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")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("SRDdev/Nebula") model = AutoModelForMultimodalLM.from_pretrained("SRDdev/Nebula", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from SRDdev/Nebula: direct link, hf CLI and curl.
- Browser
- Download file 506 Bytes
-
https://huggingface.co/SRDdev/Nebula/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://SRDdev/Nebula/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/SRDdev/Nebula/resolve/main/tokenizer_config.json
506 Bytes
| { | |
| "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" | |
| ] | |
| } | |