Instructions to use nblinh63/d7dec48d-6963-4941-a666-82afe3a01311 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh63/d7dec48d-6963-4941-a666-82afe3a01311 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-0.5B-Chat") model = PeftModel.from_pretrained(base_model, "nblinh63/d7dec48d-6963-4941-a666-82afe3a01311") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ccbe48aa99d40c53d6f211410da0c7a64d9b1896d9f75bed133a6cd1731a040
- Size of remote file:
- 30.3 MB
- SHA256:
- 0543ccadb5656ef2496bd68bb10a1771d2cf5d84aaadd7f90c26a65e1646e41d
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