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tensorblock/Sakura-SOLRCA-Math-Instruct-DPO-v1-GGUF
Sakura-SOLRCA-Math-Instruct-DPO-v1-GGUF is a text generation model from tensorblock. Use it when you need the model to write or continue text. The card lists the license as cc-by-nc-sa-4.0.
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Downloads ยท 30 days
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.gguf9.2 GB ยท 100%
From the Hugging Face model README
This repo contains GGUF format model files for kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v1.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
### System:
{system_prompt}
### User:
{prompt}
### Assistant:
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| Sakura-SOLRCA-Math-Instruct-DPO-v1-Q2_K.gguf | Q2_K | 3.728 GB | smallest, significant quality loss - not recommended for most purposes |
| Sakura-SOLRCA-Math-Instruct-DPO-v1-Q3_K_S.gguf | Q3_K_S | 4.344 GB | very small, high quality loss |
| Sakura-SOLRCA-Math-Instruct-DPO-v1-Q3_K_M.gguf | Q3_K_M | 4.839 GB | very small, high quality loss |
| Sakura-SOLRCA-Math-Instruct-DPO-v1-Q3_K_L.gguf | Q3_K_L | 5.263 GB | small, substantial quality loss |
| Sakura-SOLRCA-Math-Instruct-DPO-v1-Q4_0.gguf | Q4_0 | 5.655 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| Sakura-SOLRCA-Math-Instruct-DPO-v1-Q4_K_S.gguf | Q4_K_S | 5.698 GB | small, greater quality loss |
| Sakura-SOLRCA-Math-Instruct-DPO-v1-Q4_K_M.gguf | Q4_K_M | 6.018 GB | medium, balanced quality - recommended |
| Sakura-SOLRCA-Math-Instruct-DPO-v1-Q5_0.gguf | Q5_0 | 6.889 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| Sakura-SOLRCA-Math-Instruct-DPO-v1-Q5_K_S.gguf | Q5_K_S | 6.889 GB | large, low quality loss - recommended |
| Sakura-SOLRCA-Math-Instruct-DPO-v1-Q5_K_M.gguf | Q5_K_M | 7.076 GB | large, very low quality loss - recommended |
| Sakura-SOLRCA-Math-Instruct-DPO-v1-Q6_K.gguf | Q6_K | 8.200 GB | very large, extremely low quality loss |
| Sakura-SOLRCA-Math-Instruct-DPO-v1-Q8_0.gguf | Q8_0 | 10.621 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/Sakura-SOLRCA-Math-Instruct-DPO-v1-GGUF --include "Sakura-SOLRCA-Math-Instruct-DPO-v1-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/Sakura-SOLRCA-Math-Instruct-DPO-v1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'