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
metadata
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