metadata
license: mit
datasets:
- microsoft/mediflow
- ncbi/pubmed
- epfl-llm/guidelines
- starmpcc/Asclepius-Synthetic-Clinical-Notes
- akemiH/NoteChat
- zhengyun21/PMC-Patients
- jpcorb20/medical_wikipedia
language:
- en
base_model: microsoft/MediPhi-Instruct
library_name: transformers
tags:
- merge
- mergekit
- medical
- clinical
- TensorBlock
- GGUF
microsoft/MediPhi-Instruct - GGUF
This repo contains GGUF format model files for microsoft/MediPhi-Instruct.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b5753.
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Prompt template
<|system|>
{system_prompt}<|end|>
<|user|>
{prompt}<|end|>
<|assistant|>
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| MediPhi-Instruct-Q2_K.gguf | Q2_K | 1.416 GB | smallest, significant quality loss - not recommended for most purposes |
| MediPhi-Instruct-Q3_K_S.gguf | Q3_K_S | 1.682 GB | very small, high quality loss |
| MediPhi-Instruct-Q3_K_M.gguf | Q3_K_M | 1.955 GB | very small, high quality loss |
| MediPhi-Instruct-Q3_K_L.gguf | Q3_K_L | 2.088 GB | small, substantial quality loss |
| MediPhi-Instruct-Q4_0.gguf | Q4_0 | 2.176 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| MediPhi-Instruct-Q4_K_S.gguf | Q4_K_S | 2.189 GB | small, greater quality loss |
| MediPhi-Instruct-Q4_K_M.gguf | Q4_K_M | 2.393 GB | medium, balanced quality - recommended |
| MediPhi-Instruct-Q5_0.gguf | Q5_0 | 2.641 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| MediPhi-Instruct-Q5_K_S.gguf | Q5_K_S | 2.641 GB | large, low quality loss - recommended |
| MediPhi-Instruct-Q5_K_M.gguf | Q5_K_M | 2.815 GB | large, very low quality loss - recommended |
| MediPhi-Instruct-Q6_K.gguf | Q6_K | 3.136 GB | very large, extremely low quality loss |
| MediPhi-Instruct-Q8_0.gguf | Q8_0 | 4.061 GB | very large, extremely low quality loss - not recommended |
Downloading instruction
Command line
Firstly, install Huggingface Client
pip install -U "huggingface_hub[cli]"
Then, downoad the individual model file the a local directory
huggingface-cli download tensorblock/microsoft_MediPhi-Instruct-GGUF --include "MediPhi-Instruct-Q2_K.gguf" --local-dir MY_LOCAL_DIR
If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:
huggingface-cli download tensorblock/microsoft_MediPhi-Instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'