tensorblock/math_gpt2_sft-GGUF

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Sharathhebbar24/math_gpt2_sft - GGUF

This repo contains GGUF format model files for Sharathhebbar24/math_gpt2_sft.

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
math_gpt2_sft-Q2_K.gguf	Q2_K	0.081 GB	smallest, significant quality loss - not recommended for most purposes
math_gpt2_sft-Q3_K_S.gguf	Q3_K_S	0.090 GB	very small, high quality loss
math_gpt2_sft-Q3_K_M.gguf	Q3_K_M	0.098 GB	very small, high quality loss
math_gpt2_sft-Q3_K_L.gguf	Q3_K_L	0.102 GB	small, substantial quality loss
math_gpt2_sft-Q4_0.gguf	Q4_0	0.107 GB	legacy; small, very high quality loss - prefer using Q3_K_M
math_gpt2_sft-Q4_K_S.gguf	Q4_K_S	0.107 GB	small, greater quality loss
math_gpt2_sft-Q4_K_M.gguf	Q4_K_M	0.113 GB	medium, balanced quality - recommended
math_gpt2_sft-Q5_0.gguf	Q5_0	0.122 GB	legacy; medium, balanced quality - prefer using Q4_K_M
math_gpt2_sft-Q5_K_S.gguf	Q5_K_S	0.122 GB	large, low quality loss - recommended
math_gpt2_sft-Q5_K_M.gguf	Q5_K_M	0.127 GB	large, very low quality loss - recommended
math_gpt2_sft-Q6_K.gguf	Q6_K	0.138 GB	very large, extremely low quality loss
math_gpt2_sft-Q8_0.gguf	Q8_0	0.178 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/math_gpt2_sft-GGUF --include "math_gpt2_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/math_gpt2_sft-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'