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GGUF
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GGUF
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bigscience/bloomz-1b7 - GGUF

This repo contains GGUF format model files for bigscience/bloomz-1b7.

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

Prompt template


Model file specification

Filename Quant type File Size Description
bloomz-1b7-Q2_K.gguf Q2_K 0.980 GB smallest, significant quality loss - not recommended for most purposes
bloomz-1b7-Q3_K_S.gguf Q3_K_S 1.096 GB very small, high quality loss
bloomz-1b7-Q3_K_M.gguf Q3_K_M 1.197 GB very small, high quality loss
bloomz-1b7-Q3_K_L.gguf Q3_K_L 1.254 GB small, substantial quality loss
bloomz-1b7-Q4_0.gguf Q4_0 1.309 GB legacy; small, very high quality loss - prefer using Q3_K_M
bloomz-1b7-Q4_K_S.gguf Q4_K_S 1.315 GB small, greater quality loss
bloomz-1b7-Q4_K_M.gguf Q4_K_M 1.392 GB medium, balanced quality - recommended
bloomz-1b7-Q5_0.gguf Q5_0 1.509 GB legacy; medium, balanced quality - prefer using Q4_K_M
bloomz-1b7-Q5_K_S.gguf Q5_K_S 1.509 GB large, low quality loss - recommended
bloomz-1b7-Q5_K_M.gguf Q5_K_M 1.571 GB large, very low quality loss - recommended
bloomz-1b7-Q6_K.gguf Q6_K 1.722 GB very large, extremely low quality loss
bloomz-1b7-Q8_0.gguf Q8_0 2.226 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/bloomz-1b7-GGUF --include "bloomz-1b7-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/bloomz-1b7-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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GGUF
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Architecture
bloom

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Inference Examples
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Dataset used to train tensorblock/bloomz-1b7-GGUF

Evaluation results