tensorblock/pythia-160m-c2s-GGUF

Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server

vandijklab/pythia-160m-c2s - GGUF

This repo contains GGUF format model files for vandijklab/pythia-160m-c2s.

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
pythia-160m-c2s-Q2_K.gguf	Q2_K	0.078 GB	smallest, significant quality loss - not recommended for most purposes
pythia-160m-c2s-Q3_K_S.gguf	Q3_K_S	0.087 GB	very small, high quality loss
pythia-160m-c2s-Q3_K_M.gguf	Q3_K_M	0.095 GB	very small, high quality loss
pythia-160m-c2s-Q3_K_L.gguf	Q3_K_L	0.099 GB	small, substantial quality loss
pythia-160m-c2s-Q4_0.gguf	Q4_0	0.103 GB	legacy; small, very high quality loss - prefer using Q3_K_M
pythia-160m-c2s-Q4_K_S.gguf	Q4_K_S	0.104 GB	small, greater quality loss
pythia-160m-c2s-Q4_K_M.gguf	Q4_K_M	0.110 GB	medium, balanced quality - recommended
pythia-160m-c2s-Q5_0.gguf	Q5_0	0.119 GB	legacy; medium, balanced quality - prefer using Q4_K_M
pythia-160m-c2s-Q5_K_S.gguf	Q5_K_S	0.119 GB	large, low quality loss - recommended
pythia-160m-c2s-Q5_K_M.gguf	Q5_K_M	0.124 GB	large, very low quality loss - recommended
pythia-160m-c2s-Q6_K.gguf	Q6_K	0.135 GB	very large, extremely low quality loss
pythia-160m-c2s-Q8_0.gguf	Q8_0	0.175 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/pythia-160m-c2s-GGUF --include "pythia-160m-c2s-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/pythia-160m-c2s-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'