Image-Text-to-Text
MLX
Safetensors
English
Chinese
glm4v
reasoning
thinking
vision-language
multimodal
conversational
8-bit precision
Instructions to use mlx-community/GLM-4.1V-9B-Thinking-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/GLM-4.1V-9B-Thinking-8bit 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("mlx-community/GLM-4.1V-9B-Thinking-8bit") config = load_config("mlx-community/GLM-4.1V-9B-Thinking-8bit") # 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
Add MLX 8-bit conversion of GLM-4.1V-9B-Thinking with vision preserved and weight-level fidelity vs bf16
9677807 verified | { | |
| "size": {"shortest_edge": 12544, "longest_edge": 9633792}, | |
| "do_rescale": true, | |
| "patch_size": 14, | |
| "temporal_patch_size": 2, | |
| "merge_size": 2, | |
| "image_mean": [0.48145466, 0.4578275, 0.40821073], | |
| "image_std": [0.26862954, 0.26130258, 0.27577711], | |
| "image_processor_type": "Glm4vImageProcessor", | |
| "processor_class": "Glm4vProcessor" | |
| } |