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
- OpenAI agents attacked RubyGems back in May - 12th September 2026
- Some thoughts on the Navier–Stokes Millennium Prize Problem - 8th September 2026
- The Pelican comparison grid for Astra is pretty interesting - 4th September 2026