TiTan-Gemma3-0.27B-f16.gguf (837.7 MB) - 16-bit float (original precision, largest file)
TiTan-Gemma3-0.27B-q3_k_m.gguf (320.8 MB) - 3-bit quantization (medium quality)
TiTan-Gemma3-0.27B-q4_k_m.gguf (351.4 MB) - 4-bit quantization (medium, recommended for most use cases)
TiTan-Gemma3-0.27B-q5_k_m.gguf (368.0 MB) - 5-bit quantization (medium, good quality)
TiTan-Gemma3-0.27B-q6_k.gguf (439.9 MB) - 6-bit quantization (high quality)
TiTan-Gemma3-0.27B-q8_0.gguf (448.0 MB) - 8-bit quantization (very high quality)

Usage

With llama.cpp

bash

# Download recommended quantization
wget https://huggingface.co/theprint/TiTan-Gemma3-0.27B-GGUF/resolve/main/TiTan-Gemma3-0.27B-q4_k_m.gguf

# Run inference
./llama.cpp/main -m TiTan-Gemma3-0.27B-q4_k_m.gguf \
  -p "Your prompt here" \
  -n 256 \
  --temp 0.7 \
  --top-p 0.9

With other GGUF tools

These files are compatible with:

llama.cpp
Ollama (import as custom model)
KoboldCpp
text-generation-webui

Quantization Info

Recommended: q4_k_m provides the best balance of size, speed, and quality for most use cases.

For maximum quality: Use q8_0 or f16
For maximum speed/smallest size: Use q3_k_m or q4_k_s

License

apache-2.0

Citation

bibtex

@misc{titan_gemma3_0.27b_gguf,
  title={TiTan-Gemma3-0.27B GGUF Quantized Models},
  author={theprint},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/theprint/TiTan-Gemma3-0.27B-GGUF}
}