tensorblock/Faro-Yi-9B-DPO-GGUF

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wenbopan/Faro-Yi-9B-DPO - GGUF

This repo contains GGUF format model files for wenbopan/Faro-Yi-9B-DPO.

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

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Filename	Quant type	File Size	Description
Faro-Yi-9B-DPO-Q2_K.gguf	Q2_K	3.124 GB	smallest, significant quality loss - not recommended for most purposes
Faro-Yi-9B-DPO-Q3_K_S.gguf	Q3_K_S	3.631 GB	very small, high quality loss
Faro-Yi-9B-DPO-Q3_K_M.gguf	Q3_K_M	4.027 GB	very small, high quality loss
Faro-Yi-9B-DPO-Q3_K_L.gguf	Q3_K_L	4.369 GB	small, substantial quality loss
Faro-Yi-9B-DPO-Q4_0.gguf	Q4_0	4.691 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Faro-Yi-9B-DPO-Q4_K_S.gguf	Q4_K_S	4.724 GB	small, greater quality loss
Faro-Yi-9B-DPO-Q4_K_M.gguf	Q4_K_M	4.963 GB	medium, balanced quality - recommended
Faro-Yi-9B-DPO-Q5_0.gguf	Q5_0	5.688 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Faro-Yi-9B-DPO-Q5_K_S.gguf	Q5_K_S	5.688 GB	large, low quality loss - recommended
Faro-Yi-9B-DPO-Q5_K_M.gguf	Q5_K_M	5.828 GB	large, very low quality loss - recommended
Faro-Yi-9B-DPO-Q6_K.gguf	Q6_K	6.748 GB	very large, extremely low quality loss
Faro-Yi-9B-DPO-Q8_0.gguf	Q8_0	8.739 GB	very large, extremely low quality loss - not recommended

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/Faro-Yi-9B-DPO-GGUF --include "Faro-Yi-9B-DPO-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/Faro-Yi-9B-DPO-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'