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:
- d642fc639ab61f1b22141f5aad030d4ca5f148b9e4447e408fd00ab2bb716a81
- Size of remote file:
- 11.7 MB
- SHA256:
- fca0aa1396568c670f756379031e5f2bdfba98de8ff1f525365d17120632dbb2
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