How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Johnblick187/gemma3:BF16# Run inference directly in the terminal:
llama-cli -hf Johnblick187/gemma3:BF16Use pre-built binary
# 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 Johnblick187/gemma3:BF16# Run inference directly in the terminal:
./llama-cli -hf Johnblick187/gemma3:BF16Build from source code
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 Johnblick187/gemma3:BF16# Run inference directly in the terminal:
./build/bin/llama-cli -hf Johnblick187/gemma3:BF16Use Docker
docker model run hf.co/Johnblick187/gemma3:BF16Quick Links
Gemma 3 27B Obliterated Q80 : GGUF
This model was finetuned and converted to GGUF format using Unsloth.
Example usage:
- For text only LLMs:
llama-cli -hf Johnblick187/gemma3 --jinja - For multimodal models:
llama-mtmd-cli -hf Johnblick187/gemma3 --jinja
Available Model files:
gemma-3-27b-it-abliterated.Q8_0.ggufgemma-3-27b-it-abliterated.BF16-mmproj.ggufgemma-3-27b-it-abliterated.BF16-00002-of-00002.gguf
Note
The model's BOS token behavior was adjusted for GGUF compatibility.
This was trained 2x faster with Unsloth
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Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf Johnblick187/gemma3:BF16# Run inference directly in the terminal: llama-cli -hf Johnblick187/gemma3:BF16