Instructions to use nblinh/1b35d621-62b6-4c43-b3a4-f9f8deabfef0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh/1b35d621-62b6-4c43-b3a4-f9f8deabfef0 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/tinyllama") model = PeftModel.from_pretrained(base_model, "nblinh/1b35d621-62b6-4c43-b3a4-f9f8deabfef0") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e0fc28fc9e021d1d73d86d0d06b0f27ab27691f5594dc02bfe39ccecc2bd2902
- Size of remote file:
- 50.6 MB
- SHA256:
- 25e612d1a22a023b7eed32285da0ca206a8e7f364968ff692b55b10373a0d38c
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