Model Summary
The Phi-3-Mini-128K-Instruct is a 3.8 billion-parameter, lightweight, state-of-the-art open model trained using the Phi-3 datasets. This dataset includes both synthetic data and filtered publicly available website data, with an emphasis on high-quality and reasoning-dense properties. The model belongs to the Phi-3 family with the Mini version in two variants 4K and 128K which is the context length (in tokens) that it can support.
After initial training, the model underwent a post-training process that involved supervised fine-tuning and direct preference optimization to enhance its ability to follow instructions and adhere to safety measures. When evaluated against benchmarks that test common sense, language understanding, mathematics, coding, long-term context, and logical reasoning, the Phi-3 Mini-128K-Instruct demonstrated robust and state-of-the-art performance among models with fewer than 13 billion parameters. Resources and Technical Documentation:
Quantized Model Files
Phi-3 is available in several formats, catering to different computational needs:
- ggml-model-q4_0.gguf: 4-bit quantization, offering a compact size of 2.1 GB for efficient inference.
- ggml-model-q8_0.gguf: 8-bit quantization, providing robust performance with a file size of 3.8 GB.
- ggml-model-f16.gguf: Standard 16-bit floating-point format, with a larger file size of 7.2 GB for enhanced precision.
These formats, ranging from 4-bit to 16-bit, accommodate various computational environments, from resource-constrained devices to high-end server
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