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
- Trying out llama.cpp's new vision support - 10th May 2025
- Saying "hi" to Microsoft's Phi-4-reasoning - 6th May 2025
- Feed a video to a vision LLM as a sequence of JPEG frames on the CLI (also LLM 0.25) - 5th May 2025