Instructions to use vctmk/mantis-8b-idefics2-classification-tedED-8g_4096_regression with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vctmk/mantis-8b-idefics2-classification-tedED-8g_4096_regression with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vctmk/mantis-8b-idefics2-classification-tedED-8g_4096_regression")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("vctmk/mantis-8b-idefics2-classification-tedED-8g_4096_regression") model = AutoModelForSequenceClassification.from_pretrained("vctmk/mantis-8b-idefics2-classification-tedED-8g_4096_regression", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from vctmk/mantis-8b-idefics2-classification-tedED-8g_4096_regression: direct link, hf CLI and curl.
- Browser
- Download file 6.71 kB
-
https://huggingface.co/vctmk/mantis-8b-idefics2-classification-tedED-8g_4096_regression/resolve/main/training_args.bin
- Command line
-
hf download hf://vctmk/mantis-8b-idefics2-classification-tedED-8g_4096_regression/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/vctmk/mantis-8b-idefics2-classification-tedED-8g_4096_regression/resolve/main/training_args.bin
6.71 kB
- Xet hash:
- 9ca2698cd418f7bb86e2bf7c62465a17ff8312d99d681674faa465445ca0127c
- Size of remote file:
- 6.71 kB
- SHA256:
- c0494c6d0f196b1b40fae5f61a0c41c131e3b6a2cab5deb3e37d80b57914720e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.