Instructions to use DevQuasar/analytical_reasoning_Llama-3.2-1B_adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DevQuasar/analytical_reasoning_Llama-3.2-1B_adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.2-1B-bnb-4bit") model = PeftModel.from_pretrained(base_model, "DevQuasar/analytical_reasoning_Llama-3.2-1B_adapter") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6c0764d097e1fac1509be6bdfa5492fd5ecc81bd0348532fdda582bb5cd9818f
- Size of remote file:
- 17.2 MB
- SHA256:
- 76cfe2f054560aae896b2b75e273dc97a39e304d4ad19c44a9727a1d6b33c4cc
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