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:
- 3c7885e493623c8edb75a30958fc80388f2bad9c5182decc7c3b1cd5e779a429
- Size of remote file:
- 9.94 GB
- SHA256:
- e7ad0a8cec1ca8a723ba346bd53ae11db86c0e045222e11b4c73714d0a8af31a
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