Instructions to use prajwalJumde/rap_phase2_26march_custom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prajwalJumde/rap_phase2_26march_custom with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="prajwalJumde/rap_phase2_26march_custom")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("prajwalJumde/rap_phase2_26march_custom") model = AutoModelForQuestionAnswering.from_pretrained("prajwalJumde/rap_phase2_26march_custom", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 440755b3f9f0b6fb89066fcc244386c2d5b3721f177309fba119c2c3dc35a4c3
- Size of remote file:
- 1.11 GB
- SHA256:
- bf4bc03efb4a123f2c7c3121f006ad3b610d6e35e25d98f89bf29da90f4f307e
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.