Instructions to use BayesTensor/modernbert_seeker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BayesTensor/modernbert_seeker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BayesTensor/modernbert_seeker")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BayesTensor/modernbert_seeker") model = AutoModelForSequenceClassification.from_pretrained("BayesTensor/modernbert_seeker", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from BayesTensor/modernbert_seeker: direct link, hf CLI and curl.
- Browser
- Download file 1.2 GB
-
https://huggingface.co/BayesTensor/modernbert_seeker/resolve/main/optimizer.pt
- Command line
-
hf download hf://BayesTensor/modernbert_seeker/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/BayesTensor/modernbert_seeker/resolve/main/optimizer.pt
1.2 GB
- Xet hash:
- e429c245d493452c8e14ba497d3445074b20127c700799d33eed691403957a01
- Size of remote file:
- 1.2 GB
- SHA256:
- 93a2cd2ffdeb33f31515c6d1503fdcbab2c355b120aa38e58e6900180d66de4d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.