Instructions to use StonyBrookNLP/StarCoder2-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use StonyBrookNLP/StarCoder2-SFT 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-SFT") - Notebooks
- Google Colab
- Kaggle
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Improve model card: Add pipeline tag, license, GitHub link, abstract, and sample usage
Verified signatureThis PR significantly enhances the model card for the `StarCoder2-SFT` adapter by: * Adding the `pipeline_tag: text-generation` to improve discoverability on the Hugging Face Hub (e.g., at https://huggingface.co/models?pipeline_tag=text-generation). * Specifying the `license: other` as no explicit license was found in the repository or paper. * Including the `tags: - code-generation` to further categorize the model accurately. * Updating the paper link to the official Hugging Face paper page: https://huggingface.co/papers/2506.00419. * Adding a direct link to the associated GitHub repository: https://github.com/StonyBrookNLP/disco-lpo. * Incorporating the paper's abstract to provide immediate context about the model's purpose and methodology. * Adding a "Usage" section with a Python code snippet that demonstrates how to load this PEFT adapter with its base model and perform text generation using the `transformers` and `peft` libraries. * Including the BibTeX citation f…50072b7
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