Instructions to use Heliosoph/vit-gpt2-image-captioning-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Heliosoph/vit-gpt2-image-captioning-onnx 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="Heliosoph/vit-gpt2-image-captioning-onnx")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("Heliosoph/vit-gpt2-image-captioning-onnx") model = AutoModelForImageTextToText.from_pretrained("Heliosoph/vit-gpt2-image-captioning-onnx") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_prefix_space": false, | |
| "added_tokens_decoder": { | |
| "50256": { | |
| "content": "<|endoftext|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| } | |
| }, | |
| "bos_token": "<|endoftext|>", | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|endoftext|>", | |
| "max_length": 32, | |
| "model_max_length": 1024, | |
| "pad_to_multiple_of": null, | |
| "pad_token": "<|endoftext|>", | |
| "pad_token_type_id": 0, | |
| "padding_side": "right", | |
| "stride": 0, | |
| "tokenizer_class": "GPT2Tokenizer", | |
| "truncation_side": "right", | |
| "truncation_strategy": "longest_first", | |
| "unk_token": "<|endoftext|>" | |
| } | |