flan-t5-small-summary-peft

Model Details

Model Description

Enhanced Dialogue Summarization Model using Parameter-Efficient Fine-Tuning (PEFT) with LoRA adapters on google/flan-t5-small. Achieves improved summary quality while training only 0.16% of parameters.

• Developed by: Paul
• Model type: Seq2Seq LM with LoRA adapters
• Language(s): English
• License: Apache 2.0 (inherited from base model)
• Finetuned from: google/flan-t5-small
• Training Efficiency: 94% parameter reduction vs full fine-tuning.

Model Sources

• Repository: [Your HF Repo Link]
• Paper: DialogSum Paper
• Demo: [Gradio Space Link]

Uses

Direct Use

Optimized for dialogue summarization tasks in customer service, meeting transcripts, and conversational analysis.

Downstream Use

• Conversational AI systems
• Dialogue content indexing
• Customer interaction analytics

Out-of-Scope Use

• Medical/legal document analysis
• Multilingual summarization
• Real-time low-latency applications

Bias & Limitations

While LoRA maintains similar bias profiles to full fine-tuning, users should:

⚠️ Validate outputs for sensitive domains
⚠️ Test with diverse dialogue samples
⚠️ Monitor for hallucination in summaries

Quick Start