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:
- 940f545503f11d06df01b0dd32e81767b48eba4916989fb3b145208fed0a90e2
- Size of remote file:
- 168 MB
- SHA256:
- 5bb83e0505dd9072a16a1af3bc3246046c6f393d94e46b8fa97cecdd318fbf59
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.