Instructions to use Devenvaruv/Grad_Only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Devenvaruv/Grad_Only 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="Devenvaruv/Grad_Only")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Devenvaruv/Grad_Only") model = AutoModelForQuestionAnswering.from_pretrained("Devenvaruv/Grad_Only", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from Devenvaruv/Grad_Only: direct link, hf CLI and curl.
- Browser
- Download file 266 MB
-
https://huggingface.co/Devenvaruv/Grad_Only/resolve/main/tf_model.h5
- Command line
-
hf download hf://Devenvaruv/Grad_Only/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/Devenvaruv/Grad_Only/resolve/main/tf_model.h5
266 MB
- Xet hash:
- a9d1b4d3bb96780400bdd1b793f2421dfb62c36e15fd678594085f33f758058a
- Size of remote file:
- 266 MB
- SHA256:
- a6121312a5401fc1176f8046ba4fca45c3d6fd7279284e36f5ae1027f52a69d4
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.