Instructions to use Aybars/ModelOnWhole with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aybars/ModelOnWhole with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="Aybars/ModelOnWhole")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Aybars/ModelOnWhole") model = AutoModelForQuestionAnswering.from_pretrained("Aybars/ModelOnWhole", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
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.43b72ae
-
Upload config.json
fd356c8 -
Upload nbest_predictions_.json
16262d7 -
Upload null_odds_.json
8826064 -
Upload predictions_.json
76bfee4 -
Upload tokenizer_config.json
e3fea33 -
Upload special_tokens_map.json
6b3e88f -
Upload training_args.bin with git-lfs
fd5b789 -
Upload vocab.txt
c39ff7b -
Upload pytorch_model.bin with git-lfs
7116e46 -
initial commit
d0a8398