Instructions to use trangtrannnnn/157b4cb8-2091-4cbe-a7ed-a1fcefb36e7c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use trangtrannnnn/157b4cb8-2091-4cbe-a7ed-a1fcefb36e7c with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/mistral-7b-v0.3") model = PeftModel.from_pretrained(base_model, "trangtrannnnn/157b4cb8-2091-4cbe-a7ed-a1fcefb36e7c") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 427016af54f1a0e96acd8f31e4f34d50d1c287eefa0a299514cb7ff2b04fc763
- Size of remote file:
- 83.9 MB
- SHA256:
- 12f425431a1ef6ddb5fca57926d340e2213d50ce9c964375724b9abd429d95ce
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.