Instructions to use jlee-larr/dynaflip-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jlee-larr/dynaflip-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="jlee-larr/dynaflip-base", trust_remote_code=True) pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jlee-larr/dynaflip-base", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "DynaFLIPModel" | |
| ], | |
| "include_trace_encoder": false, | |
| "model_type": "dynaflip", | |
| "projection_dim": 1536, | |
| "text_config": { | |
| "model_type": "dynaflip_text_model" | |
| }, | |
| "torch_dtype": "float32", | |
| "trace_base_width": 64, | |
| "trace_embed_dim": 384, | |
| "trace_num_heads": 6, | |
| "trace_state_window": 7, | |
| "trace_temporal_layers": 4, | |
| "transformers_version": "4.40.1", | |
| "vision_config": { | |
| "model_type": "dynaflip_vision_model" | |
| }, | |
| "auto_map": { | |
| "AutoConfig": "configuration_dynaflip.DynaFLIPConfig", | |
| "AutoModel": "modeling_dynaflip.DynaFLIPModel", | |
| "AutoProcessor": "processing_dynaflip.DynaFLIPProcessor", | |
| "AutoImageProcessor": "image_processing_dynaflip.DynaFLIPImageProcessor" | |
| } | |
| } |