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’s accidental cyberattack against Hugging Face is science fiction that happened - 22nd July 2026
- A Fireside Chat with Cat and Thariq from the Claude Code team - 21st July 2026
- Kimi K3, and what we can still learn from the pelican benchmark - 16th July 2026