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
- Conceptual integrity and counting lines of code - 19th August 2026
- Qwen 3.8 27B is excellent, but it defaults to wildly overthinking things - 16th August 2026
- Now we have a timeline of the OpenAI accidental attack against Hugging Face - 7th August 2026