Qwen2.5 Coder 32B
Qwen3-Coder is a family of open-weight language models designed for coding agents and local development, with sizes including Qwen3-Coder-Next, 30B-A3B, and 480B-A35B. It solves the problem of efficient, high-performance code generation and agentic tasks by using a hybrid architecture and agentic training. It is for developers who need a powerful code model for tasks like repository-scale understanding, code completion, and building software agents.
✨ Key features
- Efficiency-performance tradeoff comparable to Claude Sonnet on agentic coding.
- Supports agentic coding platforms like Qwen Code, CLINE, and Claude Code.
- Native 256K token context, extendable to 1M with Yarn.
- Supports 358 coding languages.
- Function calling with new tool parser in SGLang and vLLM.
- Retains strengths in math and general capabilities.
🎯 Use cases
- Chat-based code generation and completion.
- Fill-in-the-middle code insertion.
- Building coding agents for autonomous tasks.
- Repository-scale code understanding and modification.
- Local development assistance with low inference cost.
📦 Installation
🧰 Requirements: Requires Python with transformers library, and a compatible GPU for inference. No API keys mentioned.
To install, use pip to install the required libraries:
pip install transformers torch
Then download the model from Hugging Face or ModelScope using the provided links.
🚀 Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen3-Coder-Next"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
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)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=65536
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
⚠️ Good to know
The model supports only non-thinking mode and does not generate thinking blocks; it requires the new tokenizer and tool parser for function calling.
❓ FAQ
What model sizes are available?
Qwen3-Coder is available in sizes: Qwen3-Coder-480B-A35B-Instruct, Qwen3-Coder-30B-A3B-Instruct, and Qwen3-Coder-Next, with FP8 and GGUF variants.
How do I use the model for fill-in-the-middle?
Structure the prompt with special tokens: '<|fim_prefix|>' + prefix_code + '<|fim_suffix|>' + suffix_code + '<|fim_middle|>', and use the model as shown in the README example.
Does the model support function calling?
Yes, it supports function calling, but it relies on a new tool parser in SGLang and vLLM, and you must use the updated tokenizer.
What is the context length?
It natively supports 256K tokens, extendable up to 1M using Yarn.
📊 Repository
🤖 Overview, features, install steps and FAQ were generated from the project's README on Sep 4, 2026. Always check the original source before running commands.