Instructions to use thenlpresearcher/google_gemma-2-9b_StereoDetect_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use thenlpresearcher/google_gemma-2-9b_StereoDetect_Model with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("google/gemma-2-9b") model = PeftModel.from_pretrained(base_model, "thenlpresearcher/google_gemma-2-9b_StereoDetect_Model") - Transformers
How to use thenlpresearcher/google_gemma-2-9b_StereoDetect_Model with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("thenlpresearcher/google_gemma-2-9b_StereoDetect_Model", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 55c21a409ac013c409164ec1243cc19ec4c40e9dd1f971c7065aa438bd41a84f
- Size of remote file:
- 34.4 MB
- SHA256:
- abd905f70a0604065e2115e2e99a985ae7f6c42a528cb9e0ac42c1178c6fc151
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.