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 training_args.bin from harshil10/tiny_bert_flax: direct link, hf CLI and curl.
- Browser
- Download file 1.49 kB
-
https://huggingface.co/harshil10/tiny_bert_flax/resolve/main/training_args.bin
- Command line
-
hf download hf://harshil10/tiny_bert_flax/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/harshil10/tiny_bert_flax/resolve/main/training_args.bin
1.49 kB
- Xet hash:
- 60b0c51e9d4efff335f704da13aed3a157c2db36675f1bd6290f9788563d3dea
- Size of remote file:
- 1.49 kB
- SHA256:
- 2c26310a645b2de935c72b13d46fc84f6ad0ac3dff8f756e1202d86a0b4857e2
路
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