Instructions to use Abid-Shahriar/BugSum-Qwen-7B-Distill with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Abid-Shahriar/BugSum-Qwen-7B-Distill with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct") model = PeftModel.from_pretrained(base_model, "Abid-Shahriar/BugSum-Qwen-7B-Distill") - Notebooks
- Google Colab
- Kaggle
BugSum-Qwen-7B-Distill
This is an experimental self-distillation LoRA variant of BugSum-Qwen-7B for concise English bug-report summarization. It was trained by Md. Abid Shahriar during thesis research.
The repository contains an adapter, not a standalone model. Load it on Qwen/Qwen2.5-Coder-7B-Instruct with PEFT. The training corpus is not included because its source datasets have separate access and licensing conditions.
Method
The experiment used oracle-style self-distillation with supervised negative log-likelihood training on 253 selected examples for three epochs. It started from the supervised BugSum adapter.
The thesis evaluation did not establish a statistically significant improvement over the supervised baseline, so this artifact should be treated as an experimental research checkpoint rather than a proven upgrade.
Intended use and limitations
Use it for research, educational comparison, or human-reviewed bug-summary drafting. It may omit important facts or hallucinate details and should not drive automated production decisions.
Licensing
The base model is distributed separately under Apache-2.0. This adapter is currently shared privately for research review while a project-level release license is finalized. Dataset rights are not transferred.
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