Instructions to use vishal-carvia/flan-t5-small-carvia_nlc2cmd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vishal-carvia/flan-t5-small-carvia_nlc2cmd with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("vishal-carvia/flan-t5-small-carvia_nlc2cmd") model = AutoModelForSeq2SeqLM.from_pretrained("vishal-carvia/flan-t5-small-carvia_nlc2cmd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7299b5f2ba8d18ce1b5ff894496d351b9e32e4fbc1a05a4963aa2dc45b4577e5
- Size of remote file:
- 4.16 kB
- SHA256:
- 6d900783868b2145e161bc7a8b0ddd281383ef097940ef25a0de53b16a5e52bd
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