Instructions to use liamvbetts/mt5-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liamvbetts/mt5-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("liamvbetts/mt5-v2") model = AutoModelForSeq2SeqLM.from_pretrained("liamvbetts/mt5-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6b2505d4b8c0eec00d4028ff4502e2052e50fae60a34f64ae9e2d09b2f07b99f
- Size of remote file:
- 4.86 kB
- SHA256:
- 633d7f7902fb6eb4996920398e1367857d4c6bb99c657dc963b08f8d14d92d10
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