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bigscience/bloom-3b - GGUF
This repo contains GGUF format model files for bigscience/bloom-3b.
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 |
---|---|---|---|
bloom-3b-Q2_K.gguf | Q2_K | 1.516 GB | smallest, significant quality loss - not recommended for most purposes |
bloom-3b-Q3_K_S.gguf | Q3_K_S | 1.707 GB | very small, high quality loss |
bloom-3b-Q3_K_M.gguf | Q3_K_M | 1.905 GB | very small, high quality loss |
bloom-3b-Q3_K_L.gguf | Q3_K_L | 2.016 GB | small, substantial quality loss |
bloom-3b-Q4_0.gguf | Q4_0 | 2.079 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
bloom-3b-Q4_K_S.gguf | Q4_K_S | 2.088 GB | small, greater quality loss |
bloom-3b-Q4_K_M.gguf | Q4_K_M | 2.235 GB | medium, balanced quality - recommended |
bloom-3b-Q5_0.gguf | Q5_0 | 2.428 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
bloom-3b-Q5_K_S.gguf | Q5_K_S | 2.428 GB | large, low quality loss - recommended |
bloom-3b-Q5_K_M.gguf | Q5_K_M | 2.546 GB | large, very low quality loss - recommended |
bloom-3b-Q6_K.gguf | Q6_K | 2.799 GB | very large, extremely low quality loss |
bloom-3b-Q8_0.gguf | Q8_0 | 3.621 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/bloom-3b-GGUF --include "bloom-3b-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/bloom-3b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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Model tree for tensorblock/bloom-3b-GGUF
Base model
bigscience/bloom-3bEvaluation results
- acc on arc_challengeself-reported0.280
- acc on arc_easyself-reported0.595
- acc on axbself-reported0.443
- acc on axgself-reported0.500
- acc on boolqself-reported0.617
- acc on cbself-reported0.304
- acc on colaself-reported0.611
- acc on copaself-reported0.630
- acc on crows_pairs_englishself-reported0.497
- acc on crows_pairs_frenchself-reported0.503