23rd April 2024
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
- Thoughts on OpenAI acquiring Astral and uv/ruff/ty - 19th March 2026
- GPT-5.4 mini and GPT-5.4 nano, which can describe 76,000 photos for $52 - 17th March 2026
- My fireside chat about agentic engineering at the Pragmatic Summit - 14th March 2026