Phi3-3.8|7|14B
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OSS
Phi3-3.8|7|14B
Added 1 June 2026
Overview
Phi-3 is a family of small language models from Microsoft, available in 3.8B, 7B, and 14B parameter sizes. These open-source models are designed for efficient text generation and can be fine-tuned for specific tasks. They are hosted on Hugging Face and intended to democratize AI through open science.
Best for
Best for
Developers who need compact, open-source language models for resource-constrained environments or rapid prototyping
Use cases
- Build resource-efficient chatbots for edge or mobile deployment
- Perform text generation and completion with modest compute requirements
- Fine-tune a compact model for domain-specific natural language tasks
Notes
Phi-3 is a family of small language models from Microsoft, available in 3.8B, 7B, and 14B parameter sizes. These open-source models are designed for efficient text generation and can be fine-tuned for specific tasks. They are hosted on Hugging Face and intended to democratize AI through open science.
Use cases
- Build resource-efficient chatbots for edge or mobile deployment
- Perform text generation and completion with modest compute requirements
- Fine-tune a compact model for domain-specific natural language tasks
Pros
- Small parameter sizes enable fast inference and lower memory usage
- Open source on Hugging Face with permissive licensing for research and development
- Offers a range of sizes to balance performance and resource constraints
Cons
- Smaller models may exhibit lower accuracy on complex reasoning or nuanced tasks
- Context window limited to 4K tokens in the instruct variant
- Community-maintained; official updates and support may be less consistent than vendor-backed tools
Indexed from awesome-llm and enriched against its public facts.
Pros
- Small parameter sizes enable fast inference and lower memory usage
- Open source on Hugging Face with permissive licensing for research and development
- Offers a range of sizes to balance performance and resource constraints
Cons
- Smaller models may exhibit lower accuracy on complex reasoning or nuanced tasks
- Context window limited to 4K tokens in the instruct variant
- Community-maintained; official updates and support may be less consistent than vendor-backed tools