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
File size: 596 Bytes
9677807 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | {
"image_processor": {
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.48145466,
0.4578275,
0.40821073
],
"image_processor_type": "Glm4vImageProcessor",
"image_std": [
0.26862954,
0.26130258,
0.27577711
],
"merge_size": 2,
"patch_size": 14,
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {
"longest_edge": 9633792,
"shortest_edge": 12544
},
"temporal_patch_size": 2
},
"processor_class": "Glm46VProcessor"
}
|