Activefastllm Open Source

Qwen2.5-Coder 3B

by Alibaba· Released November 2024· Cutoff June 2024

Qwen2.5-Coder 3B is a compact code-specialized language model from Alibaba's Qwen series, designed for efficient code generation and understanding. It balances strong coding performance with a small footprint, making it suitable for resource-constrained environments. As part of the Qwen2.5-Coder family, it offers competitive results on coding benchmarks while being significantly smaller than larger variants.

Official Site 🤗 Hugging Face

Input cost

Free (open source)

Output cost

Free (open source)

Context window

32,768 tokens

Max output

8,192 tokens

Modalities

text

Parameters

License

Apache-2.0

Capabilities

Code GenerationCode CompletionCode ExplanationBug FixingMulti-language ProgrammingInstruction Following

Best For

Lightweight code generation and assistance tasks where low latency and small model size are critical.

Strengths

Strong coding performance for its size, competitive with larger models on HumanEval and MBPP
Very fast inference due to small 3B parameter count
Open source with permissive license, enabling local deployment and customization
Supports multiple programming languages including Python, Java, JavaScript, C++, and more

Limitations

Limited context window (32K tokens) compared to larger models
May struggle with complex, multi-step reasoning tasks outside of code
Smaller capacity means less knowledge breadth than larger Qwen2.5 models
Not suitable for tasks requiring extensive world knowledge or nuanced language understanding

Use Cases

Real-time code autocompletion in IDEs

Automated code review and bug detection

Generating boilerplate code and scripts

Educational tool for learning programming concepts

Embedded in low-resource environments like mobile or edge devices

Code translation between programming languages

Assisting in rapid prototyping and development

Improvements Over Previous Model

Improved coding benchmarks: HumanEval score increased from ~70% (Qwen2-Coder 1.5B) to ~80%
Context window expanded from 8K to 32K tokens
Better support for more programming languages and longer code sequences
Enhanced instruction following and code explanation capabilities

Back to all models

Strengths

Strong coding performance for its size, competitive with larger models on HumanEval and MBPP

Very fast inference due to small 3B parameter count

Open source with permissive license, enabling local deployment and customization

Supports multiple programming languages including Python, Java, JavaScript, C++, and more