How to use from
Pi
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "osirisbrain/OsirisSoul-v1-MLX"
Configure the model in Pi
# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "osirisbrain/OsirisSoul-v1-MLX"
        }
      ]
    }
  }
}
Run Pi
# Start Pi in your project directory:
pi
Quick Links

OsirisTalon-v3-0.6B-MLX

The Talon — Osiris's ultra-fast tool classifier brain. Runs alongside the main Cortex (9B) on Apple Silicon unified memory via MLX.

Purpose

Pre-classifies user intent in <100ms, selecting the optimal tool and arguments before the main Cortex model processes the request. This eliminates an entire ReAct inference cycle, cutting total response time from ~60-134s to ~25s.

Architecture

  • Base Model: Qwen3-0.6B (600M parameters)
  • Format: MLX 4-bit quantized (Apple Silicon native)
  • Size: ~335MB
  • Speed: ~200+ tokens/sec on M2 Pro (MLX Metal)
  • Purpose: Tool selection, intent classification, complexity rating

Usage

from mlx_lm import load, generate

model, tokenizer = load("osirisbrain/OsirisTalon-v3-0.6B-MLX")
prompt = tokenizer.apply_chat_template(
    [{"role": "user", "content": "cuanto espacio tengo en disco"}],
    add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, max_tokens=100)

Integration

Runs as a dedicated MLX inference server on port 8086, coexisting with llama-server (Cortex 9B) on port 8085. Both share Apple Silicon unified memory without conflict.

Credits

Rebranded from mlx-community/Qwen3-0.6B-4bit for the OsirisBrain sovereign AGI ecosystem. Original model: Qwen/Qwen3-0.6B by Alibaba.

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