Text Generation
Transformers
GGUF
Korean
TensorBlock
GGUF
Inference Endpoints
TensorBlock

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HumanF-MarkrAI/COKAL-DPO-13b-v2 - GGUF

This repo contains GGUF format model files for HumanF-MarkrAI/COKAL-DPO-13b-v2.

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

Prompt template


Model file specification

Filename Quant type File Size Description
COKAL-DPO-13b-v2-Q2_K.gguf Q2_K 4.939 GB smallest, significant quality loss - not recommended for most purposes
COKAL-DPO-13b-v2-Q3_K_S.gguf Q3_K_S 5.751 GB very small, high quality loss
COKAL-DPO-13b-v2-Q3_K_M.gguf Q3_K_M 6.430 GB very small, high quality loss
COKAL-DPO-13b-v2-Q3_K_L.gguf Q3_K_L 7.022 GB small, substantial quality loss
COKAL-DPO-13b-v2-Q4_0.gguf Q4_0 7.468 GB legacy; small, very high quality loss - prefer using Q3_K_M
COKAL-DPO-13b-v2-Q4_K_S.gguf Q4_K_S 7.525 GB small, greater quality loss
COKAL-DPO-13b-v2-Q4_K_M.gguf Q4_K_M 7.968 GB medium, balanced quality - recommended
COKAL-DPO-13b-v2-Q5_0.gguf Q5_0 9.083 GB legacy; medium, balanced quality - prefer using Q4_K_M
COKAL-DPO-13b-v2-Q5_K_S.gguf Q5_K_S 9.083 GB large, low quality loss - recommended
COKAL-DPO-13b-v2-Q5_K_M.gguf Q5_K_M 9.341 GB large, very low quality loss - recommended
COKAL-DPO-13b-v2-Q6_K.gguf Q6_K 10.800 GB very large, extremely low quality loss
COKAL-DPO-13b-v2-Q8_0.gguf Q8_0 13.988 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/COKAL-DPO-13b-v2-GGUF --include "COKAL-DPO-13b-v2-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/COKAL-DPO-13b-v2-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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GGUF
Model size
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Architecture
llama

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This model is not currently available via any of the supported third-party Inference Providers, and the model is not deployed on the HF Inference API.

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