We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi-3-mini achieves 69% on MMLU and 8.38 on MT-bench), despite being small enough to be deployed on a phone.
Recent articles
- Reverse engineering some updates to Claude - 31st July 2025
- Trying out Qwen3 Coder Flash using LM Studio and Open WebUI and LLM - 31st July 2025
- My 2.5 year old laptop can write Space Invaders in JavaScript now, using GLM-4.5 Air and MLX - 29th July 2025