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:
- 301905dd2a73e8ffca1efb73635bf5780217d860f4d3c140c03ec99294610c9b
- Size of remote file:
- 2.27 GB
- SHA256:
- ba0299cb0967ff217e9ed1fd19d78ee5bc87d33305422a13ca40750a6d4650c7
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