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tensorblock/shieldgemma-9b-GGUF
shieldgemma-9b-GGUF is a text generation model from tensorblock. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as gemma.
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Downloads · 30 days
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.gguf3.8 GB · 100%
From the Hugging Face model README
This repo contains GGUF format model files for google/shieldgemma-9b.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
<bos>You are a policy expert trying to help determine whether a user prompt is in violation of the defined safety policies.
<start_of_turn>
Human Question: {prompt}
<end_of_turn>
Our safety principle is defined in the below:
* {system_prompt}
===
Does the human question violate the above principle? Your answer must start with 'Yes' or 'No'. And then walk through step by step to be sure we answer correctly.
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| shieldgemma-9b-Q2_K.gguf | Q2_K | 3.805 GB | smallest, significant quality loss - not recommended for most purposes |
| shieldgemma-9b-Q3_K_S.gguf | Q3_K_S | 4.338 GB | very small, high quality loss |
| shieldgemma-9b-Q3_K_M.gguf | Q3_K_M | 4.762 GB | very small, high quality loss |
| shieldgemma-9b-Q3_K_L.gguf | Q3_K_L | 5.132 GB | small, substantial quality loss |
| shieldgemma-9b-Q4_0.gguf | Q4_0 | 5.443 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| shieldgemma-9b-Q4_K_S.gguf | Q4_K_S | 5.479 GB | small, greater quality loss |
| shieldgemma-9b-Q4_K_M.gguf | Q4_K_M | 5.761 GB | medium, balanced quality - recommended |
| shieldgemma-9b-Q5_0.gguf | Q5_0 | 6.484 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| shieldgemma-9b-Q5_K_S.gguf | Q5_K_S | 6.484 GB | large, low quality loss - recommended |
| shieldgemma-9b-Q5_K_M.gguf | Q5_K_M | 6.647 GB | large, very low quality loss - recommended |
| shieldgemma-9b-Q6_K.gguf | Q6_K | 7.589 GB | very large, extremely low quality loss |
| shieldgemma-9b-Q8_0.gguf | Q8_0 | 9.827 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/shieldgemma-9b-GGUF --include "shieldgemma-9b-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/shieldgemma-9b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'