Instructions to use michaelc0des/gemma-direct-commentary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use michaelc0des/gemma-direct-commentary with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5gemma-b-b-prefixlm-it") model = PeftModel.from_pretrained(base_model, "michaelc0des/gemma-direct-commentary") - Transformers
How to use michaelc0des/gemma-direct-commentary with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("michaelc0des/gemma-direct-commentary", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eed65b48268308782fd6718519880047ef9efe591b4a6a4642af4844eee9a46b
- Size of remote file:
- 5.39 kB
- SHA256:
- 29fefc9f9b9914aa02d95670feacb3b929e4ae8a6483affb7ec96e7c7d06bee4
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