metadata
license: mit
license_link: https://huggingface.co/microsoft/Phi-3-medium-4k-instruct/resolve/main/LICENSE
language:
- multilingual
pipeline_tag: text-generation
tags:
- nlp
- code
- TensorBlock
- GGUF
inference:
parameters:
temperature: 0.7
widget:
- messages:
- role: user
content: Can you provide ways to eat combinations of bananas and dragonfruits?
base_model: microsoft/Phi-3-medium-4k-instruct
microsoft/Phi-3-medium-4k-instruct - GGUF
This repo contains GGUF format model files for microsoft/Phi-3-medium-4k-instruct.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit ec7f3ac.
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<|user|>
{prompt}<|end|>
<|assistant|>
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| Phi-3-medium-4k-instruct-Q2_K.gguf | Q2_K | 5.143 GB | smallest, significant quality loss - not recommended for most purposes |
| Phi-3-medium-4k-instruct-Q3_K_S.gguf | Q3_K_S | 6.065 GB | very small, high quality loss |
| Phi-3-medium-4k-instruct-Q3_K_M.gguf | Q3_K_M | 6.923 GB | very small, high quality loss |
| Phi-3-medium-4k-instruct-Q3_K_L.gguf | Q3_K_L | 7.490 GB | small, substantial quality loss |
| Phi-3-medium-4k-instruct-Q4_0.gguf | Q4_0 | 7.897 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| Phi-3-medium-4k-instruct-Q4_K_S.gguf | Q4_K_S | 7.954 GB | small, greater quality loss |
| Phi-3-medium-4k-instruct-Q4_K_M.gguf | Q4_K_M | 8.567 GB | medium, balanced quality - recommended |
| Phi-3-medium-4k-instruct-Q5_0.gguf | Q5_0 | 9.622 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| Phi-3-medium-4k-instruct-Q5_K_S.gguf | Q5_K_S | 9.622 GB | large, low quality loss - recommended |
| Phi-3-medium-4k-instruct-Q5_K_M.gguf | Q5_K_M | 10.074 GB | large, very low quality loss - recommended |
| Phi-3-medium-4k-instruct-Q6_K.gguf | Q6_K | 11.454 GB | very large, extremely low quality loss |
| Phi-3-medium-4k-instruct-Q8_0.gguf | Q8_0 | 14.835 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/Phi-3-medium-4k-instruct-GGUF --include "Phi-3-medium-4k-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/Phi-3-medium-4k-instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'