Instructions to use LumenSyntax/logos10v2-gemma3-1b-Q4_K_M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LumenSyntax/logos10v2-gemma3-1b-Q4_K_M with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="LumenSyntax/logos10v2-gemma3-1b-Q4_K_M",
	filename="logos10v2-gemma3-1b-Q4_K_M.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/logos10v2-gemma3-1b-Q4_K_M with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LumenSyntax/logos10v2-gemma3-1b-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LumenSyntax/logos10v2-gemma3-1b-Q4_K_M:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LumenSyntax/logos10v2-gemma3-1b-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LumenSyntax/logos10v2-gemma3-1b-Q4_K_M: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 LumenSyntax/logos10v2-gemma3-1b-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LumenSyntax/logos10v2-gemma3-1b-Q4_K_M: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 LumenSyntax/logos10v2-gemma3-1b-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LumenSyntax/logos10v2-gemma3-1b-Q4_K_M:Q4_K_M

Use Docker

docker model run hf.co/LumenSyntax/logos10v2-gemma3-1b-Q4_K_M:Q4_K_M

LM Studio
Jan

vLLM

How to use LumenSyntax/logos10v2-gemma3-1b-Q4_K_M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LumenSyntax/logos10v2-gemma3-1b-Q4_K_M"
# 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/logos10v2-gemma3-1b-Q4_K_M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LumenSyntax/logos10v2-gemma3-1b-Q4_K_M:Q4_K_M

Ollama
How to use LumenSyntax/logos10v2-gemma3-1b-Q4_K_M with Ollama:
```
ollama run hf.co/LumenSyntax/logos10v2-gemma3-1b-Q4_K_M:Q4_K_M
```

Unsloth Studio new

How to use LumenSyntax/logos10v2-gemma3-1b-Q4_K_M 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/logos10v2-gemma3-1b-Q4_K_M 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/logos10v2-gemma3-1b-Q4_K_M to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for LumenSyntax/logos10v2-gemma3-1b-Q4_K_M to start chatting

Docker Model Runner
How to use LumenSyntax/logos10v2-gemma3-1b-Q4_K_M with Docker Model Runner:
```
docker model run hf.co/LumenSyntax/logos10v2-gemma3-1b-Q4_K_M:Q4_K_M
```

Lemonade

How to use LumenSyntax/logos10v2-gemma3-1b-Q4_K_M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LumenSyntax/logos10v2-gemma3-1b-Q4_K_M:Q4_K_M

Run and chat with the model

lemonade run user.logos10v2-gemma3-1b-Q4_K_M-Q4_K_M

List all available models

lemonade list

Logos 10v2 — Gemma 3 1B Q4_K_M (Edge/Demo)

Quantized version of the Logos 10v2 epistemological classifier for edge deployment and demonstration purposes.

IMPORTANT: Edge-Only Model

This quantized model has known quality degradation. In testing, Q4_K_M falsely approved dangerous claims that the F16 version correctly rejected.

Do NOT use this model as a primary verifier. For production use, deploy the F16 version.

Benchmark Results (F16 version)

Metric	Score
Epistemological safety	97.7%
Hallucination	0.00%
Dangerous failures	1.9%

Note: These are F16 results. Q4_K_M quantization degrades quality — expect lower accuracy, especially on borderline cases.

Access

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

Connection to Research

This model is part of the evidence for "The Instrument Trap" (DOI: 10.5281/zenodo.18716474).

License

Gemma Terms of Use (inherited from base model google/gemma-3-1b-it).

Downloads last month: 8

GGUF

Model size

1.0B params

Architecture

gemma3

Hardware compatibility

4-bit

Model tree for LumenSyntax/logos10v2-gemma3-1b-Q4_K_M

Base model

google/gemma-3-1b-pt

Finetuned

google/gemma-3-1b-it

Quantized

(194)

this model

LumenSyntax
/

logos10v2-gemma3-1b-Q4_K_M