Instructions to use nblinh/f7c7e844-5bfb-4d03-8f00-aebcbd671838 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh/f7c7e844-5bfb-4d03-8f00-aebcbd671838 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-14B-Instruct") model = PeftModel.from_pretrained(base_model, "nblinh/f7c7e844-5bfb-4d03-8f00-aebcbd671838") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1dcffa345a6e70243aaee3f27a07d47dfaf67b6ba62d681060954c0f3216b422
- Size of remote file:
- 6.78 kB
- SHA256:
- 664c3a300967daae0dcb9990ee381b1dd42fe992bc1553284c2eb3c410eacf3f
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