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:
- 1fb6e69a5c23565ce033b0c9ab7a0b5dc7ae30870461c1e3316f3d473edc05d5
- Size of remote file:
- 34.4 MB
- SHA256:
- 3231accd2409b5d7d7e21909841f76c64199669185a042467a83c734a93c5ca0
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