Instructions to use Sahajtomar/GBERTQnA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sahajtomar/GBERTQnA 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="Sahajtomar/GBERTQnA")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Sahajtomar/GBERTQnA") model = AutoModelForQuestionAnswering.from_pretrained("Sahajtomar/GBERTQnA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Sahajtomar/GBERTQnA: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/Sahajtomar/GBERTQnA/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Sahajtomar/GBERTQnA/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Sahajtomar/GBERTQnA/resolve/main/flax_model.msgpack
1.34 GB
- Xet hash:
- a82b9ea663073c4d3372ac7fa3ada9b5ca0b5d78f9178d3bd796f86f25079f4d
- Size of remote file:
- 1.34 GB
- SHA256:
- 3793fdd873d5854046a3a18016898249bb4896dcd1fd67d359b5e67975438610
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.