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Edentns/DataVortexS-10.7B-dpo-v1.10 - GGUF

This repo contains GGUF format model files for Edentns/DataVortexS-10.7B-dpo-v1.10.

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
DataVortexS-10.7B-dpo-v1.10-Q2_K.gguf Q2_K 3.793 GB smallest, significant quality loss - not recommended for most purposes
DataVortexS-10.7B-dpo-v1.10-Q3_K_S.gguf Q3_K_S 4.414 GB very small, high quality loss
DataVortexS-10.7B-dpo-v1.10-Q3_K_M.gguf Q3_K_M 4.909 GB very small, high quality loss
DataVortexS-10.7B-dpo-v1.10-Q3_K_L.gguf Q3_K_L 5.333 GB small, substantial quality loss
DataVortexS-10.7B-dpo-v1.10-Q4_0.gguf Q4_0 5.733 GB legacy; small, very high quality loss - prefer using Q3_K_M
DataVortexS-10.7B-dpo-v1.10-Q4_K_S.gguf Q4_K_S 5.776 GB small, greater quality loss
DataVortexS-10.7B-dpo-v1.10-Q4_K_M.gguf Q4_K_M 6.095 GB medium, balanced quality - recommended
DataVortexS-10.7B-dpo-v1.10-Q5_0.gguf Q5_0 6.974 GB legacy; medium, balanced quality - prefer using Q4_K_M
DataVortexS-10.7B-dpo-v1.10-Q5_K_S.gguf Q5_K_S 6.974 GB large, low quality loss - recommended
DataVortexS-10.7B-dpo-v1.10-Q5_K_M.gguf Q5_K_M 7.160 GB large, very low quality loss - recommended
DataVortexS-10.7B-dpo-v1.10-Q6_K.gguf Q6_K 8.292 GB very large, extremely low quality loss
DataVortexS-10.7B-dpo-v1.10-Q8_0.gguf Q8_0 10.740 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/DataVortexS-10.7B-dpo-v1.10-GGUF --include "DataVortexS-10.7B-dpo-v1.10-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/DataVortexS-10.7B-dpo-v1.10-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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GGUF
Model size
10.9B params
Architecture
llama

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