Instructions to use MMInstruction/YingVLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MMInstruction/YingVLM with Transformers:
# Load model directly from transformers import AutoProcessor, VLM processor = AutoProcessor.from_pretrained("MMInstruction/YingVLM") model = VLM.from_pretrained("MMInstruction/YingVLM") - Notebooks
- Google Colab
- Kaggle
| { | |
| "bos_token": { | |
| "__type": "AddedToken", | |
| "content": "<s>", | |
| "lstrip": false, | |
| "normalized": true, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": { | |
| "__type": "AddedToken", | |
| "content": "</s>", | |
| "lstrip": false, | |
| "normalized": true, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": null, | |
| "sp_model_kwargs": {}, | |
| "tokenizer_class": "LlamaTokenizer", | |
| "unk_token": { | |
| "__type": "AddedToken", | |
| "content": "<unk>", | |
| "lstrip": false, | |
| "normalized": true, | |
| "rstrip": false, | |
| "single_word": false | |
| } | |
| } | |