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h2oai/h2o-danube3-500m-chat - GGUF

This repo contains GGUF format model files for h2oai/h2o-danube3-500m-chat.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

Prompt template

<|prompt|>{prompt}</s><|answer|>

Model file specification

Filename Quant type File Size Description
h2o-danube3-500m-chat-Q2_K.gguf Q2_K 0.195 GB smallest, significant quality loss - not recommended for most purposes
h2o-danube3-500m-chat-Q3_K_S.gguf Q3_K_S 0.224 GB very small, high quality loss
h2o-danube3-500m-chat-Q3_K_M.gguf Q3_K_M 0.245 GB very small, high quality loss
h2o-danube3-500m-chat-Q3_K_L.gguf Q3_K_L 0.262 GB small, substantial quality loss
h2o-danube3-500m-chat-Q4_0.gguf Q4_0 0.282 GB legacy; small, very high quality loss - prefer using Q3_K_M
h2o-danube3-500m-chat-Q4_K_S.gguf Q4_K_S 0.284 GB small, greater quality loss
h2o-danube3-500m-chat-Q4_K_M.gguf Q4_K_M 0.296 GB medium, balanced quality - recommended
h2o-danube3-500m-chat-Q5_0.gguf Q5_0 0.336 GB legacy; medium, balanced quality - prefer using Q4_K_M
h2o-danube3-500m-chat-Q5_K_S.gguf Q5_K_S 0.336 GB large, low quality loss - recommended
h2o-danube3-500m-chat-Q5_K_M.gguf Q5_K_M 0.343 GB large, very low quality loss - recommended
h2o-danube3-500m-chat-Q6_K.gguf Q6_K 0.393 GB very large, extremely low quality loss
h2o-danube3-500m-chat-Q8_0.gguf Q8_0 0.509 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/h2o-danube3-500m-chat-GGUF --include "h2o-danube3-500m-chat-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/h2o-danube3-500m-chat-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
514M params
Architecture
llama

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