M4-ai/Orca-2.0-Tau-1.8B - GGUF

This repo contains GGUF format model files for M4-ai/Orca-2.0-Tau-1.8B.

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

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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
Orca-2.0-Tau-1.8B-Q2_K.gguf	Q2_K	0.847 GB	smallest, significant quality loss - not recommended for most purposes
Orca-2.0-Tau-1.8B-Q3_K_S.gguf	Q3_K_S	0.954 GB	very small, high quality loss
Orca-2.0-Tau-1.8B-Q3_K_M.gguf	Q3_K_M	1.016 GB	very small, high quality loss
Orca-2.0-Tau-1.8B-Q3_K_L.gguf	Q3_K_L	1.056 GB	small, substantial quality loss
Orca-2.0-Tau-1.8B-Q4_0.gguf	Q4_0	1.120 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Orca-2.0-Tau-1.8B-Q4_K_S.gguf	Q4_K_S	1.158 GB	small, greater quality loss
Orca-2.0-Tau-1.8B-Q4_K_M.gguf	Q4_K_M	1.218 GB	medium, balanced quality - recommended
Orca-2.0-Tau-1.8B-Q5_0.gguf	Q5_0	1.311 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Orca-2.0-Tau-1.8B-Q5_K_S.gguf	Q5_K_S	1.328 GB	large, low quality loss - recommended
Orca-2.0-Tau-1.8B-Q5_K_M.gguf	Q5_K_M	1.377 GB	large, very low quality loss - recommended
Orca-2.0-Tau-1.8B-Q6_K.gguf	Q6_K	1.579 GB	very large, extremely low quality loss
Orca-2.0-Tau-1.8B-Q8_0.gguf	Q8_0	1.958 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/Orca-2.0-Tau-1.8B-GGUF --include "Orca-2.0-Tau-1.8B-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/Orca-2.0-Tau-1.8B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'