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nakodanei/Blue-Orchid-2x7b - GGUF

This repo contains GGUF format model files for nakodanei/Blue-Orchid-2x7b.

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
Blue-Orchid-2x7b-Q2_K.gguf Q2_K 4.761 GB smallest, significant quality loss - not recommended for most purposes
Blue-Orchid-2x7b-Q3_K_S.gguf Q3_K_S 5.588 GB very small, high quality loss
Blue-Orchid-2x7b-Q3_K_M.gguf Q3_K_M 6.206 GB very small, high quality loss
Blue-Orchid-2x7b-Q3_K_L.gguf Q3_K_L 6.730 GB small, substantial quality loss
Blue-Orchid-2x7b-Q4_0.gguf Q4_0 7.281 GB legacy; small, very high quality loss - prefer using Q3_K_M
Blue-Orchid-2x7b-Q4_K_S.gguf Q4_K_S 7.342 GB small, greater quality loss
Blue-Orchid-2x7b-Q4_K_M.gguf Q4_K_M 7.783 GB medium, balanced quality - recommended
Blue-Orchid-2x7b-Q5_0.gguf Q5_0 8.874 GB legacy; medium, balanced quality - prefer using Q4_K_M
Blue-Orchid-2x7b-Q5_K_S.gguf Q5_K_S 8.874 GB large, low quality loss - recommended
Blue-Orchid-2x7b-Q5_K_M.gguf Q5_K_M 9.133 GB large, very low quality loss - recommended
Blue-Orchid-2x7b-Q6_K.gguf Q6_K 10.567 GB very large, extremely low quality loss
Blue-Orchid-2x7b-Q8_0.gguf Q8_0 13.686 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/Blue-Orchid-2x7b-GGUF --include "Blue-Orchid-2x7b-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/Blue-Orchid-2x7b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
Downloads last month
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GGUF
Model size
12.9B params
Architecture
llama

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Inference Providers NEW
This model is not currently available via any of the supported third-party Inference Providers, and HF Inference API was unable to determine this model's library.

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