Instructions to use nrsmac/test_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nrsmac/test_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nrsmac/test_trainer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nrsmac/test_trainer") model = AutoModel.from_pretrained("nrsmac/test_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nrsmac/test_trainer: direct link, hf CLI and curl.
- Browser
- Download file 265 MB
-
https://huggingface.co/nrsmac/test_trainer/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nrsmac/test_trainer/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nrsmac/test_trainer/resolve/main/pytorch_model.bin
265 MB
- Xet hash:
- 3cbe685f9984d1de3724cc0b6c6e36834a6295966140923ef15bb16fa7453715
- Size of remote file:
- 265 MB
- SHA256:
- fb6324bd570c81b64fce81ab244416f81f79ef5ebe85d611481802ca57af94d4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.