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:
- 04312cfc99df9248f7ee6665c3dcc361fa84aa7c226f7c850d19fd155873beb4
- Size of remote file:
- 9.92 GB
- SHA256:
- ceda57df9490b7f0bdbf85ad71814b1022cd641186395aeb5b34bba2a940ab72
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