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
- Notes on the xAI/Anthropic data center deal - 7th May 2026
- Live blog: Code w/ Claude 2026 - 6th May 2026
- Vibe coding and agentic engineering are getting closer than I'd like - 6th May 2026