Instructions to use StonyBrookNLP/StarCoder2-LPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use StonyBrookNLP/StarCoder2-LPO with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigcode/starcoder2-7b") model = PeftModel.from_pretrained(base_model, "StonyBrookNLP/StarCoder2-LPO") - Notebooks
- Google Colab
- Kaggle
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Improve model card: Add pipeline tag, license, paper link, GitHub link, abstract, and sample usage
Verified signatureThis PR enhances the model card for `StarCoder2-LPO` by: * Adding the `pipeline_tag: text-generation` to the metadata, which improves discoverability on the Hugging Face Hub for code generation tasks. * Specifying the `license: apache-2.0`, aligning with the base model and common practice. * Updating the primary paper link to the Hugging Face Papers page: [Teaching an Old LLM Secure Coding: Localized Preference Optimization on Distilled Preferences](https://huggingface.co/papers/2506.00419). * Adding a direct link to the associated [GitHub repository](https://github.com/StonyBrookNLP/disco-lpo) for easier access to the code and further usage instructions. * Including the paper's abstract to provide immediate context about the model's research. * Adding a "Sample Usage" section that outlines how to perform inference using the `inference.py` script from the GitHub repository, providing clear, actionable steps as found in the original source. * Including the BibTeX citation…88a134d
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