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MediaTek-Research/Breeze-7B-Instruct-v1_0 - GGUF

This repo contains GGUF format model files for MediaTek-Research/Breeze-7B-Instruct-v1_0.

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

Prompt template

<s> {system_prompt}  [INST] {prompt} [/INST] 

Model file specification

Filename Quant type File Size Description
Breeze-7B-Instruct-v1_0-Q2_K.gguf Q2_K 2.664 GB smallest, significant quality loss - not recommended for most purposes
Breeze-7B-Instruct-v1_0-Q3_K_S.gguf Q3_K_S 3.091 GB very small, high quality loss
Breeze-7B-Instruct-v1_0-Q3_K_M.gguf Q3_K_M 3.421 GB very small, high quality loss
Breeze-7B-Instruct-v1_0-Q3_K_L.gguf Q3_K_L 3.703 GB small, substantial quality loss
Breeze-7B-Instruct-v1_0-Q4_0.gguf Q4_0 3.985 GB legacy; small, very high quality loss - prefer using Q3_K_M
Breeze-7B-Instruct-v1_0-Q4_K_S.gguf Q4_K_S 4.015 GB small, greater quality loss
Breeze-7B-Instruct-v1_0-Q4_K_M.gguf Q4_K_M 4.227 GB medium, balanced quality - recommended
Breeze-7B-Instruct-v1_0-Q5_0.gguf Q5_0 4.827 GB legacy; medium, balanced quality - prefer using Q4_K_M
Breeze-7B-Instruct-v1_0-Q5_K_S.gguf Q5_K_S 4.827 GB large, low quality loss - recommended
Breeze-7B-Instruct-v1_0-Q5_K_M.gguf Q5_K_M 4.952 GB large, very low quality loss - recommended
Breeze-7B-Instruct-v1_0-Q6_K.gguf Q6_K 5.722 GB very large, extremely low quality loss
Breeze-7B-Instruct-v1_0-Q8_0.gguf Q8_0 7.411 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/Breeze-7B-Instruct-v1_0-GGUF --include "Breeze-7B-Instruct-v1_0-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/Breeze-7B-Instruct-v1_0-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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GGUF
Model size
7.49B params
Architecture
llama

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Inference Examples
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