Instructions to use warrior1127/my_awesome_qa_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use warrior1127/my_awesome_qa_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="warrior1127/my_awesome_qa_model")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("warrior1127/my_awesome_qa_model") model = AutoModelForQuestionAnswering.from_pretrained("warrior1127/my_awesome_qa_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from warrior1127/my_awesome_qa_model: direct link, hf CLI and curl.
- Browser
- Download file 3.39 kB
-
https://huggingface.co/warrior1127/my_awesome_qa_model/resolve/main/training_args.bin
- Command line
-
hf download hf://warrior1127/my_awesome_qa_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/warrior1127/my_awesome_qa_model/resolve/main/training_args.bin
3.39 kB
- Xet hash:
- 1220ac2137b07bdf4b6a97d895dedc1a40145fc0c49686521785d32bf2844a4a
- Size of remote file:
- 3.39 kB
- SHA256:
- 3d003a38cc2689a39c8e433f11173898f294b8de8a3a5290fbd23c83f8206ff1
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.