Instructions to use ravi-huggingface/test_dir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ravi-huggingface/test_dir with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base") model = PeftModel.from_pretrained(base_model, "ravi-huggingface/test_dir") - Transformers
How to use ravi-huggingface/test_dir with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ravi-huggingface/test_dir", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0912a90590e9af4ef1afb37bac5dd48149e2033a8d63aa957a263027f4651694
- Size of remote file:
- 3.56 MB
- SHA256:
- 941fe34dcc172b6884fb03d63a52f36b3498f17e8e082bfce718dc9e16fc2bca
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