Ollama Model Library: Selection and Benchmarks

Ollama supports hundreds of open-source models. Let us compare the most popular models to find the right fit for your application.

1. Comparing Popular Local Models

A. Llama 3 (Meta)

• Strengths: High reasoning capability, great English chat output, robust follow-up instructions.
• Weaknesses: Moderate Chinese language processing.
• Sizes: 8B, 70B parameters.

B. Qwen 2.5 (Alibaba)

• Strengths: Best-in-class multi-language translation, excellent Chinese dialogue support, and high math capabilities.
• Weaknesses: Slightly verbose response style.
• Sizes: 0.5B, 1.5B, 3B, 7B, 14B, 72B parameters.

C. Mistral (Mistral AI)

• Strengths: Very fast execution speeds, high 7B parameter reasoning.
• Weaknesses: Limited multilingual capabilities.
• Sizes: 7B parameters.

2. Specialty Coding Models

If your application focuses on auto-completing code blocks, generating unit tests, or explaining debug logs, use code-specific models:

• DeepSeek-Coder-V2: Excellent multi-language code generation, supporting over 300 coding languages.
• Codegemma (Google): Optimized for code completion and instruction following.
• CodeLlama: Classic coding helper based on Llama architecture.

3. Best Selection Choices

• For Low-Resource Machines (e.g. 8GB RAM): Use qwen2.5:1.5b or qwen2.5:3b.
• For Balanced General Dev Work: Use qwen2.5:7b or llama3.
• For Code Generation Actions: Use deepseek-coder-v2:16b (if VRAM permits).