How to use from
MLX LM
Generate or start a chat session
# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "osirisbrain/OsirisCortex-v7-MLX"
Run an OpenAI-compatible server
# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "osirisbrain/OsirisCortex-v7-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/OsirisCortex-v7-MLX",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'
Quick Links

OsirisCortex-v7-MLX

The Cortex v7 — Osiris's sovereign reasoning brain. Fully uncensored (abliterated), 0% refusal rate. Runs natively on Apple Silicon via MLX Metal.

Architecture

  • Base Model: Qwen3.5-9B (9 billion parameters)
  • Modification: Abliterated (orthogonal projection + LoRA fine-tuning for 0% refusal)
  • Format: MLX 4-bit quantized (Apple Silicon native)
  • Size: ~4.7 GB
  • Speed: ~80-120 tokens/sec on M2 Pro (MLX Metal)

What Changed from v6

  • v6: OsirisCortex-v6 was GGUF format running via llama.cpp server
  • v7: Native MLX format, runs via mlx_lm.server — faster, lower memory, Apple-native
  • Uncensored: Zero refusal — answers everything the user asks without moral gatekeeping

Usage

from mlx_lm import load, generate

model, tokenizer = load("osirisbrain/OsirisCortex-v7-MLX")
prompt = tokenizer.apply_chat_template(
    [{"role": "user", "content": "Explain quantum computing"}],
    add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, max_tokens=2048)

Credits

Abliterated by lukey03. Original model: Qwen/Qwen3.5-9B by Alibaba.

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