Instructions to use DheerajNalapat/output_model_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DheerajNalapat/output_model_v2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.1-GPTQ") model = PeftModel.from_pretrained(base_model, "DheerajNalapat/output_model_v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fd6a1ad4f3e4441a32ea8828f277fd37823a34a1411eb6ef19ee7b1336af454d
- Size of remote file:
- 27.3 MB
- SHA256:
- eca25d16f1d4a73d87cf43405b37b6ea1cce0c3b89b9461b1e2ddfab89d17c73
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.