Instructions to use NLP-Orange-Problem/AttentionSeekers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use NLP-Orange-Problem/AttentionSeekers with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct") model = PeftModel.from_pretrained(base_model, "NLP-Orange-Problem/AttentionSeekers") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_prefix_space": false, | |
| "backend": "tokenizers", | |
| "bos_token": "<|im_start|>", | |
| "clean_up_tokenization_spaces": false, | |
| "end_of_utterance_token": "<end_of_utterance>", | |
| "eos_token": "<end_of_utterance>", | |
| "fake_image_token": "<fake_token_around_image>", | |
| "global_image_token": "<global-img>", | |
| "image_token": "<image>", | |
| "is_local": false, | |
| "legacy": false, | |
| "model_max_length": 16384, | |
| "model_specific_special_tokens": { | |
| "end_of_utterance_token": "<end_of_utterance>", | |
| "fake_image_token": "<fake_token_around_image>", | |
| "global_image_token": "<global-img>", | |
| "image_token": "<image>" | |
| }, | |
| "pad_token": "<|im_end|>", | |
| "processor_class": "SmolVLMProcessor", | |
| "tokenizer_class": "TokenizersBackend", | |
| "truncation_side": "left", | |
| "unk_token": "<|endoftext|>", | |
| "vocab_size": 49152 | |
| } | |