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
- LLM 0.22, the annotated release notes - 17th February 2025
- Run LLMs on macOS using llm-mlx and Apple's MLX framework - 15th February 2025
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