Sales Lead Qualification using Qwen3.5-0.8B-LoRA
Lightweight AI system that converts unstructured B2B sales messages into structured lead intelligence using a fine-tuned Qwen3.5-0.8B LLM with LoRA (Unsloth). Features an interactive Gradio test interface.
Sales Lead Qualification using Qwen3.5-0.8B-LoRA
A lightweight AI system that converts unstructured B2B sales messages into structured lead intelligence using a fine-tuned Small Language Model (SLM).
Overview
This project demonstrates how a small language model like Qwen3.5-0.8B can be efficiently fine-tuned using LoRA (PEFT) via Unsloth to perform structured information extraction for B2B sales workflows.
Model Specification
- **Base Model**: Qwen3.5-0.8B
- **Fine-tuning Method**: LoRA (Parameter-Efficient Fine-Tuning)
- **Framework**: Unsloth + PyTorch
- **Task**: Structured lead qualification (classification + extraction)
- **Hugging Face Adapter**: [SRafi007/qwen3.5-0.8b-lora-lead-qualifier](https://huggingface.co/SRafi007/qwen3.5-0.8b-lora-lead-qualifier)
Dataset
Trained on ~1,248 synthetic B2B sales conversations designed to simulate real-world lead scenarios:
- Demo requests
- Pricing inquiries
- Integration questions
- Enterprise vs startup leads
Example Input & Output
Example Input Raw Text:
`"We are a startup exploring pricing and Slack integration."`
Structured JSON Output:
{
"authority_level": "recommender",
"budget_range": "low",
"intent": "integration_question",
"lead_type": "startup",
"recommended_action": "Share integration documentation",
"urgency": "medium"
}