Instructions to use Fsoft-AIC/dopamin-python-developmentnotes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fsoft-AIC/dopamin-python-developmentnotes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Fsoft-AIC/dopamin-python-developmentnotes")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Fsoft-AIC/dopamin-python-developmentnotes") model = AutoModelForSequenceClassification.from_pretrained("Fsoft-AIC/dopamin-python-developmentnotes", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from Fsoft-AIC/dopamin-python-developmentnotes: direct link, hf CLI and curl.
- Browser
- Download file 612 MB
-
https://huggingface.co/Fsoft-AIC/dopamin-python-developmentnotes/resolve/main/model.safetensors
- Command line
-
hf download hf://Fsoft-AIC/dopamin-python-developmentnotes/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Fsoft-AIC/dopamin-python-developmentnotes/resolve/main/model.safetensors
612 MB
- Xet hash:
- 604dba90399b954e9532ea1e6333497483e36adc2e4e1a64ab4044dfbc610c97
- Size of remote file:
- 612 MB
- SHA256:
- ccd7f03f3f7138d596dc0ce02b8c7eb162cce8a08666202f2d9151e6bb16cb8e
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