Instructions to use poolside-laguna-hackathon/laguna-vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use poolside-laguna-hackathon/laguna-vision 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="poolside-laguna-hackathon/laguna-vision")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("poolside-laguna-hackathon/laguna-vision", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| """Startup compatibility patches for Hugging Face's endpoint wrapper.""" | |
| try: | |
| import transformers | |
| import transformers.file_utils as file_utils | |
| import transformers.utils as transformers_utils | |
| try: | |
| from transformers.utils.import_utils import is_torch_available | |
| except Exception: | |
| from transformers.utils import is_torch_available | |
| if not hasattr(file_utils, "is_tf_available"): | |
| file_utils.is_tf_available = lambda: False | |
| if not hasattr(file_utils, "is_torch_available"): | |
| file_utils.is_torch_available = is_torch_available | |
| for name, value in { | |
| "CONFIG_NAME": "config.json", | |
| "FLAX_WEIGHTS_NAME": "flax_model.msgpack", | |
| "SAFE_WEIGHTS_INDEX_NAME": "model.safetensors.index.json", | |
| "SAFE_WEIGHTS_NAME": "model.safetensors", | |
| "TF2_WEIGHTS_NAME": "tf_model.h5", | |
| "TF_WEIGHTS_NAME": "model.ckpt", | |
| "WEIGHTS_INDEX_NAME": "pytorch_model.bin.index.json", | |
| "WEIGHTS_NAME": "pytorch_model.bin", | |
| }.items(): | |
| for module in (file_utils, transformers_utils): | |
| if getattr(module, name, None) is None: | |
| setattr(module, name, value) | |
| except Exception: | |
| pass | |
| try: | |
| import transformers | |
| class _EndpointUnusedMT5Tokenizer: | |
| pass | |
| fallback_tokenizer = _EndpointUnusedMT5Tokenizer | |
| fallback_tokenizer_fast = _EndpointUnusedMT5Tokenizer | |
| try: | |
| fallback_tokenizer = getattr(transformers, "T5Tokenizer") | |
| except Exception: | |
| pass | |
| try: | |
| fallback_tokenizer_fast = getattr(transformers, "T5TokenizerFast") | |
| except Exception: | |
| fallback_tokenizer_fast = fallback_tokenizer | |
| transformers.MT5Tokenizer = fallback_tokenizer | |
| transformers.MT5TokenizerFast = fallback_tokenizer_fast | |
| except Exception: | |
| pass | |