Instructions to use dzanbek/9c0ba83d-7ad1-4ee2-9ce3-e505d153c9e6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dzanbek/9c0ba83d-7ad1-4ee2-9ce3-e505d153c9e6 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Hermes-2-Theta-Llama-3-8B") model = PeftModel.from_pretrained(base_model, "dzanbek/9c0ba83d-7ad1-4ee2-9ce3-e505d153c9e6") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a341b767afe4954b0a7d5c9b2743f81ac3cea82a9eb30df84ac77eb2da4ac8b1
- Size of remote file:
- 336 MB
- SHA256:
- e15b6912a4065c7c0bb3f47a72099cb1e34a26c12a97605310bc29aa99042b35
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