Instructions to use LoftQ/Llama-2-13b-hf-bit4-rank64 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-rank64 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-rank64") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7874b83dd4df0d3c939d05cd8880fb761e6f7a586766af59cd27e11be9f9fefc
- Size of remote file:
- 9.93 GB
- SHA256:
- edb247086a549e2ffeedc7df017bd75985f636aa103712e67eb76bf23d570e48
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