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:
- bda3bcb3fb482958ecd067f95082c378d80653532655e925c676a6311b87b391
- Size of remote file:
- 9.97 GB
- SHA256:
- 7d1cb88dd0b432c8ea8b4ca105c7bf758675a8a792dd07e03244c17d1b4bb43c
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