Unlocking Qwen3-Coder-30B: Features, Fast Start, and Agentic Coding Guide

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The Qwen3‑Coder‑30B‑A3B‑Instruct model, officially named Qwen3‑Coder‑Flash, brings a 3‑billion‑parameter active set that balances effectiveness and efficiency. Key improvements include strong performance on Agentic Coding, Agentic Browser‑Use, and other fundamental coding tasks, native support for 256K‑token context (extendable to 1M tokens with Yarn), and compatibility with major tool platforms such as Qwen Code and CLINE via a custom function‑call format.

Model Overview

Type: Causal Language Model

Training stages: Pre‑training & Post‑training

Total parameters: 30.5 B (3.3 B active)

Layers: 48

Attention heads (GQA): Q=32, KV=4

Number of experts: 128 (8 active)

Context length (native): 262,144 tokens

Note: The model does not support the <think></think> block, so the enable_thinking=False flag is unnecessary.

Quick Start

It is recommended to use the latest transformers library. With transformers<4.51.0 you may encounter errors.

The following code demonstrates how to generate text with the model given a prompt.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qwen/Qwen3-Coder-30B-A3B-Instruct"

# Load tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(
     model_name,
     torch_dtype="auto",
     device_map="auto",
 )

# Prepare 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)

# Generate 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)

Tip: If you encounter out‑of‑memory (OOM) issues, reduce the context length, e.g., to 32,768 tokens.

Local runtimes such as Ollama, LMStudio, MLX‑LM, llama.cpp, and KTransformers already support Qwen3.

Agentic Coding

Qwen3‑Coder excels at tool‑calling scenarios. Below is a minimal example that defines a custom tool and invokes the model via an OpenAI‑compatible endpoint.

# 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‑compatible endpoint)
 client = OpenAI(
     base_url='http://localhost:8000/v1',
     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

Sampling parameters: temperature=0.7, top_p=0.8, top_k=20, repetition_penalty=1.05.

Sufficient output length: For most queries, an output length of 65,536 tokens is adequate for instruct models.

Citation

@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},
}