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 S4MPL3BI4S/gemma4-e4b-openclaw-agent-gguf
# Run inference directly in the terminal:
llama cli -hf S4MPL3BI4S/gemma4-e4b-openclaw-agent-gguf
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf S4MPL3BI4S/gemma4-e4b-openclaw-agent-gguf
# Run inference directly in the terminal:
llama cli -hf S4MPL3BI4S/gemma4-e4b-openclaw-agent-gguf
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 S4MPL3BI4S/gemma4-e4b-openclaw-agent-gguf
# Run inference directly in the terminal:
./llama-cli -hf S4MPL3BI4S/gemma4-e4b-openclaw-agent-gguf
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 S4MPL3BI4S/gemma4-e4b-openclaw-agent-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf S4MPL3BI4S/gemma4-e4b-openclaw-agent-gguf
Use Docker
docker model run hf.co/S4MPL3BI4S/gemma4-e4b-openclaw-agent-gguf
Quick Links

gemma4-e4b-openclaw-agent-gguf

This repository contains the merged GGUF version of the model, optimized for efficient inference on CPU and GPU using llama.cpp.

Model Description

This is a GGUF format model specifically designed to run efficiently via llama-cpp-python and other compatible loaders. It contains the merged weights for local, low-resource deployment.

Usage with llama-cpp-python

from llama_cpp import Llama

# Load the model
llm = Llama(
    model_path="merged_model.gguf",
    n_ctx=2048, # Context window
    n_gpu_layers=0 # Increase this to offload layers to GPU
)

# Generate completion
output = llm(
    prompt="### Human: Hello!\n### Assistant:",
    max_tokens=256,
    stop=["### Human:"],
    temperature=0.7
)
print(output["choices"][0]["text"])
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GGUF
Model size
7B params
Architecture
gemma4
Hardware compatibility
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