tensorblock/AMD-OLMo-1B-SFT-GGUF

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amd/AMD-OLMo-1B-SFT - GGUF

This repo contains GGUF format model files for amd/AMD-OLMo-1B-SFT.

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

Filename	Quant type	File Size	Description
AMD-OLMo-1B-SFT-Q2_K.gguf	Q2_K	0.480 GB	smallest, significant quality loss - not recommended for most purposes
AMD-OLMo-1B-SFT-Q3_K_S.gguf	Q3_K_S	0.548 GB	very small, high quality loss
AMD-OLMo-1B-SFT-Q3_K_M.gguf	Q3_K_M	0.604 GB	very small, high quality loss
AMD-OLMo-1B-SFT-Q3_K_L.gguf	Q3_K_L	0.651 GB	small, substantial quality loss
AMD-OLMo-1B-SFT-Q4_0.gguf	Q4_0	0.690 GB	legacy; small, very high quality loss - prefer using Q3_K_M
AMD-OLMo-1B-SFT-Q4_K_S.gguf	Q4_K_S	0.697 GB	small, greater quality loss
AMD-OLMo-1B-SFT-Q4_K_M.gguf	Q4_K_M	0.734 GB	medium, balanced quality - recommended
AMD-OLMo-1B-SFT-Q5_0.gguf	Q5_0	0.824 GB	legacy; medium, balanced quality - prefer using Q4_K_M
AMD-OLMo-1B-SFT-Q5_K_S.gguf	Q5_K_S	0.824 GB	large, low quality loss - recommended
AMD-OLMo-1B-SFT-Q5_K_M.gguf	Q5_K_M	0.847 GB	large, very low quality loss - recommended
AMD-OLMo-1B-SFT-Q6_K.gguf	Q6_K	0.967 GB	very large, extremely low quality loss
AMD-OLMo-1B-SFT-Q8_0.gguf	Q8_0	1.252 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/AMD-OLMo-1B-SFT-GGUF --include "AMD-OLMo-1B-SFT-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/AMD-OLMo-1B-SFT-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'