Qwen 2.5 7B
By Alibaba · China
Overview
Alibaba's Qwen 2.5 7B, a top-tier 7B for its era with a 128k context, strong multilingual coverage across 29 languages, and Apache 2.0 licensing.
When to pick this model
- Multilingual assistants beyond English/French
- Long-context document Q&A and summarization
- General-purpose chat with permissive commercial licensing
- Math and code tasks on a single consumer GPU
- Drop-in alternative to Llama 3.1 8B
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 5 GB |
| Q5_K_M | 6 GB |
| Q8_0 | 9 GB |
| FP16 (no quantization) | 16 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
Published benchmark scores
| Benchmark | Score |
|---|---|
| MMLU | 74.2 |
| HumanEval | 84.8 |
| MATH | 75.5 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
Strengths
- 128k context window
- Apache 2.0 license with no MAU restrictions
- Strong multilingual performance across 29 languages
- Better math and coding than Llama 3.1 8B at the same size
Limitations
- Surpassed by Qwen 3 8B in 2025
- Trails Qwen 2.5 Coder on dedicated coding tasks
- Reasoning weaker than DeepSeek R1 distills
Architecture & training
Architecture: Dense Transformer · 28 layers · GQA · Qwen 2.5
Training: 18T tokens, strong multilingual coverage (29 languages), enriched code and math data.
A still-useful general-purpose 7B with permissive licensing — but check Qwen 3 first if you're starting fresh.
Quick start
ollama run qwen2.5:7bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.