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DopeorNope/SOLARC-MOE-10.7Bx4 - GGUF

This repo contains GGUF format model files for DopeorNope/SOLARC-MOE-10.7Bx4.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.

Prompt template

### System:
{system_prompt}

### User:
{prompt}

### Assistant:

Model file specification

Filename Quant type File Size Description
SOLARC-MOE-10.7Bx4-Q2_K.gguf Q2_K 13.189 GB smallest, significant quality loss - not recommended for most purposes
SOLARC-MOE-10.7Bx4-Q3_K_S.gguf Q3_K_S 15.568 GB very small, high quality loss
SOLARC-MOE-10.7Bx4-Q3_K_M.gguf Q3_K_M 17.288 GB very small, high quality loss
SOLARC-MOE-10.7Bx4-Q3_K_L.gguf Q3_K_L 18.734 GB small, substantial quality loss
SOLARC-MOE-10.7Bx4-Q4_0.gguf Q4_0 20.345 GB legacy; small, very high quality loss - prefer using Q3_K_M
SOLARC-MOE-10.7Bx4-Q4_K_S.gguf Q4_K_S 20.523 GB small, greater quality loss
SOLARC-MOE-10.7Bx4-Q4_K_M.gguf Q4_K_M 21.824 GB medium, balanced quality - recommended
SOLARC-MOE-10.7Bx4-Q5_0.gguf Q5_0 24.840 GB legacy; medium, balanced quality - prefer using Q4_K_M
SOLARC-MOE-10.7Bx4-Q5_K_S.gguf Q5_K_S 24.840 GB large, low quality loss - recommended
SOLARC-MOE-10.7Bx4-Q5_K_M.gguf Q5_K_M 25.603 GB large, very low quality loss - recommended
SOLARC-MOE-10.7Bx4-Q6_K.gguf Q6_K 29.617 GB very large, extremely low quality loss
SOLARC-MOE-10.7Bx4-Q8_0.gguf Q8_0 38.360 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/SOLARC-MOE-10.7Bx4-GGUF --include "SOLARC-MOE-10.7Bx4-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/SOLARC-MOE-10.7Bx4-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
Downloads last month
159
GGUF
Model size
36.1B params
Architecture
llama

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