Instructions to use LoftQ/Llama-2-13b-hf-bit4-rank32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use LoftQ/Llama-2-13b-hf-bit4-rank32 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf") model = PeftModel.from_pretrained(base_model, "LoftQ/Llama-2-13b-hf-bit4-rank32") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2685323b8f081b4b296292c758283cf6b45e39706ae43ded9612c764ef350fcc
- Size of remote file:
- 9.97 GB
- SHA256:
- 8c9a1888367e06736fa40b55420a8d6e7f5a30c7bf5a0d6651d0e1f779e40cef
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