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allbyai/ToRoLaMa-7b-v1.0 - GGUF

This repo contains GGUF format model files for allbyai/ToRoLaMa-7b-v1.0.

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
ToRoLaMa-7b-v1.0-Q2_K.gguf Q2_K 2.600 GB smallest, significant quality loss - not recommended for most purposes
ToRoLaMa-7b-v1.0-Q3_K_S.gguf Q3_K_S 3.022 GB very small, high quality loss
ToRoLaMa-7b-v1.0-Q3_K_M.gguf Q3_K_M 3.372 GB very small, high quality loss
ToRoLaMa-7b-v1.0-Q3_K_L.gguf Q3_K_L 3.671 GB small, substantial quality loss
ToRoLaMa-7b-v1.0-Q4_0.gguf Q4_0 3.907 GB legacy; small, very high quality loss - prefer using Q3_K_M
ToRoLaMa-7b-v1.0-Q4_K_S.gguf Q4_K_S 3.938 GB small, greater quality loss
ToRoLaMa-7b-v1.0-Q4_K_M.gguf Q4_K_M 4.162 GB medium, balanced quality - recommended
ToRoLaMa-7b-v1.0-Q5_0.gguf Q5_0 4.740 GB legacy; medium, balanced quality - prefer using Q4_K_M
ToRoLaMa-7b-v1.0-Q5_K_S.gguf Q5_K_S 4.740 GB large, low quality loss - recommended
ToRoLaMa-7b-v1.0-Q5_K_M.gguf Q5_K_M 4.872 GB large, very low quality loss - recommended
ToRoLaMa-7b-v1.0-Q6_K.gguf Q6_K 5.626 GB very large, extremely low quality loss
ToRoLaMa-7b-v1.0-Q8_0.gguf Q8_0 7.286 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/ToRoLaMa-7b-v1.0-GGUF --include "ToRoLaMa-7b-v1.0-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/ToRoLaMa-7b-v1.0-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
Downloads last month
136
GGUF
Model size
6.86B params
Architecture
llama

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Inference Examples
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Model tree for tensorblock/ToRoLaMa-7b-v1.0-GGUF

Quantized
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this model

Evaluation results