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metadata
license: gemma
library_name: transformers
pipeline_tag: text-generation
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: >-
  To access Gemma on Hugging Face, you’re required to review and agree to
  Google’s usage license. To do this, please ensure you’re logged in to Hugging
  Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: google/gemma-2-9b
tags:
  - TensorBlock
  - GGUF
TensorBlock

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google/gemma-2-9b - GGUF

This repo contains GGUF format model files for google/gemma-2-9b.

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
gemma-2-9b-Q2_K.gguf Q2_K 3.805 GB smallest, significant quality loss - not recommended for most purposes
gemma-2-9b-Q3_K_S.gguf Q3_K_S 4.338 GB very small, high quality loss
gemma-2-9b-Q3_K_M.gguf Q3_K_M 4.762 GB very small, high quality loss
gemma-2-9b-Q3_K_L.gguf Q3_K_L 5.132 GB small, substantial quality loss
gemma-2-9b-Q4_0.gguf Q4_0 5.443 GB legacy; small, very high quality loss - prefer using Q3_K_M
gemma-2-9b-Q4_K_S.gguf Q4_K_S 5.479 GB small, greater quality loss
gemma-2-9b-Q4_K_M.gguf Q4_K_M 5.761 GB medium, balanced quality - recommended
gemma-2-9b-Q5_0.gguf Q5_0 6.484 GB legacy; medium, balanced quality - prefer using Q4_K_M
gemma-2-9b-Q5_K_S.gguf Q5_K_S 6.484 GB large, low quality loss - recommended
gemma-2-9b-Q5_K_M.gguf Q5_K_M 6.647 GB large, very low quality loss - recommended
gemma-2-9b-Q6_K.gguf Q6_K 7.589 GB very large, extremely low quality loss
gemma-2-9b-Q8_0.gguf Q8_0 9.827 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/gemma-2-9b-GGUF --include "gemma-2-9b-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/gemma-2-9b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'