Instructions to use Fsoft-AIC/dopamin-java-usage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fsoft-AIC/dopamin-java-usage with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Fsoft-AIC/dopamin-java-usage")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Fsoft-AIC/dopamin-java-usage") model = AutoModelForSequenceClassification.from_pretrained("Fsoft-AIC/dopamin-java-usage", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from Fsoft-AIC/dopamin-java-usage: direct link, hf CLI and curl.
- Browser
- Download file 612 MB
-
https://huggingface.co/Fsoft-AIC/dopamin-java-usage/resolve/main/model.safetensors
- Command line
-
hf download hf://Fsoft-AIC/dopamin-java-usage/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Fsoft-AIC/dopamin-java-usage/resolve/main/model.safetensors
612 MB
- Xet hash:
- 86ee37b6175cd9500d336fbe982bfeb822f0cbf706faa20725097a963a98a44b
- Size of remote file:
- 612 MB
- SHA256:
- b119bee0b21eb128e241135f056a6f5d4539a8d082d71cec4c3336ab30476a0a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.