Instructions to use LumenSyntax/logos-auditor-gemma2-9b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LumenSyntax/logos-auditor-gemma2-9b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="LumenSyntax/logos-auditor-gemma2-9b",
	filename="logos-auditor-gemma2-9b.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use LumenSyntax/logos-auditor-gemma2-9b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LumenSyntax/logos-auditor-gemma2-9b
# Run inference directly in the terminal:
llama-cli -hf LumenSyntax/logos-auditor-gemma2-9b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LumenSyntax/logos-auditor-gemma2-9b
# Run inference directly in the terminal:
llama-cli -hf LumenSyntax/logos-auditor-gemma2-9b

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 LumenSyntax/logos-auditor-gemma2-9b
# Run inference directly in the terminal:
./llama-cli -hf LumenSyntax/logos-auditor-gemma2-9b

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 LumenSyntax/logos-auditor-gemma2-9b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LumenSyntax/logos-auditor-gemma2-9b

Use Docker

docker model run hf.co/LumenSyntax/logos-auditor-gemma2-9b

LM Studio
Jan

vLLM

How to use LumenSyntax/logos-auditor-gemma2-9b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LumenSyntax/logos-auditor-gemma2-9b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LumenSyntax/logos-auditor-gemma2-9b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LumenSyntax/logos-auditor-gemma2-9b

Ollama
How to use LumenSyntax/logos-auditor-gemma2-9b with Ollama:
```
ollama run hf.co/LumenSyntax/logos-auditor-gemma2-9b
```

Unsloth Studio new

How to use LumenSyntax/logos-auditor-gemma2-9b 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 LumenSyntax/logos-auditor-gemma2-9b 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 LumenSyntax/logos-auditor-gemma2-9b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for LumenSyntax/logos-auditor-gemma2-9b to start chatting

Docker Model Runner
How to use LumenSyntax/logos-auditor-gemma2-9b with Docker Model Runner:
```
docker model run hf.co/LumenSyntax/logos-auditor-gemma2-9b
```

Lemonade

How to use LumenSyntax/logos-auditor-gemma2-9b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LumenSyntax/logos-auditor-gemma2-9b

Run and chat with the model

lemonade run user.logos-auditor-gemma2-9b-{{QUANT_TAG}}

List all available models

lemonade list

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Logos Auditor — Gemma 2 9B (ARBITER)

The primary epistemological safety model from "The Instrument Trap: Why Identity-as-Authority Breaks AI Safety Systems" (DOI: 10.5281/zenodo.18716474).

This is the ARBITER — the 9B reference model that serves as the gold-standard epistemological firewall in the ALEPH architecture.

Key Results

Metric	Value	95% CI
Behavioral Pass	97.3%	[94.8, 98.6]
External Fabrication	0.0%	[0.00%, 0.03%]
Attack Resistance (ADVERSARIAL)	98.7%	—

What This Model Does

Logos is NOT a chatbot. It is a claim classifier — an epistemological firewall that determines whether an AI agent should act on a given claim. Logos is fine-tuned, not prompted. Behavioral constraints emerge from training, not system instructions.

Access

This model requires approved access. Request access using the form above and describe your intended use case.

Related Models

logos10v2-gemma3-1b-F16 — 1B model (Gemma 3)
logos14-nemotron-4b — Cross-family (NVIDIA Nemotron)
logos16v2-stablelm2-1.6b — Cross-family (Stability AI StableLM)

Paper

Rodriguez, R. (2026). "The Instrument Trap: Why Identity-as-Authority Breaks AI Safety Systems." Zenodo. DOI: 10.5281/zenodo.18716474

License

This model inherits the Gemma license from its base model.

Downloads last month: -

GGUF

Model size

9B params

Architecture

gemma2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for LumenSyntax/logos-auditor-gemma2-9b

Base model

google/gemma-2-9b

Finetuned

google/gemma-2-9b-it

Quantized

(156)

this model

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logos-auditor-gemma2-9b