Instructions to use jienengchen/ViTamin-B-LTT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jienengchen/ViTamin-B-LTT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jienengchen/ViTamin-B-LTT", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jienengchen/ViTamin-B-LTT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_commit_hash": null, | |
| "architectures": [ | |
| "ViTaminCLIP" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_vitamin.ViTaminConfig", | |
| "AutoModel": "model.ViTaminCLIP" | |
| }, | |
| "embed_dim": 768, | |
| "vision_cfg": { | |
| "timm_model_name": "vitamin_base", | |
| "timm_model_pretrained": false, | |
| "timm_pool": "", | |
| "timm_proj": "linear", | |
| "timm_drop": 0.0, | |
| "timm_drop_path": 0.1, | |
| "image_size": 224 | |
| }, | |
| "text_cfg": { | |
| "context_length": 77, | |
| "vocab_size": 49408, | |
| "width": 768, | |
| "heads": 12, | |
| "layers": 12 | |
| } | |
| } |