Instructions to use Thomas-lemoullec/mistral_7b_vertexQandA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Thomas-lemoullec/mistral_7b_vertexQandA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1") model = PeftModel.from_pretrained(base_model, "Thomas-lemoullec/mistral_7b_vertexQandA") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2af4c7e41c1ae5f921a31ad59eb81afaa64c6989fe34131bf30f38a06b3ffd5d
- Size of remote file:
- 84 MB
- SHA256:
- 63a2a55b438256c396507687d2a39f0480b1c7acac2cd29c0118c73ce3dd8851
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.