Instructions to use shieldstackllc/GLM-4.7-Flash-PRISM-mlx-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use shieldstackllc/GLM-4.7-Flash-PRISM-mlx-8bit 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-8bit") 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-8bit 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-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
- Hermes Agent new
How to use shieldstackllc/GLM-4.7-Flash-PRISM-mlx-8bit 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-8bit"
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-8bit
Run Hermes
hermes
- MLX LM
How to use shieldstackllc/GLM-4.7-Flash-PRISM-mlx-8bit 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-8bit"
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-8bit" # 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-8bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
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
- GLM-4.7-Flash-PRISM MLX 4-bit (~16 GB)
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
- Base model: THUDM/GLM-4 by Zhipu AI
- Abliteration: Ex0bit/GLM-4.7-Flash-PRISM
- MLX conversion: vMLX — Run AI locally on Mac. No compromises.
Contact
For questions, issues, or collaboration: admin@vmlx.net
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8-bit
Model tree for shieldstackllc/GLM-4.7-Flash-PRISM-mlx-8bit
Base model
Ex0bit/GLM-4.7-Flash-PRISM
# 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-8bit") 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)