Instructions to use ShynBui/s23 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ShynBui/s23 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="ShynBui/s23")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("ShynBui/s23") model = AutoModelForQuestionAnswering.from_pretrained("ShynBui/s23", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ShynBui/s23: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/ShynBui/s23/resolve/main/training_args.bin
- Command line
-
hf download hf://ShynBui/s23/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ShynBui/s23/resolve/main/training_args.bin
3.96 kB
- Xet hash:
- 7b8ee24bc8579cd1ab7159db140badec1dc4ac9a682e1d39303da4ead3addb27
- Size of remote file:
- 3.96 kB
- SHA256:
- 93f5a8a979f867e45ee1f243c8db8426676963dec404ad6029e23c374423cd44
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.