tensorblock/NeuralQuant-9B-GGUF

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mlabonne/NeuralQuant-9B - GGUF

This repo contains GGUF format model files for mlabonne/NeuralQuant-9B.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.

Filename	Quant type	File Size	Description
NeuralQuant-9B-Q2_K.gguf	Q2_K	3.361 GB	smallest, significant quality loss - not recommended for most purposes
NeuralQuant-9B-Q3_K_S.gguf	Q3_K_S	3.915 GB	very small, high quality loss
NeuralQuant-9B-Q3_K_M.gguf	Q3_K_M	4.354 GB	very small, high quality loss
NeuralQuant-9B-Q3_K_L.gguf	Q3_K_L	4.736 GB	small, substantial quality loss
NeuralQuant-9B-Q4_0.gguf	Q4_0	5.091 GB	legacy; small, very high quality loss - prefer using Q3_K_M
NeuralQuant-9B-Q4_K_S.gguf	Q4_K_S	5.129 GB	small, greater quality loss
NeuralQuant-9B-Q4_K_M.gguf	Q4_K_M	5.415 GB	medium, balanced quality - recommended
NeuralQuant-9B-Q5_0.gguf	Q5_0	6.198 GB	legacy; medium, balanced quality - prefer using Q4_K_M
NeuralQuant-9B-Q5_K_S.gguf	Q5_K_S	6.198 GB	large, low quality loss - recommended
NeuralQuant-9B-Q5_K_M.gguf	Q5_K_M	6.365 GB	large, very low quality loss - recommended
NeuralQuant-9B-Q6_K.gguf	Q6_K	7.374 GB	very large, extremely low quality loss
NeuralQuant-9B-Q8_0.gguf	Q8_0	9.550 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/NeuralQuant-9B-GGUF --include "NeuralQuant-9B-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/NeuralQuant-9B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'