Instructions to use nblinh/1b8ab1e0-6218-4b84-9c24-f74892620058 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh/1b8ab1e0-6218-4b84-9c24-f74892620058 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-1.5B") model = PeftModel.from_pretrained(base_model, "nblinh/1b8ab1e0-6218-4b84-9c24-f74892620058") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 00a28cc1140e7beb747efdfd42df729732323988b2835b5838585405a2ef9a36
- Size of remote file:
- 6.78 kB
- SHA256:
- c01346c91642362ce2c04a39b8acb015911063b2623d050c9369ebe112a64052
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