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 "snsnc/Qwopus3.5-9B-Coder-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": "snsnc/Qwopus3.5-9B-Coder-MLX"
        }
      ]
    }
  }
}
Run Pi
# Start Pi in your project directory:
pi
Quick Links

Qwopus3.5-9B-Coder-MLX

MLX conversion of Jackrong/Qwopus3.5-9B-Coder for Apple Silicon.

Original upstream weights, converted to MLX format only. No additional fine-tuning or architecture changes.

Use with mlx-lm

pip install -U mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("snsnc/Qwopus3.5-9B-Coder-MLX")

prompt = "Write a short Python function that returns the factorial of a number."

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_dict=False,
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)
print(response)

Use with mlx-serve

pip install -U mlx-serve
mlx-serve \
  --model hf://snsnc/Qwopus3.5-9B-Coder-MLX \
  --serve \
  --host 0.0.0.0 \
  --port 11234

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