Instructions to use nblinh/d9d9d183-3847-4d82-a423-f5719375e8dc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh/d9d9d183-3847-4d82-a423-f5719375e8dc with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2-7B-Instruct") model = PeftModel.from_pretrained(base_model, "nblinh/d9d9d183-3847-4d82-a423-f5719375e8dc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0a68f189fc457778caeb5eb0d20de32fff026d464dc853f6591287bcc4a4c9a6
- Size of remote file:
- 162 MB
- SHA256:
- af5851cab993d6ba944fb74043c27d028bda135cecf0014044461459b4736c5a
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