Instructions to use FrontisAI/Frontis-MA1-30B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use FrontisAI/Frontis-MA1-30B-GGUF 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 FrontisAI/Frontis-MA1-30B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf FrontisAI/Frontis-MA1-30B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FrontisAI/Frontis-MA1-30B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf FrontisAI/Frontis-MA1-30B-GGUF:Q4_K_M

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 FrontisAI/Frontis-MA1-30B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf FrontisAI/Frontis-MA1-30B-GGUF:Q4_K_M

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 FrontisAI/Frontis-MA1-30B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FrontisAI/Frontis-MA1-30B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/FrontisAI/Frontis-MA1-30B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use FrontisAI/Frontis-MA1-30B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FrontisAI/Frontis-MA1-30B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FrontisAI/Frontis-MA1-30B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FrontisAI/Frontis-MA1-30B-GGUF:Q4_K_M

Ollama
How to use FrontisAI/Frontis-MA1-30B-GGUF with Ollama:
```
ollama run hf.co/FrontisAI/Frontis-MA1-30B-GGUF:Q4_K_M
```

Unsloth Studio

How to use FrontisAI/Frontis-MA1-30B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for FrontisAI/Frontis-MA1-30B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for FrontisAI/Frontis-MA1-30B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FrontisAI/Frontis-MA1-30B-GGUF to start chatting

How to use FrontisAI/Frontis-MA1-30B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FrontisAI/Frontis-MA1-30B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "FrontisAI/Frontis-MA1-30B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use FrontisAI/Frontis-MA1-30B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FrontisAI/Frontis-MA1-30B-GGUF:Q4_K_M

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 FrontisAI/Frontis-MA1-30B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use FrontisAI/Frontis-MA1-30B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FrontisAI/Frontis-MA1-30B-GGUF:Q4_K_M

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 "FrontisAI/Frontis-MA1-30B-GGUF:Q4_K_M" \
  --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"

Docker Model Runner
How to use FrontisAI/Frontis-MA1-30B-GGUF with Docker Model Runner:
```
docker model run hf.co/FrontisAI/Frontis-MA1-30B-GGUF:Q4_K_M
```

Lemonade

How to use FrontisAI/Frontis-MA1-30B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FrontisAI/Frontis-MA1-30B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Frontis-MA1-30B-GGUF-Q4_K_M

List all available models

lemonade list

Frontis-MA1-30B-GGUF

📄 Paper • 🌐 Project • 💻 Code • 🤗 Models • 🧩 Tasks • 📚 SFT Traces

This repository is the official Q4_K_M GGUF derivative of Frontis-MA1-30B. It is provided for practical local inference with llama.cpp.

It accompanies the paper Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering and the OpenRSI code release.

Files

File	Size	Purpose
`Frontis-MA1-30B-Q4_K_M.gguf`	17.28 GiB	Q4_K_M language model
`checksums.txt`	—	SHA-256 integrity manifest

Only one quantization is published intentionally. Q4_K_M is the default local-deployment format for this release; the canonical BF16 Transformers weights remain in the base repository.

Quickstart

Tested conversion and inference tool: llama.cpp b9637, commit aedb2a5e9ca3d4064148bbb919e0ddc0c1b70ab3.

llama-cli \
  -m ./Frontis-MA1-30B-Q4_K_M.gguf \
  -ngl all \
  -c 32768 \
  -n 1024 \
  -cnv -st --simple-io \
  -p "Build a strong tabular classification baseline and explain the validation design."

Reduce -c when memory is limited. On systems that cannot offload all layers, set -ngl to a smaller value or let llama.cpp choose automatically.

The bundled chat template always begins an explicit thinking block. Allow sufficient -n budget for the model to finish thinking and produce its final answer.

Release validation

The final file passed SHA-256 verification, a full 579-tensor GGUF structure read, and a real llama-cli load-and-generate smoke with full GPU offload on one NVIDIA H200. This validates the release artifact and command path; it is not a consumer-hardware speed benchmark.

Scope and quality

This is a lossy 4-bit quantization. Use the BF16 repository when maximum fidelity or paper-result reproduction is required.
The paper's reported scores were obtained with the canonical model and the OpenMLE-Evo harness; they are not GGUF one-shot benchmark results.
Generated code may be incorrect or unsafe. Execute it only in an isolated environment with explicit resource limits.

Paper result

The canonical BF16 model reaches 53.03% Medal Average and 0.7055 Human Rank with OpenMLE-Evo on the official 22-task MLE-Bench Lite split, compared with 34.85% and 0.5573 for its base model under the same harness. These are BF16 model–harness results, not GGUF one-shot scores.

License

Original Frontis-MA1 material is released under CC BY-NC 4.0 for attribution-required, non-commercial use. Commercial use is not granted. The upstream Qwen Apache License 2.0 notice is preserved in LICENSE-UPSTREAM-APACHE-2.0 and NOTICE.