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:
- eba6148bf8e7d7d519fffbdc9658860ce7ba10c80e04ec24502ae3e8c2321255
- Size of remote file:
- 2.27 GB
- SHA256:
- a7522fe208e374cf8605ef7f30d8fce7c1ad94732a5591b9a3c1e2678d40065c
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