Instructions to use mlx-community/GLM-OCR-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlx-community/GLM-OCR-4bit with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="mlx-community/GLM-OCR-4bit")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("mlx-community/GLM-OCR-4bit") model = AutoModelForMultimodalLM.from_pretrained("mlx-community/GLM-OCR-4bit", device_map="auto") - MLX
How to use mlx-community/GLM-OCR-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir GLM-OCR-4bit mlx-community/GLM-OCR-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
File size: 597 Bytes
97f5875 | 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": "Glm46VImageProcessor",
"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": "GlmOcrProcessor"
}
|