Instructions to use lindeberg/LaMini-T5-61M_optimized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lindeberg/LaMini-T5-61M_optimized with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("lindeberg/LaMini-T5-61M_optimized") model = AutoModelForSeq2SeqLM.from_pretrained("lindeberg/LaMini-T5-61M_optimized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download encoder_model_optimized.onnx from lindeberg/LaMini-T5-61M_optimized: direct link, hf CLI and curl.
- Browser
- Download file 141 MB
-
https://huggingface.co/lindeberg/LaMini-T5-61M_optimized/resolve/main/encoder_model_optimized.onnx
- Command line
-
hf download hf://lindeberg/LaMini-T5-61M_optimized/encoder_model_optimized.onnx
-
curl -L -o encoder_model_optimized.onnx https://huggingface.co/lindeberg/LaMini-T5-61M_optimized/resolve/main/encoder_model_optimized.onnx
141 MB
- Xet hash:
- 811650fa447bbd716cefb448066d242197712374f9cb1f7d89df7a7642af68af
- Size of remote file:
- 141 MB
- SHA256:
- c80115074ec91ffa8e2a6250f121ac2e1d4d41251f0cd9a1230aa40d0bb776ab
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