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 "shieldstackllc/GLM-4.7-Flash-PRISM-mlx-8bit"
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": "shieldstackllc/GLM-4.7-Flash-PRISM-mlx-8bit"
        }
      ]
    }
  }
}
Run Pi
# Start Pi in your project directory:
pi
Quick Links

vMLX

GLM-4.7-Flash-PRISM — MLX 8-bit

MLX 8-bit quantized version of Ex0bit/GLM-4.7-Flash-PRISM for efficient local inference on Apple Silicon.

  • Quantization: 8-bit (8.5 bits per weight, group size 64, affine mode)
  • Architecture: GLM-4 MoE Lite — 47 layers, 64 routed experts, 4 active per token
  • Context: 202K tokens
  • Size: ~30 GB

Usage

from mlx_lm import load, generate

model, tokenizer = load("shieldstackllc/GLM-4.7-Flash-PRISM-mlx-8bit")
response = generate(model, tokenizer, prompt="Hello!", verbose=True)

Or with vMLX for native macOS inference.

About

This model is an abliterated (uncensored) variant of GLM-4.7-Flash, a Mixture-of-Experts language model by Zhipu AI / THUDM. The abliteration was done by Ex0bit as part of the PRISM series. MLX quantization by vMLX.

Also Available

Made for vMLX

This model was converted and optimized for vMLX — a free, open source macOS native MLX inference engine for Apple Silicon. Download vMLX to run this model locally with zero configuration.

Credits

Contact

For questions, issues, or collaboration: admin@vmlx.net

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