Instructions to use JMwagunda/GIR-ENG-MODEL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JMwagunda/GIR-ENG-MODEL with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("JMwagunda/GIR-ENG-MODEL") model = AutoModelForSeq2SeqLM.from_pretrained("JMwagunda/GIR-ENG-MODEL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from JMwagunda/GIR-ENG-MODEL: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/JMwagunda/GIR-ENG-MODEL/resolve/main/training_args.bin
- Command line
-
hf download hf://JMwagunda/GIR-ENG-MODEL/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/JMwagunda/GIR-ENG-MODEL/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- 00dd91b43a7486b197115fadcea5f84246b4a778642bdc36f063fa4c964c5a0c
- Size of remote file:
- 5.43 kB
- SHA256:
- 07c198d6f59c67a2815ffb6098b36a17ea2c338e374404eb5f136bc515dd546f
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