Instructions to use nblinh63/b67ec381-83ab-4b09-aa03-c8300fb17428 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh63/b67ec381-83ab-4b09-aa03-c8300fb17428 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2") model = PeftModel.from_pretrained(base_model, "nblinh63/b67ec381-83ab-4b09-aa03-c8300fb17428") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4dcbb1eefdb0e3fec8e6bcf58dacfcce24a8628c86125ff6037f057da754a616
- Size of remote file:
- 6.78 kB
- SHA256:
- 3810ccc954dc6ced09eb2f9d83326b01ea7da870449abdcd615e4d36be12bb2c
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