Instructions to use SageStack01/go2-ft-04 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use SageStack01/go2-ft-04 with 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 SageStack01/go2-ft-04
# Run inference directly in the terminal:
llama cli -hf SageStack01/go2-ft-04

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SageStack01/go2-ft-04
# Run inference directly in the terminal:
llama cli -hf SageStack01/go2-ft-04

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 SageStack01/go2-ft-04
# Run inference directly in the terminal:
./llama-cli -hf SageStack01/go2-ft-04

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 SageStack01/go2-ft-04
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SageStack01/go2-ft-04

Use Docker

docker model run hf.co/SageStack01/go2-ft-04

Llama new
LM Studio
Jan
Ollama
How to use SageStack01/go2-ft-04 with Ollama:
```
ollama run hf.co/SageStack01/go2-ft-04
```
Unsloth Desktop

How to use SageStack01/go2-ft-04 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SageStack01/go2-ft-04

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "SageStack01/go2-ft-04"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use SageStack01/go2-ft-04 with Docker Model Runner:
```
docker model run hf.co/SageStack01/go2-ft-04
```

Lemonade

How to use SageStack01/go2-ft-04 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SageStack01/go2-ft-04

Run and chat with the model

lemonade run user.go2-ft-04-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use SageStack01/go2-ft-04 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SageStack01/go2-ft-04

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default SageStack01/go2-ft-04

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use SageStack01/go2-ft-04 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SageStack01/go2-ft-04

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "SageStack01/go2-ft-04" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

go2-ft-04

A fine-tune of Google's Gemma 4 E2B IT that controls a Unitree Go2 robot dog through tool calls: it walks, turns, sits, does tricks, looks around and finds objects when you ask in plain English, and explains what happened. Made by AUT Project 661 (LLM Agents for Robotics).

It's the language model of the command console in go2-mujoco-sim, which downloads this file on its first start, checks its SHA-256 and runs it with a private Ollama process. Every tool call the model makes passes the console's safety executor (per-call limits and LiDAR clearance checks) before the robot moves.


File	`go2-ft-04.gguf`, Q4_K_M, 3,416,119,840 bytes
SHA-256	`15a875b610d8640e4d004d71e3888ba73a7647c4eddf9b91b4df95929498293d`
Base model	google/gemma-4-E2B-it (LoRA fine-tune, merged, then quantized to 4-bit)
Prompt and tools	The compact system prompt and tool definitions in the repo (`go2/agent.py`, `go2/tools.py`)
Result	182 of 186 (98%) on the project's tool-calling benchmark

Intended use

Research and teaching with the Go2 in simulation, and supervised tests on a real Go2 EDU through the console. Not approved for production, and not for driving a robot without the console's safety executor, a supervisor and an emergency stop.