ai/qwen3-coder repository overview

See our collection⁠ for all versions of Qwen3 including GGUF, 4-bit & 16-bit formats.

Learn to run Qwen3-Coder correctly - Read our Guide⁠.

See Unsloth Dynamic 2.0 GGUFs⁠ for our quantization benchmarks.

⁠ ⁠ ⁠

⁠✨ Read our Qwen3-Coder Guide here⁠!

• Fine-tune Qwen3 (14B) for free using our Google Colab notebook⁠!
• Read our Blog about Qwen3 support: unsloth.ai/blog/qwen3⁠
• View the rest of our notebooks in our docs here⁠. | Unsloth supports | Free Notebooks | Performance | Memory use | |-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------| | Qwen3 (14B) | ▶️ Start on Colab⁠ | 3x faster | 70% less | | GRPO with Qwen3 (8B) | ▶️ Start on Colab⁠ | 3x faster | 80% less | | Llama-3.2 (3B) | ▶️ Start on Colab⁠ | 2.4x faster | 58% less | | Llama-3.2 (11B vision) | ▶️ Start on Colab⁠ | 2x faster | 60% less | | Qwen2.5 (7B) | ▶️ Start on Colab⁠ | 2x faster | 60% less |

⁠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.

⁠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}, 
}