Instructions to use lambdasonly/miniLM-L12-v2-WCSL-multilang-pretrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lambdasonly/miniLM-L12-v2-WCSL-multilang-pretrained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="lambdasonly/miniLM-L12-v2-WCSL-multilang-pretrained")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("lambdasonly/miniLM-L12-v2-WCSL-multilang-pretrained") model = AutoModel.from_pretrained("lambdasonly/miniLM-L12-v2-WCSL-multilang-pretrained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from lambdasonly/miniLM-L12-v2-WCSL-multilang-pretrained: direct link, hf CLI and curl.
- Browser
- Download file 471 MB
-
https://huggingface.co/lambdasonly/miniLM-L12-v2-WCSL-multilang-pretrained/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://lambdasonly/miniLM-L12-v2-WCSL-multilang-pretrained/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/lambdasonly/miniLM-L12-v2-WCSL-multilang-pretrained/resolve/main/pytorch_model.bin
471 MB
- Xet hash:
- 4c6ff1c92385a9adf45d33fe861a49587dcfc2c2f4f42689676f65c312fabd33
- Size of remote file:
- 471 MB
- SHA256:
- 2c6ac281e4a9b3db23fa63a21d51c7591f44b330f3b38a2f9dedd7a9988d724f
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