Instructions to use Tianle/test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tianle/test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Tianle/test")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Tianle/test") model = AutoModel.from_pretrained("Tianle/test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "None", | |
| "architectures": [ | |
| "CLIPVisionModel" | |
| ], | |
| "attention_dropout": 0.0, | |
| "dropout": 0.0, | |
| "hidden_act": "gelu", | |
| "hidden_size": 1024, | |
| "image_size": 224, | |
| "initializer_factor": 1.0, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "layer_norm_eps": 1e-05, | |
| "model_type": "clip_vision_model", | |
| "num_attention_heads": 16, | |
| "num_channels": 3, | |
| "num_hidden_layers": 24, | |
| "patch_size": 14, | |
| "projection_dim": 768, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.40.2" | |
| } | |