Instructions to use inferencerlabs/archived-GLM-5-MLX-4.8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use inferencerlabs/archived-GLM-5-MLX-4.8bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("inferencerlabs/archived-GLM-5-MLX-4.8bit") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use inferencerlabs/archived-GLM-5-MLX-4.8bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "inferencerlabs/archived-GLM-5-MLX-4.8bit" --prompt "Once upon a time"
GLM-5-MLX-4.8bit
NOTICE - This model has been superseded by GLM5.1-Q4.8-INF: available here
See GLM-5 MLX in action: demonstration video
Originally tested on a M3 Ultra 512GB RAM using Inferencer app
- Single inference ~16.6 tokens/s @ 1000 tokens
- Batched inference ~31.8 total tokens/s across six inferences
- Memory usage: ~417 GiB
Model tree for inferencerlabs/archived-GLM-5-MLX-4.8bit
Base model
zai-org/GLM-5