Instructions to use mlabonne/codellama-2-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mlabonne/codellama-2-7b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf") model = PeftModel.from_pretrained(base_model, "mlabonne/codellama-2-7b") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1236de3a1ad18379f73f8b9f8294fbf91eb46ecbd3ae43ff2fe909a3742c3d2d
- Size of remote file:
- 4.03 kB
- SHA256:
- 19340d0437a993f30d41325d9a705673fbaff1205cfc4eb10a779716c6961e89
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.