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Qwen2.5 32B

by Alibaba· Released September 2024· Cutoff June 2024

Qwen2.5 32B is a large language model from Alibaba's Qwen series, offering a strong balance of performance and efficiency with 32 billion parameters. It supports a 128K token context window and excels in coding, mathematics, and multilingual tasks. This model is part of the Qwen2.5 family, which includes sizes from 0.5B to 72B, and is available as an open-source model on Hugging Face.

Official Site API Docs 🤗 Hugging Face 📄 Research Paper

Input cost

Free (open source)

Output cost

Free (open source)

Context window

128K tokens

Max output

8192 tokens

Modalities

text

Parameters

32B

License

Apache-2.0

Capabilities

Function CallingCode GenerationStreamingJSON ModeMultilingual SupportLong Context UnderstandingMath & Reasoning

Best For

Complex reasoning tasks, coding, and multilingual applications requiring a large context window.

Strengths

Strong performance on coding and math benchmarks
128K token context window for long documents
Multilingual support with high accuracy
Open-source and freely available
Efficient inference for a 32B model

Limitations

Smaller than 72B variant, may lag on some benchmarks
No native vision or multimodal support
Requires significant GPU memory for local deployment
Knowledge cutoff may not include latest events

Use Cases

Code generation and debugging

Mathematical problem solving

Long document summarization and analysis

Multilingual translation and content creation

Chatbots and virtual assistants

Data extraction and structured output generation

Educational tutoring and explanation

Improvements Over Previous Model

Increased context window from 32K to 128K tokens compared to Qwen2
Improved coding benchmarks: HumanEval score increased from 85% to 92%
Enhanced multilingual capabilities with better performance on non-English languages
Reduced inference latency by approximately 30% over Qwen2-32B
Added support for function calling and JSON mode
Better handling of long-context tasks with improved attention mechanisms

Back to all models

Improvements Over Previous Model

Increased context window from 32K to 128K tokens compared to Qwen2

Improved coding benchmarks: HumanEval score increased from 85% to 92%

Enhanced multilingual capabilities with better performance on non-English languages

Reduced inference latency by approximately 30% over Qwen2-32B

Added support for function calling and JSON mode

Better handling of long-context tasks with improved attention mechanisms