Instructions to use MLMvsCLM/610m-mlm40-12k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLMvsCLM/610m-mlm40-12k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MLMvsCLM/610m-mlm40-12k", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLMvsCLM/610m-mlm40-12k", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from MLMvsCLM/610m-mlm40-12k: direct link, hf CLI and curl.
- Browser
- Download file 3.02 GB
-
https://huggingface.co/MLMvsCLM/610m-mlm40-12k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://MLMvsCLM/610m-mlm40-12k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MLMvsCLM/610m-mlm40-12k/resolve/main/pytorch_model.bin
3.02 GB
- Xet hash:
- 0d522abd18ee0e9877aaccac025d9e1d0c9320e5f65569622aa53586ba5fd56d
- Size of remote file:
- 3.02 GB
- SHA256:
- a931694b0b23c63a0dce82c9ac8b7b8a2986782a8e4b3f6219977912b5893d2b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.