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 sridharkkaruppusamy/beastcli:
# Run inference directly in the terminal:
llama cli -hf sridharkkaruppusamy/beastcli:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sridharkkaruppusamy/beastcli:
# Run inference directly in the terminal:
llama cli -hf sridharkkaruppusamy/beastcli:
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 sridharkkaruppusamy/beastcli:
# Run inference directly in the terminal:
./llama-cli -hf sridharkkaruppusamy/beastcli:
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 sridharkkaruppusamy/beastcli:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sridharkkaruppusamy/beastcli:
Use Docker
docker model run hf.co/sridharkkaruppusamy/beastcli:
Quick Links

BeastCLI - Fine-tuned Gemma-4-E4B-it

Fine-tuned Gemma 4 E4B model via Unsloth Studio.

Quantization

  • Q4_K_M (~5GB) - Recommended for most use cases
  • BF16-mmproj (~1GB) - Multimodal projector

Ollama Usage

ollama run sridharkkaruppusamy/beastcli:Q4_K_M
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