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:
- 9182cf8f1ceaa9d13e1ab3ed8c45f50e30f63a6ec89d30bd741b11cbe657d163
- Size of remote file:
- 74 MB
- SHA256:
- 880f83aba02608965df3913db691df649458fd8da04bd36dec397640e6062381
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