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
- Storing times for human events - 27th November 2024
- Ask questions of SQLite databases and CSV/JSON files in your terminal - 25th November 2024
- Weeknotes: asynchronous LLMs, synchronous embeddings, and I kind of started a podcast - 22nd November 2024