Instructions to use ALBADDAWI/ft-mistralai-Mistral-7B-Instruct-v0.2-qlora-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ALBADDAWI/ft-mistralai-Mistral-7B-Instruct-v0.2-qlora-v2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") model = PeftModel.from_pretrained(base_model, "ALBADDAWI/ft-mistralai-Mistral-7B-Instruct-v0.2-qlora-v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- beccbf0feffdf93639076b01a10a3828800cb1e28f4b6ec183fa612e8cfffed1
- Size of remote file:
- 4.98 kB
- SHA256:
- 4511e3a3a630a3732d336c0841dd0ef5c77b80265c7fb48d56624a95d853dbd6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.