Instructions to use KRayRay/my_awesome_qa_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KRayRay/my_awesome_qa_model 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="KRayRay/my_awesome_qa_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("KRayRay/my_awesome_qa_model") model = AutoModelForQuestionAnswering.from_pretrained("KRayRay/my_awesome_qa_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KRayRay/my_awesome_qa_model: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/KRayRay/my_awesome_qa_model/resolve/main/training_args.bin
- Command line
-
hf download hf://KRayRay/my_awesome_qa_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KRayRay/my_awesome_qa_model/resolve/main/training_args.bin
4.6 kB
- Xet hash:
- 864ee57be6162750d9d51d55730ffdc226b00fc7a773689d16b46f37bfe20b8c
- Size of remote file:
- 4.6 kB
- SHA256:
- b8d6133174a47c26e93b92ce6b1a29d1eb8017a52c863569a604784d5b3c4b34
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.