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ryandt/MusingCaterpillar - GGUF

This repo contains GGUF format model files for ryandt/MusingCaterpillar.

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

Prompt template


Model file specification

Filename Quant type File Size Description
MusingCaterpillar-Q2_K.gguf Q2_K 3.361 GB smallest, significant quality loss - not recommended for most purposes
MusingCaterpillar-Q3_K_S.gguf Q3_K_S 3.915 GB very small, high quality loss
MusingCaterpillar-Q3_K_M.gguf Q3_K_M 4.354 GB very small, high quality loss
MusingCaterpillar-Q3_K_L.gguf Q3_K_L 4.736 GB small, substantial quality loss
MusingCaterpillar-Q4_0.gguf Q4_0 5.091 GB legacy; small, very high quality loss - prefer using Q3_K_M
MusingCaterpillar-Q4_K_S.gguf Q4_K_S 5.129 GB small, greater quality loss
MusingCaterpillar-Q4_K_M.gguf Q4_K_M 5.415 GB medium, balanced quality - recommended
MusingCaterpillar-Q5_0.gguf Q5_0 6.198 GB legacy; medium, balanced quality - prefer using Q4_K_M
MusingCaterpillar-Q5_K_S.gguf Q5_K_S 6.198 GB large, low quality loss - recommended
MusingCaterpillar-Q5_K_M.gguf Q5_K_M 6.365 GB large, very low quality loss - recommended
MusingCaterpillar-Q6_K.gguf Q6_K 7.374 GB very large, extremely low quality loss
MusingCaterpillar-Q8_0.gguf Q8_0 9.550 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/MusingCaterpillar-GGUF --include "MusingCaterpillar-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/MusingCaterpillar-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
Downloads last month
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GGUF
Model size
8.99B params
Architecture
llama

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Model tree for tensorblock/MusingCaterpillar-GGUF

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this model

Dataset used to train tensorblock/MusingCaterpillar-GGUF

Evaluation results