Instructions to use Vijayendra/llama3-8b-lora-cyclic-attention with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Vijayendra/llama3-8b-lora-cyclic-attention with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Vijayendra/llama3-8b-lora-cyclic-attention") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6e54b28e6088da4c2f384d5c7358a21ce1400965714c0550bb2a1ec21794e10b
- Size of remote file:
- 5.87 GB
- SHA256:
- eb3e04e2dd1c558db1c11ab1005e92abe78852f67a0eb3064c21d36929240bd3
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