Instructions to use MLMvsCLM/1b-mlm20-42k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLMvsCLM/1b-mlm20-42k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MLMvsCLM/1b-mlm20-42k", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLMvsCLM/1b-mlm20-42k", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from MLMvsCLM/1b-mlm20-42k: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/MLMvsCLM/1b-mlm20-42k/resolve/main/tokenizer.json
- Command line
-
hf download hf://MLMvsCLM/1b-mlm20-42k/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/MLMvsCLM/1b-mlm20-42k/resolve/main/tokenizer.json
17.2 MB
- Xet hash:
- da070f8626ee36c29d81d14f7a18b0a943ba6477aaa86b433d0f865f98bf8392
- Size of remote file:
- 17.2 MB
- SHA256:
- 6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
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