Instructions to use LDD/MLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LDD/MLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LDD/MLM")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LDD/MLM") model = AutoModel.from_pretrained("LDD/MLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from LDD/MLM: direct link, hf CLI and curl.
- Browser
- Download file 409 MB
-
https://huggingface.co/LDD/MLM/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://LDD/MLM@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/LDD/MLM/resolve/refs%2Fpr%2F1/pytorch_model.bin
409 MB
- Xet hash:
- c3420a9d07b75fe4cb2e2af5aa81a9ce3f0a7a5242fa47763d4ccb9f90bf52e8
- Size of remote file:
- 409 MB
- SHA256:
- c1ee1a5002bb820b556997bda988d7edc146d13d4a78ce2cdd61612683456fbe
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