Instructions to use WesleySantos/mistral-finetuned-samsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use WesleySantos/mistral-finetuned-samsum 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, "WesleySantos/mistral-finetuned-samsum") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6df1bd39cd9e42d2e520d3d7b03f11003cd2405200bda9346f4bece4eaa68a30
- Size of remote file:
- 9.98 GB
- SHA256:
- 7baa092afda7575c3c0b5b1b2e880c97f97e573dcde0a304638d53282719d593
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.