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AIdenU/SOLAR-10.7b-ko-Y24_v0.1 - GGUF

This repo contains GGUF format model files for AIdenU/SOLAR-10.7b-ko-Y24_v0.1.

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

Prompt template


Model file specification

Filename Quant type File Size Description
SOLAR-10.7b-ko-Y24_v0.1-Q2_K.gguf Q2_K 3.728 GB smallest, significant quality loss - not recommended for most purposes
SOLAR-10.7b-ko-Y24_v0.1-Q3_K_S.gguf Q3_K_S 4.344 GB very small, high quality loss
SOLAR-10.7b-ko-Y24_v0.1-Q3_K_M.gguf Q3_K_M 4.839 GB very small, high quality loss
SOLAR-10.7b-ko-Y24_v0.1-Q3_K_L.gguf Q3_K_L 5.263 GB small, substantial quality loss
SOLAR-10.7b-ko-Y24_v0.1-Q4_0.gguf Q4_0 5.655 GB legacy; small, very high quality loss - prefer using Q3_K_M
SOLAR-10.7b-ko-Y24_v0.1-Q4_K_S.gguf Q4_K_S 5.698 GB small, greater quality loss
SOLAR-10.7b-ko-Y24_v0.1-Q4_K_M.gguf Q4_K_M 6.018 GB medium, balanced quality - recommended
SOLAR-10.7b-ko-Y24_v0.1-Q5_0.gguf Q5_0 6.889 GB legacy; medium, balanced quality - prefer using Q4_K_M
SOLAR-10.7b-ko-Y24_v0.1-Q5_K_S.gguf Q5_K_S 6.889 GB large, low quality loss - recommended
SOLAR-10.7b-ko-Y24_v0.1-Q5_K_M.gguf Q5_K_M 7.076 GB large, very low quality loss - recommended
SOLAR-10.7b-ko-Y24_v0.1-Q6_K.gguf Q6_K 8.200 GB very large, extremely low quality loss
SOLAR-10.7b-ko-Y24_v0.1-Q8_0.gguf Q8_0 10.621 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/SOLAR-10.7b-ko-Y24_v0.1-GGUF --include "SOLAR-10.7b-ko-Y24_v0.1-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/SOLAR-10.7b-ko-Y24_v0.1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
Downloads last month
53
GGUF
Model size
10.7B params
Architecture
llama

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Inference Examples
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