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:
- 56cde6719f196cbf040ecd9b0a5341b93faf9d7a29580c5d16ec13002f9828c5
- Size of remote file:
- 9.93 GB
- SHA256:
- b10f05eff2b5e063386b986ec87928c0d997815f0c6d062c7b561dca887bbbbc
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