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:
- c6c8d7fc7e14e8c5a1c8e3b23539c7ed4a2ec05dffcf4d54e28680c9bdc052dd
- Size of remote file:
- 3.63 kB
- SHA256:
- 31c1d2d6246175bb258b9cfd505edcb32db689c17c60839deddb406895fb6454
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