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tensorblock/
SN9-C4-llama-HK5-1-GGUF

Name: SN9-C4-llama-HK5-1-GGUF
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deepnet/SN9-C4-llama-HK5-1 - GGUF

This repo contains GGUF format model files for deepnet/SN9-C4-llama-HK5-1.

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

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## Prompt template

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Filename	Quant type	File Size	Description
SN9-C4-llama-HK5-1-Q2_K.gguf	Q2_K	5.760 GB	smallest, significant quality loss - not recommended for most purposes
SN9-C4-llama-HK5-1-Q3_K_S.gguf	Q3_K_S	6.626 GB	very small, high quality loss
SN9-C4-llama-HK5-1-Q3_K_M.gguf	Q3_K_M	7.236 GB	very small, high quality loss
SN9-C4-llama-HK5-1-Q3_K_L.gguf	Q3_K_L	7.577 GB	small, substantial quality loss
SN9-C4-llama-HK5-1-Q4_0.gguf	Q4_0	7.927 GB	legacy; small, very high quality loss - prefer using Q3_K_M
SN9-C4-llama-HK5-1-Q4_K_S.gguf	Q4_K_S	8.419 GB	small, greater quality loss
SN9-C4-llama-HK5-1-Q4_K_M.gguf	Q4_K_M	9.105 GB	medium, balanced quality - recommended
SN9-C4-llama-HK5-1-Q5_0.gguf	Q5_0	9.613 GB	legacy; medium, balanced quality - prefer using Q4_K_M
SN9-C4-llama-HK5-1-Q5_K_S.gguf	Q5_K_S	9.843 GB	large, low quality loss - recommended
SN9-C4-llama-HK5-1-Q5_K_M.gguf	Q5_K_M	10.433 GB	large, very low quality loss - recommended
SN9-C4-llama-HK5-1-Q6_K.gguf	Q6_K	12.296 GB	very large, extremely low quality loss
SN9-C4-llama-HK5-1-Q8_0.gguf	Q8_0	14.769 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/SN9-C4-llama-HK5-1-GGUF --include "SN9-C4-llama-HK5-1-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/SN9-C4-llama-HK5-1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'