Provider: Microsoft License: MIT (permissive, commercial-friendly) Access: Open weights on Hugging Face Architecture: Decoder-only Transformer (Small Language Model) Size: ~3.8B parameters
🔍 Overview
Phi-3 Mini (4K) Instruct is part of Microsoft’s Phi-3 family of small language models (SLMs), engineered to deliver strong reasoning and instruction-following performance at a fraction of the cost and footprint of large LLMs. The model is optimized for practical deployment scenarios such as on-device inference, serverless APIs, and high-throughput systems.
Key strengths:
- ⚡ High Performance per Parameter: Strong math, logic, and coding behavior relative to size
- 📱 On-Device Friendly: Suitable for edge devices and constrained environments
- 🧠 Instruction-Tuned: Ready for assistant-style prompting out of the box
⚙️ Technical Specs
- Architecture: Decoder-only Transformer
- Parameters: ~3.8B
- Context Length: 4,096 tokens
- Training: High-quality curated data + synthetic reasoning traces
- Tokenizer: BPE-based, optimized for compact models
🚀 Deployment
- Hugging Face Repo: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct
- Frameworks: 🤗 Transformers, vLLM, llama.cpp (converted), ONNX
- Use Cases: Lightweight chatbots, coding helpers, on-device assistants, high-QPS APIs
- Hardware: Runs well on CPU, low-end GPU, or mobile/edge accelerators