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:
- fd3991ac7577708c68b1e32a8186a52fe0740dc5fc2ca6f36e44981c71791849
- Size of remote file:
- 6.78 kB
- SHA256:
- 8701fc7dbe311df0f3f3e72a6dfebb64ffa83700c3efdd4d36962bddb6c5b7e6
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