Instructions to use SRDdev/Nebula with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SRDdev/Nebula 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="SRDdev/Nebula")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("SRDdev/Nebula") model = AutoModelForImageTextToText.from_pretrained("SRDdev/Nebula") - Notebooks
- Google Colab
- Kaggle
File size: 506 Bytes
5abf501 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"cls_token": "[CLS]",
"do_basic_tokenize": true,
"do_lower_case": true,
"mask_token": "[MASK]",
"model_max_length": 512,
"name_or_path": "bert-base-uncased",
"never_split": null,
"pad_token": "[PAD]",
"processor_class": "BlipProcessor",
"sep_token": "[SEP]",
"special_tokens_map_file": null,
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]",
"model_input_names": [
"input_ids",
"attention_mask"
]
}
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