Instructions to use nblinh63/a1389999-4055-4a93-bdd1-2efcc0bebbff with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh63/a1389999-4055-4a93-bdd1-2efcc0bebbff with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Nous-Hermes-2-Mistral-7B-DPO") model = PeftModel.from_pretrained(base_model, "nblinh63/a1389999-4055-4a93-bdd1-2efcc0bebbff") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1fe6f6874701d824aa2c5ca39a56a43998ccdd2e77c36e34ef8d33ea70a0a938
- Size of remote file:
- 168 MB
- SHA256:
- 227dbdd5dfcbf7b9b0d03aa590f0513ac746e0c3a96f7bdd88c88c4bb7d1052f
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