Instructions to use routellm/bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use routellm/bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="routellm/bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("routellm/bert") model = AutoModelForSequenceClassification.from_pretrained("routellm/bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from routellm/bert: direct link, hf CLI and curl.
- Browser
- Download file 2.22 GB
-
https://huggingface.co/routellm/bert/resolve/main/optimizer.pt
- Command line
-
hf download hf://routellm/bert/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/routellm/bert/resolve/main/optimizer.pt
2.22 GB
- Xet hash:
- cb484e6f646a1e544c0a4836a6ad87fcd00e04d5a200fa44a2c597affc237874
- Size of remote file:
- 2.22 GB
- SHA256:
- 506c884738e38be7fd3a95f09cb8d898455c67724a5df86dc654cb11ae7e57c1
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