Instructions to use hiyouga/Llama-2-70b-AQLM-2Bit-QLoRA-function-calling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use hiyouga/Llama-2-70b-AQLM-2Bit-QLoRA-function-calling with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("BlackSamorez/Llama-2-70b-AQLM-2Bit-1x16-hf") model = PeftModel.from_pretrained(base_model, "hiyouga/Llama-2-70b-AQLM-2Bit-QLoRA-function-calling") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2736830ce46e9ad40ff3f8b523591060c5cdb15a4d6a1d6bc15f5bcafb8c8c7a
- Size of remote file:
- 5.11 kB
- SHA256:
- bf8955447713682dea96b55ed6553542f208fe798191ff47e302b2670f37f689
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.