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
- Weeknotes: Llama 3, AI for Data Journalism, llm-evals and datasette-secrets - 23rd April 2024
- Options for accessing Llama 3 from the terminal using LLM - 22nd April 2024
- AI for Data Journalism: demonstrating what we can do with this stuff right now - 17th April 2024
- Three major LLM releases in 24 hours (plus weeknotes) - 10th April 2024
- Building files-to-prompt entirely using Claude 3 Opus - 8th April 2024
- Running OCR against PDFs and images directly in your browser - 30th March 2024