Instructions to use feedback-to-code/swe-diff-llama-3-8B-Instruct-First with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use feedback-to-code/swe-diff-llama-3-8B-Instruct-First with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b-Instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "feedback-to-code/swe-diff-llama-3-8B-Instruct-First") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b05ef304739d8c7272250f55981ca969542a1c9ade840cb8832368885fb12c65
- Size of remote file:
- 168 MB
- SHA256:
- 1a3b8cfcf72ed139d60518d3d944b05314a374c653ae51a1711fcaa78df25d9f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.