README.md

metadata

license: mit
base_model: microsoft/MediPhi-Instruct
library_name: mlx
pipeline_tag: text-generation
language:
  - en
tags:
  - mlx
  - mlx-lm
  - phi
  - phi-3
  - medical
  - clinical
  - healthcare
  - quantized
  - 4bit
  - on-device
  - ios
  - apple-silicon
model_type: phi
quantization:
  - 4bit
datasets:
  - microsoft/mediflow
  - ncbi/pubmed
  - epfl-llm/guidelines
  - starmpcc/Asclepius-Synthetic-Clinical-Notes
  - akemiH/NoteChat
  - zhengyun21/PMC-Patients
  - jpcorb20/medical_wikipedia

MediPhi-Instruct (MLX · 4-bit)

This repository contains an MLX-format 4-bit quantized version of
microsoft/MediPhi-Instruct,
converted using mlx-lm for efficient on-device inference on Apple silicon.

This model is intended for iOS / iPadOS / macOS usage where memory and power constraints require aggressive quantization while preserving clinical reasoning quality.

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Model details

• Base model: MediPhi-Instruct (Phi-3 family)
• Parameters: ~3.8B
• Quantization: 4-bit (MLX)
• Format: MLX (not GGUF)
• Intended use: On-device medical and clinical QA, decision support, and explanations
• Language: English

⚠️ This is a conversion only. No additional fine-tuning was performed.

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Why MLX 4-bit?

Compared to larger 4–7B medical models, MediPhi-Instruct shows:

• Strong clinical reasoning per parameter
• Better robustness under 4-bit quantization
• Lower memory footprint suitable for mobile devices

This makes it a strong candidate for on-device medical assistants on iPhone and iPad.

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Usage (MLX-LM)

Install

pip install mlx-lm