Instructions to use Hartunka/distilbert_rand_100_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/distilbert_rand_100_v2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, DistilBertForLDAMaskedLM tokenizer = AutoTokenizer.from_pretrained("Hartunka/distilbert_rand_100_v2") model = DistilBertForLDAMaskedLM.from_pretrained("Hartunka/distilbert_rand_100_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hartunka/distilbert_rand_100_v2: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/Hartunka/distilbert_rand_100_v2/resolve/main/training_args.bin
- Command line
-
hf download hf://Hartunka/distilbert_rand_100_v2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hartunka/distilbert_rand_100_v2/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- 8b605e4833c1c09796ab0d1ee2f7717285288a30a852fa8cd0c2be852ef247a5
- Size of remote file:
- 5.43 kB
- SHA256:
- a639ed7c460f199869f774f87cf275bfdd4897aaf5db207af240ec87609e9704
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