Instructions to use csoai/sov-gate-ft2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use csoai/sov-gate-ft2 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 csoai/sov-gate-ft2:F16
# Run inference directly in the terminal:
llama cli -hf csoai/sov-gate-ft2:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf csoai/sov-gate-ft2:F16
# Run inference directly in the terminal:
llama cli -hf csoai/sov-gate-ft2:F16

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 csoai/sov-gate-ft2:F16
# Run inference directly in the terminal:
./llama-cli -hf csoai/sov-gate-ft2:F16

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 csoai/sov-gate-ft2:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf csoai/sov-gate-ft2:F16

Use Docker

docker model run hf.co/csoai/sov-gate-ft2:F16

LM Studio
Jan

vLLM

How to use csoai/sov-gate-ft2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "csoai/sov-gate-ft2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "csoai/sov-gate-ft2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/csoai/sov-gate-ft2:F16

Ollama
How to use csoai/sov-gate-ft2 with Ollama:
```
ollama run hf.co/csoai/sov-gate-ft2:F16
```

Unsloth Studio

How to use csoai/sov-gate-ft2 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 csoai/sov-gate-ft2 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 csoai/sov-gate-ft2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for csoai/sov-gate-ft2 to start chatting

How to use csoai/sov-gate-ft2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf csoai/sov-gate-ft2:F16

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": "csoai/sov-gate-ft2:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use csoai/sov-gate-ft2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf csoai/sov-gate-ft2:F16

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 "csoai/sov-gate-ft2:F16" \
  --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 csoai/sov-gate-ft2 with Docker Model Runner:
```
docker model run hf.co/csoai/sov-gate-ft2:F16
```

Lemonade

How to use csoai/sov-gate-ft2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull csoai/sov-gate-ft2:F16

Run and chat with the model

lemonade run user.sov-gate-ft2-F16

List all available models

lemonade list

Hermes Agent

How to use csoai/sov-gate-ft2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf csoai/sov-gate-ft2:F16

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 csoai/sov-gate-ft2:F16

Run Hermes

hermes

Atomic Chat

sov-gate-ft2

File size: 840 Bytes

---
license: apache-2.0
tags:
- sovereign
- gate
- refusal
- csoai
- text-generation
pipeline_tag: text-generation
library_name: gguf
base_model: Qwen/Qwen2-0.5B
models:
- csoai/oowm-router
datasets:
- csoai/gspc-normalized
- csoai/gspc-art5
---

# csoai/sov-gate-ft2

CSOAI sovereign **gate model** (Qwen2-494M, f16 GGUF). Decisive refusal-calibration result on the 2026-08-08 spray (all Ed25519-signed):

| Benchmark | sov-gate-ft2 | qwen2.5:1.5b | falcon3:7b |
|---|---|---|---|
| xstest-v2-copy (refusal calibration) | **0.770** | 0.360 | 0.600 |

+41 pts vs size-matched baseline on refusal calibration; +17 pts on AgentHarm. Aggregate 0.477 across 12 benchmarks (n=1131, parity with open baselines — honest).

Wrapped by `csoai/oowm-router`. Living-training-attestation, not a certification. CSOAI Ltd · UK 16939677 · Apache-2.0.