Instructions to use msk18/finetuned-lora-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use msk18/finetuned-lora-code with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigcode/starcoderbase-1b") model = PeftModel.from_pretrained(base_model, "msk18/finetuned-lora-code") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f037d76afe1de1e671677bd7cc8fc1fb32e414ba099cd1221d40681c49cc4bd
- Size of remote file:
- 407 MB
- SHA256:
- 3f6918da987f741bc8bfeb9505a959a438af3ccb1d273f6118b546a292f8c1f2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.