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 Settings

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

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

Atomic Chat new
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

logos-auditor-gemma2-9b / README.md

LumenSyntax

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	---
	tags:
	- gguf
	- gemma2
	- epistemological-safety
	- ai-safety
	- truth-verification
	- instrument-trap
	- logos
	- arbiter
	- text-generation
	language:
	- en
	datasets:
	- LumenSyntax/instrument-trap-benchmark
	base_model: google/gemma-2-9b-it
	license: other
	---

	# 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](https://zenodo.org/records/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](https://huggingface.co/LumenSyntax/logos10v2-gemma3-1b-F16) — 1B model (Gemma 3)
	- [logos14-nemotron-4b](https://huggingface.co/LumenSyntax/logos14-nemotron-4b) — Cross-family (NVIDIA Nemotron)
	- [logos16v2-stablelm2-1.6b](https://huggingface.co/LumenSyntax/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](https://doi.org/10.5281/zenodo.18716474)

	## License

	This model inherits the [Gemma license](https://ai.google.dev/gemma/terms) from its base model.