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metadata
license: gemma
library_name: transformers
pipeline_tag: text-generation
extra_gated_heading: Access Gemma on Hugging Face
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  To access Gemma on Hugging Face, you’re required to review and agree to
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base_model: google/gemma-3-1b-it
tags:
  - TensorBlock
  - GGUF
TensorBlock

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google/gemma-3-1b-it - GGUF

This repo contains GGUF format model files for google/gemma-3-1b-it.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b5165.

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Prompt template

<bos><start_of_turn>user
{system_prompt}

{prompt}<end_of_turn>
<start_of_turn>model

Model file specification

Filename Quant type File Size Description
gemma-3-1b-it-Q2_K.gguf Q2_K 0.690 GB smallest, significant quality loss - not recommended for most purposes
gemma-3-1b-it-Q3_K_S.gguf Q3_K_S 0.689 GB very small, high quality loss
gemma-3-1b-it-Q3_K_M.gguf Q3_K_M 0.722 GB very small, high quality loss
gemma-3-1b-it-Q3_K_L.gguf Q3_K_L 0.752 GB small, substantial quality loss
gemma-3-1b-it-Q4_0.gguf Q4_0 0.720 GB legacy; small, very high quality loss - prefer using Q3_K_M
gemma-3-1b-it-Q4_K_S.gguf Q4_K_S 0.781 GB small, greater quality loss
gemma-3-1b-it-Q4_K_M.gguf Q4_K_M 0.806 GB medium, balanced quality - recommended
gemma-3-1b-it-Q5_0.gguf Q5_0 0.808 GB legacy; medium, balanced quality - prefer using Q4_K_M
gemma-3-1b-it-Q5_K_S.gguf Q5_K_S 0.836 GB large, low quality loss - recommended
gemma-3-1b-it-Q5_K_M.gguf Q5_K_M 0.851 GB large, very low quality loss - recommended
gemma-3-1b-it-Q6_K.gguf Q6_K 1.012 GB very large, extremely low quality loss
gemma-3-1b-it-Q8_0.gguf Q8_0 1.069 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/google_gemma-3-1b-it-GGUF --include "gemma-3-1b-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/google_gemma-3-1b-it-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'