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tensorblock/piccolo-math-2x7b-GGUF
piccolo-math-2x7b-GGUF is a machine learning model from tensorblock. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
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Downloads · 30 days
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.gguf4.8 GB · 100%
From the Hugging Face model README
This repo contains GGUF format model files for macadeliccc/piccolo-math-2x7b.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| piccolo-math-2x7b-Q2_K.gguf | Q2_K | 4.761 GB | smallest, significant quality loss - not recommended for most purposes |
| piccolo-math-2x7b-Q3_K_S.gguf | Q3_K_S | 5.588 GB | very small, high quality loss |
| piccolo-math-2x7b-Q3_K_M.gguf | Q3_K_M | 6.206 GB | very small, high quality loss |
| piccolo-math-2x7b-Q3_K_L.gguf | Q3_K_L | 6.730 GB | small, substantial quality loss |
| piccolo-math-2x7b-Q4_0.gguf | Q4_0 | 7.281 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| piccolo-math-2x7b-Q4_K_S.gguf | Q4_K_S | 7.342 GB | small, greater quality loss |
| piccolo-math-2x7b-Q4_K_M.gguf | Q4_K_M | 7.783 GB | medium, balanced quality - recommended |
| piccolo-math-2x7b-Q5_0.gguf | Q5_0 | 8.874 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| piccolo-math-2x7b-Q5_K_S.gguf | Q5_K_S | 8.874 GB | large, low quality loss - recommended |
| piccolo-math-2x7b-Q5_K_M.gguf | Q5_K_M | 9.133 GB | large, very low quality loss - recommended |
| piccolo-math-2x7b-Q6_K.gguf | Q6_K | 10.567 GB | very large, extremely low quality loss |
| piccolo-math-2x7b-Q8_0.gguf | Q8_0 | 13.686 GB | very large, extremely low quality loss - not recommended |
Firstly, install Huggingface Client
pip install -U "huggingface_hub[cli]"
Then, downoad the individual model file the a local directory
huggingface-cli download tensorblock/piccolo-math-2x7b-GGUF --include "piccolo-math-2x7b-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/piccolo-math-2x7b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'