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NucleusOrg/Nucleus-1B-alpha-1 - GGUF

This repo contains GGUF format model files for NucleusOrg/Nucleus-1B-alpha-1.

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
Nucleus-1B-alpha-1-Q2_K.gguf Q2_K 0.472 GB smallest, significant quality loss - not recommended for most purposes
Nucleus-1B-alpha-1-Q3_K_S.gguf Q3_K_S 0.540 GB very small, high quality loss
Nucleus-1B-alpha-1-Q3_K_M.gguf Q3_K_M 0.583 GB very small, high quality loss
Nucleus-1B-alpha-1-Q3_K_L.gguf Q3_K_L 0.622 GB small, substantial quality loss
Nucleus-1B-alpha-1-Q4_0.gguf Q4_0 0.673 GB legacy; small, very high quality loss - prefer using Q3_K_M
Nucleus-1B-alpha-1-Q4_K_S.gguf Q4_K_S 0.675 GB small, greater quality loss
Nucleus-1B-alpha-1-Q4_K_M.gguf Q4_K_M 0.705 GB medium, balanced quality - recommended
Nucleus-1B-alpha-1-Q5_0.gguf Q5_0 0.798 GB legacy; medium, balanced quality - prefer using Q4_K_M
Nucleus-1B-alpha-1-Q5_K_S.gguf Q5_K_S 0.798 GB large, low quality loss - recommended
Nucleus-1B-alpha-1-Q5_K_M.gguf Q5_K_M 0.815 GB large, very low quality loss - recommended
Nucleus-1B-alpha-1-Q6_K.gguf Q6_K 0.932 GB very large, extremely low quality loss
Nucleus-1B-alpha-1-Q8_0.gguf Q8_0 1.206 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/Nucleus-1B-alpha-1-GGUF --include "Nucleus-1B-alpha-1-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/Nucleus-1B-alpha-1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
Downloads last month
162
GGUF
Model size
1.13B params
Architecture
llama

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Inference API
Unable to determine this model's library. Check the docs .

Model tree for tensorblock/Nucleus-1B-alpha-1-GGUF

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Dataset used to train tensorblock/Nucleus-1B-alpha-1-GGUF