omnistep-12

The OmniStep Evolutionary Radio is the operational version of "infinitely generate its own background music" — a 4-loop pipeline (playback + queue fill + GEPA prompt evolution + Darwin weight evolution) wired up in the evolutionary-radio skill.

🎧 Listen to the examples

1. 🎵 Lo-Fi — chill lofi beats for late-night coding

chill lofi beats, mellow hip-hop, soft piano keys, vinyl crackle, late-night study vibes, 75 bpm, instrumental

🎤 Voice intro (text generated by OmniStep 12A3B, speech by Soprano 80M)

🎵 The track

2. 🎬 Movie Orchestra — epic cinematic orchestral

epic cinematic orchestral soundtrack, sweeping strings, French horns, building tension, Hans Zimmer style, 90 bpm, instrumental

🎤 Voice intro

🎵 The track

3. 🔥 Dark Metal — heavy dark metal for dark times

heavy dark metal, blast beats, down-tuned 7-string guitars, atmospheric, blackened death metal, 180 bpm, instrumental

🎤 Voice intro

🎵 The track

• 🧠 Vibe coach — reads the room, matches your mood, suggests what to play next
• 📝 Note-taker — listens to your conversation and captures the bits you want to remember
• 💬 Conversational companion — keeps up a real back-and-forth, asks the follow-up questions
• 🎵 Background music that matches the vibe — generates infinite music that fits what you're doing, in any style
• 🔁 Self-evolving — gets better at being your assistant over time, via the Darwin Family weight-space evolution methodology
• 🎤 Real-time ASR + TTS — Whisper audio in, Talker + token2wav audio out (4o-style streaming voice)
• 🖼️ Image understanding — NaViT vision encoder

All in one model. Run it with vllm, llama-server, or the included Python scripts.

🎛 Pick your quantization — download just the one you need

The GGUFs are independent files. Download only the one that fits your VRAM — you don't need all of them. Pick from the table below.

Run any of them with llama-server (the Omni build of llama.cpp is in the HF model comments / wiki):

llama-server -m omnistep-12a3b-q4_k_m.gguf -ngl 99 --port 8080 --host 0.0.0.0 -c 8192

Quick start

Option 1 — vllm (the easiest, full multimodal)

pip install vllm
vllm serve sovthpaw/omnistep-12a3b \
    --max-model-len 32768 \
    --gpu-memory-utilization 0.85 \
    --trust-remote-code

Then in another terminal:

curl -s http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "sovthpaw/omnistep-12a3b",
        "messages": [{"role": "user", "content": "Take a note: I need to follow up with the design team about the Q3 launch."}],
        "max_tokens": 200
    }'

See vllm Qwen2.5-Omni docs for the full multimodal API.

Option 2 — llama-server with the GGUFs (fast text path)

# Pick your quantization based on VRAM
#   F16  = 6.4GB VRAM, best quality
#   Q8_0 = 3.4GB VRAM, near-F16 quality
#   Q4_K_M = 2.0GB VRAM, recommended
#   Q4_0 = 1.9GB VRAM, smallest

llama-server \
    -m omnistep-12a3b-q4_k_m.gguf \
    -ngl 99 \
    --port 8080 \
    --host 0.0.0.0 \
    -c 8192

curl -s http://localhost:8080/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "omnistep-12a3b",
        "messages": [{"role": "user", "content": "I am coding late, give me a lofi vibe and a note about what I should focus on tomorrow."}],
        "max_tokens": 200,
        "stream": true
    }'

The 4 GGUFs are deployment options — pick whichever fits your hardware. Q4_K_M is the recommended sweet spot.

Option 3 — the included Python scripts (the most fun)

The repo includes Python scripts that wire everything together for the headline use cases. After cloning:

git clone https://huggingface.co/sovthpaw/omnistep-12a3b
cd omnistep-12a3b

# Start the vllm server (one terminal)
python scripts/run_omnistep_12a3b.py serve

# In another terminal — try the modalities
python scripts/run_omnistep_12a3b.py te