Instructions to use eclec/test_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eclec/test_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eclec/test_trainer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eclec/test_trainer") model = AutoModelForSequenceClassification.from_pretrained("eclec/test_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download run-0/checkpoint-4/optimizer.pt from eclec/test_trainer: direct link, hf CLI and curl.
- Browser
- Download file 4.55 MB
-
https://huggingface.co/eclec/test_trainer/resolve/main/run-0/checkpoint-4/optimizer.pt
- Command line
-
hf download hf://eclec/test_trainer/run-0/checkpoint-4/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/eclec/test_trainer/resolve/main/run-0/checkpoint-4/optimizer.pt
4.55 MB
- Xet hash:
- b8f65b67739d5a14f918f4ed3c858049fa821c88d3507632430f1e442975e9d3
- Size of remote file:
- 4.55 MB
- SHA256:
- dde2c00413a7c0e17461346556bb319d422c50a9a39c3994d338c88d16074e95
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.