Instructions to use nblinh63/1b8ab1e0-6218-4b84-9c24-f74892620058 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh63/1b8ab1e0-6218-4b84-9c24-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, "nblinh63/1b8ab1e0-6218-4b84-9c24-f74892620058") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1e083e16b1ce3ec2d9f8fee31fed4b4e61f2c188c4ae11c126aec0d685bb20c1
- Size of remote file:
- 73.9 MB
- SHA256:
- 4498f8b24ab3e58b555723a40a116d4cce6b9915319cda9367865edf3ad811ab
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