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GritLM/GritLM-7B - GGUF
This repo contains GGUF format model files for GritLM/GritLM-7B.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
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Model file specification
Filename | Quant type | File Size | Description |
---|---|---|---|
GritLM-7B-Q2_K.gguf | Q2_K | 2.532 GB | smallest, significant quality loss - not recommended for most purposes |
GritLM-7B-Q3_K_S.gguf | Q3_K_S | 2.947 GB | very small, high quality loss |
GritLM-7B-Q3_K_M.gguf | Q3_K_M | 3.277 GB | very small, high quality loss |
GritLM-7B-Q3_K_L.gguf | Q3_K_L | 3.560 GB | small, substantial quality loss |
GritLM-7B-Q4_0.gguf | Q4_0 | 3.827 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
GritLM-7B-Q4_K_S.gguf | Q4_K_S | 3.856 GB | small, greater quality loss |
GritLM-7B-Q4_K_M.gguf | Q4_K_M | 4.068 GB | medium, balanced quality - recommended |
GritLM-7B-Q5_0.gguf | Q5_0 | 4.654 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
GritLM-7B-Q5_K_S.gguf | Q5_K_S | 4.654 GB | large, low quality loss - recommended |
GritLM-7B-Q5_K_M.gguf | Q5_K_M | 4.779 GB | large, very low quality loss - recommended |
GritLM-7B-Q6_K.gguf | Q6_K | 5.534 GB | very large, extremely low quality loss |
GritLM-7B-Q8_0.gguf | Q8_0 | 7.167 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/GritLM-7B-GGUF --include "GritLM-7B-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/GritLM-7B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
- Downloads last month
- 12
Model tree for tensorblock/GritLM-7B-GGUF
Base model
GritLM/GritLM-7BDataset used to train tensorblock/GritLM-7B-GGUF
Evaluation results
- accuracy on MTEB AmazonCounterfactualClassification (en)test set self-reported81.179
- ap on MTEB AmazonCounterfactualClassification (en)test set self-reported46.263
- f1 on MTEB AmazonCounterfactualClassification (en)test set self-reported75.446
- accuracy on MTEB AmazonPolarityClassificationtest set self-reported96.516
- ap on MTEB AmazonPolarityClassificationtest set self-reported94.791
- f1 on MTEB AmazonPolarityClassificationtest set self-reported96.515
- accuracy on MTEB AmazonReviewsClassification (en)test set self-reported57.806
- f1 on MTEB AmazonReviewsClassification (en)test set self-reported56.784
- map_at_1 on MTEB ArguAnatest set self-reported38.478
- map_at_10 on MTEB ArguAnatest set self-reported54.955