Activefastllm Open Source

Phi-3 Mini

by Microsoft· Released April 2024· Cutoff October 2023

Phi-3 Mini is a small language model (3.8B parameters) from Microsoft that achieves strong performance on benchmarks comparable to larger models like Mixtral 8x7B and GPT-3.5. It is designed for efficient on-device and edge deployment, with a focus on high-quality training data and instruction tuning.

Official Site API Docs 🤗 Hugging Face 📄 Research Paper

Input cost

Free (open source)

Output cost

Free (open source)

Context window

128K tokens

Max output

—

Modalities

text

Parameters

3.8B

License

MIT

Capabilities

Text GenerationCode GenerationReasoningInstruction FollowingMultilingual Support

Best For

On-device and edge applications requiring a capable yet lightweight language model.

Strengths

Strong performance for its size, rivaling larger models
Efficient inference suitable for mobile and edge devices
Large 128K context window
Open source with permissive MIT license

Limitations

Smaller parameter count limits complex reasoning compared to frontier models
Not multimodal (text-only)
May struggle with highly specialized or niche tasks
Limited community ecosystem compared to larger open models

Use Cases

On-device chatbots and virtual assistants

Code completion and generation in IDEs

Text summarization and classification on edge devices

Educational tools and tutoring

Privacy-sensitive applications requiring local processing

IoT and embedded systems

Rapid prototyping and experimentation

Improvements Over Previous Model

First model in the Phi-3 family, so no direct predecessor; improvements over Phi-2 include larger context window (128K vs 2K), better instruction following, and higher benchmark scores.

Back to all models

Activefastllm Open Source

Phi-3 Mini

by Microsoft· Released April 2024· Cutoff October 2023

Official Site API Docs 🤗 Hugging Face 📄 Research Paper

Input cost

Free (open source)

Output cost

Free (open source)

Context window

128K tokens

Max output

—

Modalities

text

Parameters

3.8B

License

MIT

Capabilities

Text GenerationCode GenerationReasoningInstruction FollowingMultilingual Support

Best For

On-device and edge applications requiring a capable yet lightweight language model.

Strengths

Strong performance for its size, rivaling larger models
Efficient inference suitable for mobile and edge devices
Large 128K context window
Open source with permissive MIT license

Limitations

Smaller parameter count limits complex reasoning compared to frontier models
Not multimodal (text-only)
May struggle with highly specialized or niche tasks
Limited community ecosystem compared to larger open models

Use Cases

On-device chatbots and virtual assistants

Code completion and generation in IDEs

Text summarization and classification on edge devices

Educational tools and tutoring

Privacy-sensitive applications requiring local processing

IoT and embedded systems

Rapid prototyping and experimentation

Improvements Over Previous Model

First model in the Phi-3 family, so no direct predecessor; improvements over Phi-2 include larger context window (128K vs 2K), better instruction following, and higher benchmark scores.

Back to all models