Instructions to use immanuelpeter/MiniMax-M3-Vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use immanuelpeter/MiniMax-M3-Vision with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="immanuelpeter/MiniMax-M3-Vision")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("immanuelpeter/MiniMax-M3-Vision") model = AutoModel.from_pretrained("immanuelpeter/MiniMax-M3-Vision", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 604 Bytes
accba69 | 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 | {
"processor_class": "AutoImageProcessor",
"image_processor_type": "MiniMaxM3VLImageProcessor",
"process_image_mode": "dynamic_res",
"image_mean": [
0.48145466,
0.4578275,
0.40821073
],
"image_std": [
0.26862954,
0.26130258,
0.27577711
],
"size": [
672,
672
],
"patch_size": 14,
"img_token_compression_config": {
"image_token_compression_threshold": 1.1,
"image_token_compression_method": "patch_merge",
"max_image_resolution": 1008,
"spatial_merge_size": 2,
"temporal_patch_size": 2
},
"add_start_end_special_tokens": true
}
|