Instructions to use JeremyLi96/Control_Token_Joint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremyLi96/Control_Token_Joint with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" 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("summarization", model="JeremyLi96/Control_Token_Joint")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("JeremyLi96/Control_Token_Joint") model = AutoModelForSeq2SeqLM.from_pretrained("JeremyLi96/Control_Token_Joint", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 06b80fb66ac948b429724e09e3a56ad6295a478d04bbd6f33368e8e302169ffe
- Size of remote file:
- 558 MB
- SHA256:
- 854e48e4ad357f8bee9111b8be084d22e8562abd417937981ef8598a626c4a0e
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