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ajibawa-2023/OpenHermes-2.5-Code-290k-13B - GGUF

This repo contains GGUF format model files for ajibawa-2023/OpenHermes-2.5-Code-290k-13B.

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

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Model file specification

Filename Quant type File Size Description
OpenHermes-2.5-Code-290k-13B-Q2_K.gguf Q2_K 4.854 GB smallest, significant quality loss - not recommended for most purposes
OpenHermes-2.5-Code-290k-13B-Q3_K_S.gguf Q3_K_S 5.659 GB very small, high quality loss
OpenHermes-2.5-Code-290k-13B-Q3_K_M.gguf Q3_K_M 6.338 GB very small, high quality loss
OpenHermes-2.5-Code-290k-13B-Q3_K_L.gguf Q3_K_L 6.930 GB small, substantial quality loss
OpenHermes-2.5-Code-290k-13B-Q4_0.gguf Q4_0 7.366 GB legacy; small, very high quality loss - prefer using Q3_K_M
OpenHermes-2.5-Code-290k-13B-Q4_K_S.gguf Q4_K_S 7.423 GB small, greater quality loss
OpenHermes-2.5-Code-290k-13B-Q4_K_M.gguf Q4_K_M 7.866 GB medium, balanced quality - recommended
OpenHermes-2.5-Code-290k-13B-Q5_0.gguf Q5_0 8.972 GB legacy; medium, balanced quality - prefer using Q4_K_M
OpenHermes-2.5-Code-290k-13B-Q5_K_S.gguf Q5_K_S 8.972 GB large, low quality loss - recommended
OpenHermes-2.5-Code-290k-13B-Q5_K_M.gguf Q5_K_M 9.230 GB large, very low quality loss - recommended
OpenHermes-2.5-Code-290k-13B-Q6_K.gguf Q6_K 10.679 GB very large, extremely low quality loss
OpenHermes-2.5-Code-290k-13B-Q8_0.gguf Q8_0 13.831 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/OpenHermes-2.5-Code-290k-13B-GGUF --include "OpenHermes-2.5-Code-290k-13B-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/OpenHermes-2.5-Code-290k-13B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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GGUF
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Architecture
llama
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