)

print("Quantization completed successfully!")

</details>

---

📄 Original Model README

Qwen3-Coder-30B-A3B-Instruct

<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;"> <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;"/> </a>

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.

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 <think></think> 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, GitHub, and Documentation.

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.

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.

# 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}, 
}