Transformers
JAX
Safetensors
English
t5
text2text-generation
biomedical
clinical
ul2
encoder-decoder
pretraining
medical
text-generation-inference
Instructions to use Siddharth63/medul2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Siddharth63/medul2-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Siddharth63/medul2-base") model = AutoModelForSeq2SeqLM.from_pretrained("Siddharth63/medul2-base") - Notebooks
- Google Colab
- Kaggle
| import argparse | |
| from transformers import T5ForConditionalGeneration, TFT5ForConditionalGeneration | |
| def main(args): | |
| pt_model = T5ForConditionalGeneration.from_pretrained(args.model_dir, from_flax=True) | |
| pt_model.save_pretrained(args.model_dir) | |
| tf_model = TFT5ForConditionalGeneration.from_pretrained(args.model_dir, from_pt=True) | |
| tf_model.save_pretrained(args.model_dir) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--model_dir', type=str, default='.') |