Instructions to use MLMvsCLM/610m-mlm40-42k-5000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLMvsCLM/610m-mlm40-42k-5000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MLMvsCLM/610m-mlm40-42k-5000", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLMvsCLM/610m-mlm40-42k-5000", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from MLMvsCLM/610m-mlm40-42k-5000: direct link, hf CLI and curl.
- Browser
- Download file 3.02 GB
-
https://huggingface.co/MLMvsCLM/610m-mlm40-42k-5000/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://MLMvsCLM/610m-mlm40-42k-5000/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MLMvsCLM/610m-mlm40-42k-5000/resolve/main/pytorch_model.bin
3.02 GB
- Xet hash:
- 3a1a609ea4cf466ec381589507bcb3a19715d8e72b5947541e4668f52c092c7b
- Size of remote file:
- 3.02 GB
- SHA256:
- e8f3f051850539c4d94d63ab0ef5ee1dfe28dec2ffedd8e3a7f5b4e37dc73bf9
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