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:
- 06bb1aacec525dd5621963b74c0eb5afeecd52567f9a8f9cdd4a0b84da1045e6
- Size of remote file:
- 6.78 kB
- SHA256:
- 70105660166d6a42d60860a0499aad651b5cda7e258fa87475cd9bb02a89cdbf
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