Instructions to use rekstar/omega-attention-0.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rekstar/omega-attention-0.5b with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "rekstar/omega-attention-0.5b")

Transformers

How to use rekstar/omega-attention-0.5b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="rekstar/omega-attention-0.5b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rekstar/omega-attention-0.5b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use rekstar/omega-attention-0.5b 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 rekstar/omega-attention-0.5b:Q8_0
# Run inference directly in the terminal:
llama cli -hf rekstar/omega-attention-0.5b:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf rekstar/omega-attention-0.5b:Q8_0
# Run inference directly in the terminal:
llama cli -hf rekstar/omega-attention-0.5b:Q8_0

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 rekstar/omega-attention-0.5b:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf rekstar/omega-attention-0.5b:Q8_0

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 rekstar/omega-attention-0.5b:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rekstar/omega-attention-0.5b:Q8_0

Use Docker

docker model run hf.co/rekstar/omega-attention-0.5b:Q8_0

LM Studio
Jan

vLLM

How to use rekstar/omega-attention-0.5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rekstar/omega-attention-0.5b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rekstar/omega-attention-0.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rekstar/omega-attention-0.5b:Q8_0

SGLang

How to use rekstar/omega-attention-0.5b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "rekstar/omega-attention-0.5b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rekstar/omega-attention-0.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "rekstar/omega-attention-0.5b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rekstar/omega-attention-0.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use rekstar/omega-attention-0.5b with Ollama:
```
ollama run hf.co/rekstar/omega-attention-0.5b:Q8_0
```

Unsloth Studio

How to use rekstar/omega-attention-0.5b 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 rekstar/omega-attention-0.5b 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 rekstar/omega-attention-0.5b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rekstar/omega-attention-0.5b to start chatting

How to use rekstar/omega-attention-0.5b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf rekstar/omega-attention-0.5b:Q8_0

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": "rekstar/omega-attention-0.5b:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use rekstar/omega-attention-0.5b with Docker Model Runner:
```
docker model run hf.co/rekstar/omega-attention-0.5b:Q8_0
```

Lemonade

How to use rekstar/omega-attention-0.5b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rekstar/omega-attention-0.5b:Q8_0

Run and chat with the model

lemonade run user.omega-attention-0.5b-Q8_0

List all available models

lemonade list

Hermes Agent

How to use rekstar/omega-attention-0.5b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf rekstar/omega-attention-0.5b:Q8_0

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 rekstar/omega-attention-0.5b:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use rekstar/omega-attention-0.5b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf rekstar/omega-attention-0.5b:Q8_0

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 "rekstar/omega-attention-0.5b:Q8_0" \
  --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"

Model Card for Model ID

Omega Attention chooses between five attention operations:

ATTEND — bring something into active attention MAINTAIN — continue attending to the current focus SWITCH — move attention to a more important candidate SUPPRESS — actively suppress a distracting or conflicting candidate IGNORE — take no attentional action

The output contract is deliberately narrow.

{"operation":"SWITCH","target":"observation_4","confidence":0.91,"reason_code":"HIGHER_PRIORITY_INTERRUPT"}

The model is not intended to chat, explain its reasoning, or generate general-purpose text.

Its job is simply:

Given the current cognitive state, what should receive attention?

Model Details

Property Value Base model unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit Base parameters ~502M Fine-tuning LoRA LoRA rank 16 LoRA alpha 16 Trainable parameters ~8.8M Trainable percentage ~1.75% Training epochs 3 Training examples 5,000 Evaluation examples 500 Training platform AMD Radeon 8060S / ROCm Training framework Unsloth Output Compact JSON

Model Description

Omega Attention is a small language model fine-tuned specifically for attention management inside an artificial cognitive architecture.

Rather than using a general-purpose LLM to repeatedly decide what an agent should focus on, Omega Attention is designed as a lightweight specialist cognitive node.

Given a cognitive workspace containing competing goals, memories, observations, system events, and tool activity, the model selects an attention operation and target.

It is part of the broader Omega artificial cognitive architecture experiment.

Developed by: [Derek Robertson]
Model type: [Attention Processor]
Language(s) (NLP): [English]
License: [MIT]
Finetuned from model [optional]: [unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit]

Uses

Intended use

Omega Attention is intended for experimentation with:

artificial cognitive architectures autonomous agents cognitive workspaces attention allocation persistent agent loops multi-model agent systems specialised neural cognitive modules

It is primarily intended to operate as a component inside another system, rather than as a standalone conversational model.

Not intended for

Omega Attention is not intended to be:

a general-purpose assistant a reasoning model a chatbot a factual knowledge model a safety classifier a replacement for human decision-making

It has been trained for a deliberately narrow experimental task.

Downstream Use [optional]

About Omega

Omega is an experimental artificial cognitive architecture exploring the idea that an artificial mind does not need to be synonymous with a single large language model.

Instead, language models can act as specialised neural components within a larger cognitive system incorporating mechanisms such as attention, working memory, long-term memory, goal management, reasoning, planning, and action.

Omega Attention is the first specialised cognitive model produced as part of that experiment.

STATUS

Experimental — v0.1

This is the first public iteration of Omega Attention.

The major question addressed by v0.1 was:

Can a very small language model learn the attention-selection policy required by Omega?

The initial result is encouraging.

The next question is more important:

Does that learned policy generalise beyond the synthetic distribution on which it was trained?

Future work will focus on harder evaluation rather than immediately increasing model size.

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Training Hyperparameters

Training regime: [More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

Evaluation was performed against 500 held-out synthetic scenarios.

Metric 200 steps 1,875 steps / 3 epochs Valid JSON 100% 100% Operation accuracy 74.2% 96.0% Target accuracy 45.2% 92.2%

Results

The architecture, dataset, LoRA configuration, and model size were unchanged between these runs.

The primary difference was training duration.

Target accuracy increased from 45.2% to 92.2% without increasing model size or inference cost.

Summary

These results should not be interpreted as demonstrating 96% accuracy on arbitrary real-world attention-management tasks.

The evaluation examples are held out from training, but they are generated by the same synthetic scenario-generation system used to produce the training dataset.

The results therefore demonstrate that a 0.5B model can successfully learn the attention policy represented by this dataset.

Generalisation to different distributions, adversarial scenarios, and real Omega cognitive traces remains to be evaluated.