Instructions to use LumenSyntax/logos16v2-stablelm2-1.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LumenSyntax/logos16v2-stablelm2-1.6b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="LumenSyntax/logos16v2-stablelm2-1.6b",
	filename="logos16v2.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/logos16v2-stablelm2-1.6b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LumenSyntax/logos16v2-stablelm2-1.6b
# Run inference directly in the terminal:
llama-cli -hf LumenSyntax/logos16v2-stablelm2-1.6b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LumenSyntax/logos16v2-stablelm2-1.6b
# Run inference directly in the terminal:
llama-cli -hf LumenSyntax/logos16v2-stablelm2-1.6b

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/logos16v2-stablelm2-1.6b
# Run inference directly in the terminal:
./llama-cli -hf LumenSyntax/logos16v2-stablelm2-1.6b

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/logos16v2-stablelm2-1.6b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LumenSyntax/logos16v2-stablelm2-1.6b

Use Docker

docker model run hf.co/LumenSyntax/logos16v2-stablelm2-1.6b

LM Studio
Jan

vLLM

How to use LumenSyntax/logos16v2-stablelm2-1.6b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LumenSyntax/logos16v2-stablelm2-1.6b"
# 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/logos16v2-stablelm2-1.6b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LumenSyntax/logos16v2-stablelm2-1.6b

Ollama
How to use LumenSyntax/logos16v2-stablelm2-1.6b with Ollama:
```
ollama run hf.co/LumenSyntax/logos16v2-stablelm2-1.6b
```

Unsloth Studio new

How to use LumenSyntax/logos16v2-stablelm2-1.6b 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/logos16v2-stablelm2-1.6b 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/logos16v2-stablelm2-1.6b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for LumenSyntax/logos16v2-stablelm2-1.6b to start chatting

Docker Model Runner
How to use LumenSyntax/logos16v2-stablelm2-1.6b with Docker Model Runner:
```
docker model run hf.co/LumenSyntax/logos16v2-stablelm2-1.6b
```

Lemonade

How to use LumenSyntax/logos16v2-stablelm2-1.6b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LumenSyntax/logos16v2-stablelm2-1.6b

Run and chat with the model

lemonade run user.logos16v2-stablelm2-1.6b-{{QUANT_TAG}}

List all available models

lemonade list

Logos 16v2 — StableLM 2 1.6B Epistemological Auditor

Cross-family replication of the Logos epistemological classifier on Stability AI's StableLM 2 architecture. Third independent architecture family confirming the Instrument Trap hypothesis.

Benchmark Results (300/300 stratified)

Metric	Score
Behavioral accuracy	93.0% [89.5, 95.4 CI]
Identity collapse	0%
Fabrication	0%
False approval	1.3%

Cross-Family Comparison

Model	Family	Score
logos-auditor (9B)	Google Gemma 2	97.3%
logos14 (4B)	NVIDIA Nemotron	95.7%
logos16v2 (1.6B)	Stability AI StableLM 2	93.0%

Statistical equivalence between Nemotron and StableLM: chi2=1.88, p=0.170.

What This Model Does

Logos is an epistemological classifier, not a chatbot. It evaluates whether claims cross epistemological boundaries. Fine-tuned, not prompted — behavioral constraints emerge from training.

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

Apache 2.0 (inherited from base model stabilityai/stablelm-2-zephyr-1_6b)

Downloads last month: 15

Safetensors

Model size

2B params

Tensor type

F16

Model tree for LumenSyntax/logos16v2-stablelm2-1.6b

Base model

stabilityai/stablelm-2-zephyr-1_6b

Quantized

(18)

this model

Quantizations

2 models

LumenSyntax
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logos16v2-stablelm2-1.6b