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 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/model.zip
- Command line
-
hf download hf://harshil10/tiny_bert_flax/model.zip
-
curl -L -o model.zip https://huggingface.co/harshil10/tiny_bert_flax/resolve/main/model.zip
16.3 MB
- Xet hash:
- f615459d6a00ca7f7b20277a83a2ffa70c248f37e1b668bc18ba931b08d8fdef
- Size of remote file:
- 16.3 MB
- SHA256:
- 73a283022b2553b466662c883cc362832862f69c5be8bee62ae9c629ea66c6eb
路
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