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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.
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tags:
  - conversational
  - TensorBlock
  - GGUF
base_model: google/datagemma-rag-27b-it
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google/datagemma-rag-27b-it - GGUF

This repo contains GGUF format model files for google/datagemma-rag-27b-it.

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

Prompt template 

<bos><start_of_turn>user
{prompt}<end_of_turn>
<start_of_turn>model

Model file specification

Filename Quant type File Size Description
datagemma-rag-27b-it-Q2_K.gguf Q2_K 10.450 GB smallest, significant quality loss - not recommended for most purposes
datagemma-rag-27b-it-Q3_K_S.gguf Q3_K_S 12.169 GB very small, high quality loss
datagemma-rag-27b-it-Q3_K_M.gguf Q3_K_M 13.425 GB very small, high quality loss
datagemma-rag-27b-it-Q3_K_L.gguf Q3_K_L 14.519 GB small, substantial quality loss
datagemma-rag-27b-it-Q4_0.gguf Q4_0 15.628 GB legacy; small, very high quality loss - prefer using Q3_K_M
datagemma-rag-27b-it-Q4_K_S.gguf Q4_K_S 15.739 GB small, greater quality loss
datagemma-rag-27b-it-Q4_K_M.gguf Q4_K_M 16.645 GB medium, balanced quality - recommended
datagemma-rag-27b-it-Q5_0.gguf Q5_0 18.884 GB legacy; medium, balanced quality - prefer using Q4_K_M
datagemma-rag-27b-it-Q5_K_S.gguf Q5_K_S 18.884 GB large, low quality loss - recommended
datagemma-rag-27b-it-Q5_K_M.gguf Q5_K_M 19.408 GB large, very low quality loss - recommended
datagemma-rag-27b-it-Q6_K.gguf Q6_K 22.344 GB very large, extremely low quality loss
datagemma-rag-27b-it-Q8_0.gguf Q8_0 28.937 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/datagemma-rag-27b-it-GGUF --include "datagemma-rag-27b-it-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/datagemma-rag-27b-it-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'