Instructions to use inferencerlabs/GLM-5.2-MTP-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use inferencerlabs/GLM-5.2-MTP-MLX with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("inferencerlabs/GLM-5.2-MTP-MLX") config = load_config("inferencerlabs/GLM-5.2-MTP-MLX") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
- Xet hash:
- 6c31f29fd51ed17573503897419706dc12a8093fae7571bd994facf7f8ebca99
- Size of remote file:
- 5.6 GB
- SHA256:
- 6b12720c860be86d61a3e49ae229adb05def5732da52a8d90e65330995c41f7d
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