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 "abenzerps/MiniCPM5-2B-MLX-6bit"
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": "abenzerps/MiniCPM5-2B-MLX-6bit"
        }
      ]
    }
  }
}
Run Pi
# Start Pi in your project directory:
pi
Quick Links

MiniCPM5-2B MLX - 6-bit

MLX 6-bit conversion of OpenBMB/MiniCPM5-2B, a 2B dense Llama-based model for local deployment, coding, reasoning, long-context work, and tool use. The source checkpoint supports a native context length of 131,072 tokens (128K).

Benchmarks

MiniCPM5-2B evaluation results

MiniCPM5-2B capability radar

Benchmark results reported by OpenBMB for the original MiniCPM5-2B checkpoint.

Release

Format Quantization Size
MLX safetensors Affine 6-bit, group size 64 2.0 GB

The model is text-only. No vision projector or MTP files are included.

Usage

pip install -U mlx-lm
mlx_lm.generate \
  --model . \
  --prompt "Explain why reproducible builds matter." \
  --max-tokens 512

Source

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Safetensors
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6-bit

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