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 "StargazerLabs/Qwen3.8-32B-Jumbo-8bit"
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": "StargazerLabs/Qwen3.8-32B-Jumbo-8bit"
        }
      ]
    }
  }
}
Run Pi
# Start Pi in your project directory:
pi
Quick Links

Qwen3.8-32B-Jumbo (8-bit)

8-bit quantization of Qwen3.8-32B-Jumbo.

A 76-layer, 31.9B-parameter model created by transplanting the three most-drifted organs from Qwen3.6-27B into Qwen3.8-27B. See the bf16 model card for the full organ selection methodology.

Key Stats

  • Parameters: ~31.9B
  • Layers: 76
  • Precision: 8-bit (mlx-lm quantized)
  • MTP Compatible: Accepts the parent Qwen3.8 MTP drafter for speculative decoding

Usage (MLX)

# Standard generation
mlx_vlm.generate \
  --model StargazerLabs/Qwen3.8-32B-Jumbo-8bit \
  --prompt "Your prompt here" --max-tokens 2048

# With MTP speculative decoding
mlx_vlm.generate \
  --model StargazerLabs/Qwen3.8-32B-Jumbo-8bit \
  --draft-model mlx-community/Qwen3.8-27B-MTP-8bit \
  --prompt "Your prompt here" --max-tokens 2048

Related

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