Activefastllm Open Source

Qwen2.5 7B

by Alibaba· Released September 2024· Cutoff June 2024

Qwen2.5 7B is a large language model from Alibaba's Qwen series, offering strong performance in coding, mathematics, and general knowledge tasks. It features an extended context window of up to 128K tokens and supports over 29 languages, making it versatile for multilingual applications. As part of the Qwen2.5 family, it balances efficiency and capability for a wide range of use cases.

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

License

Apache-2.0

Capabilities

Function CallingCode GenerationMultilingual SupportLong Context UnderstandingJSON ModeStreaming

Best For

Efficient and capable open-source LLM for coding, math, and multilingual tasks with long context support.

Strengths

Strong performance in coding and mathematics benchmarks
Supports 128K token context window
Multilingual capabilities covering 29+ languages
Open-source with permissive Apache 2.0 license
Efficient inference with relatively small 7B parameter size

Limitations

Smaller parameter count may limit complex reasoning compared to larger models
Not a multimodal model (text-only input)
May require fine-tuning for specialized domains
Knowledge cutoff may not include very recent events

Use Cases

Code generation and assistance

Mathematical problem solving

Multilingual translation and content creation

Document summarization with long context

Chatbots and virtual assistants

Educational tutoring in STEM subjects

Data extraction and structured output generation

Improvements Over Previous Model

Context window expanded from 32K to 128K tokens compared to Qwen2-7B
Improved coding benchmark scores (HumanEval 85% vs 79% in Qwen2-7B)
Enhanced multilingual support with 29+ languages vs 27 in Qwen2
Better mathematical reasoning (MATH 75% vs 68% in Qwen2-7B)
Reduced hallucination rates in factual generation tasks

Back to all models

Improvements Over Previous Model

Context window expanded from 32K to 128K tokens compared to Qwen2-7B

Improved coding benchmark scores (HumanEval 85% vs 79% in Qwen2-7B)

Enhanced multilingual support with 29+ languages vs 27 in Qwen2

Better mathematical reasoning (MATH 75% vs 68% in Qwen2-7B)

Reduced hallucination rates in factual generation tasks