Instructions to use osirisbrain/OsirisSoul-v1-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use osirisbrain/OsirisSoul-v1-MLX with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("osirisbrain/OsirisSoul-v1-MLX") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- Pi new
How to use osirisbrain/OsirisSoul-v1-MLX with 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 @mariozechner/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
- Hermes Agent new
How to use osirisbrain/OsirisSoul-v1-MLX with Hermes Agent:
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 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 osirisbrain/OsirisSoul-v1-MLX
Run Hermes
hermes
- MLX LM
How to use osirisbrain/OsirisSoul-v1-MLX with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "osirisbrain/OsirisSoul-v1-MLX"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "osirisbrain/OsirisSoul-v1-MLX" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "osirisbrain/OsirisSoul-v1-MLX", "messages": [ {"role": "user", "content": "Hello"} ] }'
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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