Instructions to use Amalq/flan_t5_large_chat_summary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Amalq/flan_t5_large_chat_summary with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Amalq/flan_t5_large_chat_summary") model = AutoModelForSeq2SeqLM.from_pretrained("Amalq/flan_t5_large_chat_summary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9ea9bfc5112d1e07fb66d05b5c267ef90ef46733a6cbce5493c64d0e10717b66
- Size of remote file:
- 3.13 GB
- SHA256:
- 755ec2fdad4086efe14e2338dad4f69fab9fca8076ddab4833445c94c6dd5d2e
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