Gemma 3
Collection
Collection of quants for Google's Gemma 3 • 3 items • Updated
How to use lex-au/Google.Gemma-3-1b-it-GGUF with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf lex-au/Google.Gemma-3-1b-it-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf lex-au/Google.Gemma-3-1b-it-GGUF:Q2_K
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf lex-au/Google.Gemma-3-1b-it-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf lex-au/Google.Gemma-3-1b-it-GGUF:Q2_K
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf lex-au/Google.Gemma-3-1b-it-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf lex-au/Google.Gemma-3-1b-it-GGUF:Q2_K
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf lex-au/Google.Gemma-3-1b-it-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf lex-au/Google.Gemma-3-1b-it-GGUF:Q2_K
docker model run hf.co/lex-au/Google.Gemma-3-1b-it-GGUF:Q2_K
How to use lex-au/Google.Gemma-3-1b-it-GGUF with Ollama:
ollama run hf.co/lex-au/Google.Gemma-3-1b-it-GGUF:Q2_K
How to use lex-au/Google.Gemma-3-1b-it-GGUF with Docker Model Runner:
docker model run hf.co/lex-au/Google.Gemma-3-1b-it-GGUF:Q2_K
How to use lex-au/Google.Gemma-3-1b-it-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull lex-au/Google.Gemma-3-1b-it-GGUF:Q2_K
lemonade run user.Google.Gemma-3-1b-it-GGUF-Q2_K
lemonade list
This repository contains GGUF quantized versions of Google's Gemma 3 1B instruction-tuned model, optimized for efficient deployment across various hardware configurations.
| Model | Size | Compression Ratio | Size Reduction |
|---|---|---|---|
| Q8_0 | 1.07 GB | 54% | 46% |
| Q6_K | 1.01 GB | 51% | 49% |
| Q4_K | 0.81 GB | 40% | 60% |
| Q2_K | 0.69 GB | 34% | 66% |
These models can be used with llama.cpp and its various interfaces. Example:
# Running with llama-gemma3-cli.exe (adjust paths as needed)
./llama-gemma3-cli --model Google.Gemma-3-1b-it-Q4_K.gguf --ctx-size 4096 --temp 0.7 --prompt "Write a short story about a robot who discovers it has feelings."
This model is released under the same Gemma license as the original model.
This quantized set is derived from Google's Gemma 3 1B instruction-tuned model.
@article{gemma_2025,
title={Gemma 3},
url={https://goo.gle/Gemma3Report},
publisher={Kaggle},
author={Gemma Team},
year={2025}
}
@misc{gemma3_quantization_2025,
title={Quantized Versions of Google's Gemma 3 1B Model},
author={Lex-au},
year={2025},
month={March},
note={Quantized models (Q8_0, Q6_K, Q4_K, Q2_K) derived from Google's Gemma 3 1B},
url={https://huggingface.co/lex-au}
}
2-bit
6-bit
8-bit
docker model run hf.co/lex-au/Google.Gemma-3-1b-it-GGUF: