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PracticeLLM/KoSOLAR-Platypus-10.7B - GGUF

This repo contains GGUF format model files for PracticeLLM/KoSOLAR-Platypus-10.7B.

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
KoSOLAR-Platypus-10.7B-Q2_K.gguf Q2_K 3.768 GB smallest, significant quality loss - not recommended for most purposes
KoSOLAR-Platypus-10.7B-Q3_K_S.gguf Q3_K_S 4.387 GB very small, high quality loss
KoSOLAR-Platypus-10.7B-Q3_K_M.gguf Q3_K_M 4.882 GB very small, high quality loss
KoSOLAR-Platypus-10.7B-Q3_K_L.gguf Q3_K_L 5.306 GB small, substantial quality loss
KoSOLAR-Platypus-10.7B-Q4_0.gguf Q4_0 5.703 GB legacy; small, very high quality loss - prefer using Q3_K_M
KoSOLAR-Platypus-10.7B-Q4_K_S.gguf Q4_K_S 5.746 GB small, greater quality loss
KoSOLAR-Platypus-10.7B-Q4_K_M.gguf Q4_K_M 6.065 GB medium, balanced quality - recommended
KoSOLAR-Platypus-10.7B-Q5_0.gguf Q5_0 6.941 GB legacy; medium, balanced quality - prefer using Q4_K_M
KoSOLAR-Platypus-10.7B-Q5_K_S.gguf Q5_K_S 6.941 GB large, low quality loss - recommended
KoSOLAR-Platypus-10.7B-Q5_K_M.gguf Q5_K_M 7.128 GB large, very low quality loss - recommended
KoSOLAR-Platypus-10.7B-Q6_K.gguf Q6_K 8.257 GB very large, extremely low quality loss
KoSOLAR-Platypus-10.7B-Q8_0.gguf Q8_0 10.694 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/KoSOLAR-Platypus-10.7B-GGUF --include "KoSOLAR-Platypus-10.7B-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/KoSOLAR-Platypus-10.7B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
Downloads last month
183
GGUF
Model size
10.8B params
Architecture
llama

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