Instructions to use dilanjt/java-javascript_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dilanjt/java-javascript_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dilanjt/java-javascript_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dilanjt/java-javascript_model") model = AutoModelForMaskedLM.from_pretrained("dilanjt/java-javascript_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from dilanjt/java-javascript_model: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/dilanjt/java-javascript_model/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://dilanjt/java-javascript_model@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/dilanjt/java-javascript_model/resolve/refs%2Fpr%2F1/model.safetensors
499 MB
- Xet hash:
- 605f5d80581920cfd9ae06c54b517202106877e1ae0014744c8d739a9d6b5341
- Size of remote file:
- 499 MB
- SHA256:
- e4c34e48b9b21a557cc79d883ecd547ce2b3dfff32d0fbd44240ed5d4be692a5
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