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Phi-3-medium-128k-instruct-GGUF

Text Generation
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Phi-3-medium-128k-instruct-GGUF / README.md
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metadata
license: mit
license_link: >-
  https://huggingface.co/microsoft/Phi-3-medium-128k-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-128k-instruct
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microsoft/Phi-3-medium-128k-instruct - GGUF

This repo contains GGUF format model files for microsoft/Phi-3-medium-128k-instruct.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

Prompt template 

<|user|>
{prompt}<|end|>
<|assistant|>

Model file specification

Filename Quant type File Size Description
Phi-3-medium-128k-instruct-Q2_K.gguf Q2_K 4.790 GB smallest, significant quality loss - not recommended for most purposes
Phi-3-medium-128k-instruct-Q3_K_S.gguf Q3_K_S 5.648 GB very small, high quality loss
Phi-3-medium-128k-instruct-Q3_K_M.gguf Q3_K_M 6.448 GB very small, high quality loss
Phi-3-medium-128k-instruct-Q3_K_L.gguf Q3_K_L 6.976 GB small, substantial quality loss
Phi-3-medium-128k-instruct-Q4_0.gguf Q4_0 7.355 GB legacy; small, very high quality loss - prefer using Q3_K_M
Phi-3-medium-128k-instruct-Q4_K_S.gguf Q4_K_S 7.408 GB small, greater quality loss
Phi-3-medium-128k-instruct-Q4_K_M.gguf Q4_K_M 7.978 GB medium, balanced quality - recommended
Phi-3-medium-128k-instruct-Q5_0.gguf Q5_0 8.961 GB legacy; medium, balanced quality - prefer using Q4_K_M
Phi-3-medium-128k-instruct-Q5_K_S.gguf Q5_K_S 8.961 GB large, low quality loss - recommended
Phi-3-medium-128k-instruct-Q5_K_M.gguf Q5_K_M 9.382 GB large, very low quality loss - recommended
Phi-3-medium-128k-instruct-Q6_K.gguf Q6_K 10.667 GB very large, extremely low quality loss
Phi-3-medium-128k-instruct-Q8_0.gguf Q8_0 13.816 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-128k-instruct-GGUF --include "Phi-3-medium-128k-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-128k-instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'