Instructions to use Valencio/LLM_course_EncDec_model_summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Valencio/LLM_course_EncDec_model_summarization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Valencio/LLM_course_EncDec_model_summarization") model = AutoModelForSeq2SeqLM.from_pretrained("Valencio/LLM_course_EncDec_model_summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0cc3ec7bfd347bd6ad3b87888b4959df3f486786b45a5706168fd7f19ee2c692
- Size of remote file:
- 242 MB
- SHA256:
- 9a11bc2cb435dd77685688ae0f7b6ff0b321a9885bc640de07290f50381726c7
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