Instructions to use STAM/agricore with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use STAM/agricore with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("STAM/agricore") model = AutoModelForSeq2SeqLM.from_pretrained("STAM/agricore", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model
This is an automated PR created with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions Feel free to ignore this PR.46d9707
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upload complete model text2sparql for Agricore
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These are the files related to the new T5 tokenizer trained for the purposes of Agricored3b2341 -
Upload new T5 tokenizer config file
These are the files related to the new T5 tokenizer trained for the purposes of Agricore4849a7a -
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These are the files related to the new T5 tokenizer trained for the purposes of Agricore94972e3 -
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