Instructions to use inferencerlabs/GLM-5.2-MTP-MLX-Q4 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-Q4 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-Q4") config = load_config("inferencerlabs/GLM-5.2-MTP-MLX-Q4") # 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
GLM-5.2 MTP
See GLM-5.2 in action: demonstration videos
This draft model contains the Multi-Token Prediction (MTP) layers from zai-org/GLM-5.2 for use alongside the GLM-5.2-MLX model as a speculative decoder.
Q4 quant typically achieves higher throughput with less RAM usage (compared to base MTP) at no loss in quality.
Tested on a M3 Ultra 512GB RAM using Inferencer app v2.0.6
| C++ | ~77.9% correct predictions at 3 steps |
| HTML/ECMAScript | ~70% correct predictions at 3 steps |
| Maths | ~76.6% correct predictions at 3 steps |
| Encyclopedic | ~71.4% correct predictions at 3 steps |
| Creative Writing | ~46.2% correct predictions at 3 steps |
Enable speculative decoding in Inference Contols:
Disclaimer
We are not the creator, originator, or owner of any model listed. Each model is created and provided by third parties. Models may not always be accurate or contextually appropriate. You are responsible for verifying the information before making important decisions. We are not liable for any damages, losses, or issues arising from its use, including data loss or inaccuracies in AI-generated content.
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Quantized
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Base model
zai-org/GLM-5.2