Instructions to use LRJ1981/RITO_Version1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LRJ1981/RITO_Version1.0 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("LRJ1981/RITO_Version1.0") model = AutoModelForSeq2SeqLM.from_pretrained("LRJ1981/RITO_Version1.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from LRJ1981/RITO_Version1.0: direct link, hf CLI and curl.
- Browser
- Download file 406 Bytes
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https://huggingface.co/LRJ1981/RITO_Version1.0/resolve/main/README.md
- Command line
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hf download hf://LRJ1981/RITO_Version1.0/README.md
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curl -L -o README.md https://huggingface.co/LRJ1981/RITO_Version1.0/resolve/main/README.md
406 Bytes
| tags: | |
| - autotrain | |
| - text2text-generation | |
| widget: | |
| - text: "I love AutoTrain" | |
| datasets: | |
| - autotrain-9t83i-0umcp/autotrain-data | |
| # Model Trained Using AutoTrain | |
| - Problem type: Seq2Seq | |
| ## Validation Metrics | |
| loss: 0.003461688058450818 | |
| rouge1: 99.738 | |
| rouge2: 99.6362 | |
| rougeL: 99.7372 | |
| rougeLsum: 99.739 | |
| gen_len: 127.7143 | |
| runtime: 37.8429 | |
| samples_per_second: 2.59 | |
| steps_per_second: 0.661 | |
| : 7.0 | |