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:
- 4e123734fa8615937594682d63403f7de897fdecbf2b5ee1f9c4050df6121908
- Size of remote file:
- 9.77 GB
- SHA256:
- 7b3a4a2623f3dcc887ae957269aa799af05188a23fe9da700d2818dbb657d517
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