Instructions to use shieldstackllc/GLM-4.7-Flash-PRISM-mlx-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shieldstackllc/GLM-4.7-Flash-PRISM-mlx-4bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("shieldstackllc/GLM-4.7-Flash-PRISM-mlx-4bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

Pi new

How to use shieldstackllc/GLM-4.7-Flash-PRISM-mlx-4bit with 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-4bit"

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-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use shieldstackllc/GLM-4.7-Flash-PRISM-mlx-4bit with Hermes Agent:

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-4bit"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default shieldstackllc/GLM-4.7-Flash-PRISM-mlx-4bit

Run Hermes

hermes

MLX LM

How to use shieldstackllc/GLM-4.7-Flash-PRISM-mlx-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "shieldstackllc/GLM-4.7-Flash-PRISM-mlx-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "shieldstackllc/GLM-4.7-Flash-PRISM-mlx-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "shieldstackllc/GLM-4.7-Flash-PRISM-mlx-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

GLM-4.7-Flash-PRISM-mlx-4bit / README.md

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	---
	language:
	- en
	- zh
	license: other
	license_name: glm-4-license
	pipeline_tag: text-generation
	tags:
	- mlx
	- glm4
	- moe
	- prism
	- abliterated
	- 4bit
	- quantized
	- apple-silicon
	library_name: mlx
	base_model: Ex0bit/GLM-4.7-Flash-PRISM
	---

	<p align="center">
	<a href="https://vmlx.net">
	<img src="vmlx-logo.png" alt="vMLX" width="120">
	</a>
	</p>

	# GLM-4.7-Flash-PRISM — MLX 4-bit

	MLX 4-bit quantized version of [Ex0bit/GLM-4.7-Flash-PRISM](https://huggingface.co/Ex0bit/GLM-4.7-Flash-PRISM) for efficient local inference on Apple Silicon.

	- Quantization: 4-bit (4.5 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: ~16 GB

	## Usage

	```python
	from mlx_lm import load, generate

	model, tokenizer = load("shieldstackllc/GLM-4.7-Flash-PRISM-mlx-4bit")
	response = generate(model, tokenizer, prompt="Hello!", verbose=True)
	```

	Or with [vMLX](https://vmlx.net) 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](https://huggingface.co/Ex0bit) as part of the PRISM series. MLX quantization by [vMLX](https://vmlx.net).

	## Also Available

	- [GLM-4.7-Flash-PRISM MLX 8-bit](https://huggingface.co/shieldstackllc/GLM-4.7-Flash-PRISM-mlx-8bit) (~30 GB)

	## Made for vMLX

	This model was converted and optimized for [vMLX](https://vmlx.net) — a free, open source macOS native MLX inference engine for Apple Silicon. Download vMLX to run this model locally with zero configuration.

	## Credits

	- Base model: [THUDM/GLM-4](https://github.com/THUDM/GLM-4) by Zhipu AI
	- Abliteration: [Ex0bit/GLM-4.7-Flash-PRISM](https://huggingface.co/Ex0bit/GLM-4.7-Flash-PRISM)
	- MLX conversion: [vMLX](https://vmlx.net) — Run AI locally on Mac. No compromises.

	## Contact

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