Instructions to use harshil10/tiny_bert_flax with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harshil10/tiny_bert_flax 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="harshil10/tiny_bert_flax")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("harshil10/tiny_bert_flax") model = AutoModelForQuestionAnswering.from_pretrained("harshil10/tiny_bert_flax", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.zip from harshil10/tiny_bert_flax: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/harshil10/tiny_bert_flax/resolve/main/pytorch_model.zip
- Command line
-
hf download hf://harshil10/tiny_bert_flax/pytorch_model.zip
-
curl -L -o pytorch_model.zip https://huggingface.co/harshil10/tiny_bert_flax/resolve/main/pytorch_model.zip
16.3 MB
- Xet hash:
- 3f2edb7ba54cc47e5edcadcc583f6fea4c7d63e5875337b5387c221e36675843
- Size of remote file:
- 16.3 MB
- SHA256:
- bcc403a1c3fd5a8e2093dff046f6f35ba7a454ce5bf1b8e1ad8d0f697e0c6bd8
路
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