Instructions to use Junrulu/MemoChat-Fastchat-T5-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Junrulu/MemoChat-Fastchat-T5-3B with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Junrulu/MemoChat-Fastchat-T5-3B") model = AutoModelForSeq2SeqLM.from_pretrained("Junrulu/MemoChat-Fastchat-T5-3B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7e518a860fb53ea19a9332d32c9e713bd7d0c710a307630752821e46f902b9ec
- Size of remote file:
- 5.7 GB
- SHA256:
- 5dd225effacd4822e787b5f66b83bb25c51eb68709717f589ec0870ef41db5d6
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