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state-spaces/mamba-790m-hf - GGUF

This repo contains GGUF format model files for state-spaces/mamba-790m-hf.

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

Prompt template


Model file specification

Filename Quant type File Size Description
mamba-790m-hf-Q2_K.gguf Q2_K 0.294 GB smallest, significant quality loss - not recommended for most purposes
mamba-790m-hf-Q3_K_S.gguf Q3_K_S 0.360 GB very small, high quality loss
mamba-790m-hf-Q3_K_M.gguf Q3_K_M 0.360 GB very small, high quality loss
mamba-790m-hf-Q3_K_L.gguf Q3_K_L 0.360 GB small, substantial quality loss
mamba-790m-hf-Q4_0.gguf Q4_0 0.447 GB legacy; small, very high quality loss - prefer using Q3_K_M
mamba-790m-hf-Q4_K_S.gguf Q4_K_S 0.448 GB small, greater quality loss
mamba-790m-hf-Q4_K_M.gguf Q4_K_M 0.448 GB medium, balanced quality - recommended
mamba-790m-hf-Q5_0.gguf Q5_0 0.530 GB legacy; medium, balanced quality - prefer using Q4_K_M
mamba-790m-hf-Q5_K_S.gguf Q5_K_S 0.531 GB large, low quality loss - recommended
mamba-790m-hf-Q5_K_M.gguf Q5_K_M 0.531 GB large, very low quality loss - recommended
mamba-790m-hf-Q6_K.gguf Q6_K 0.621 GB very large, extremely low quality loss
mamba-790m-hf-Q8_0.gguf Q8_0 0.796 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/mamba-790m-hf-GGUF --include "mamba-790m-hf-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/mamba-790m-hf-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
Downloads last month
4
GGUF
Model size
793M params
Architecture
mamba

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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 pipeline type.

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