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:
- 6b01783363d7db43f2ea00cb767c6b330d4f87077f580d19eb3c5d222fe73784
- Size of remote file:
- 9.77 GB
- SHA256:
- f801b4e4f2d27b50c39c0e7fc3c11f51583f45b05d914eb4a8471aa6a444c915
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