How to use from
llama.cpp
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf idkwhattoputherenow/gemma-4-E4B-it-qat-q4_0-maxerr:Q4_0
# Run inference directly in the terminal:
llama cli -hf idkwhattoputherenow/gemma-4-E4B-it-qat-q4_0-maxerr:Q4_0
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf idkwhattoputherenow/gemma-4-E4B-it-qat-q4_0-maxerr:Q4_0
# Run inference directly in the terminal:
llama cli -hf idkwhattoputherenow/gemma-4-E4B-it-qat-q4_0-maxerr:Q4_0
Use 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 idkwhattoputherenow/gemma-4-E4B-it-qat-q4_0-maxerr:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf idkwhattoputherenow/gemma-4-E4B-it-qat-q4_0-maxerr:Q4_0
Build 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 idkwhattoputherenow/gemma-4-E4B-it-qat-q4_0-maxerr:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf idkwhattoputherenow/gemma-4-E4B-it-qat-q4_0-maxerr:Q4_0
Use Docker
docker model run hf.co/idkwhattoputherenow/gemma-4-E4B-it-qat-q4_0-maxerr:Q4_0
Quick Links

maxerr quant of gemma-4-E4B qat

E4B                                                         Mean KLD     Same Top%    RMS Δp%       95% KLD
----                                                        --------     ---------     -------       -------
original maxerr Q4_0 vs original F32                        0.001168       98.553%      0.878%      0.003391
unsloth Q4_K_XL vs original F32                             0.001194       98.508%      0.881%      0.003423
google Q4_0 vs original F32                                 0.037809       90.928%      4.611%      0.125722
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Model size
7B params
Architecture
gemma4
Hardware compatibility
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