Instructions to use Jnx03/kanitakorn-260614-mnemo2c-mnemo2c-best180-v9target-step40 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Jnx03/kanitakorn-260614-mnemo2c-mnemo2c-best180-v9target-step40 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-Nemo-Instruct-2407") model = PeftModel.from_pretrained(base_model, "Jnx03/kanitakorn-260614-mnemo2c-mnemo2c-best180-v9target-step40") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d3160ae1b978af3a00dca984c89a8395d92dae13acc58477ee6caa88cb1336bd
- Size of remote file:
- 5.71 kB
- SHA256:
- c92a9b77ac66c943c1bfb4d20df94311feca25ca06281d4f44457f5f532f3683
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.