By Joseph Zhang

TL;DR

A new technical report from The Alan Turing Institute introduces a lean, locally deployable RAG (Retrieval-Augmented Generation) framework powered by Qwen-2.5-Instruct, DeepSeek-R1, and synthetic data. This layered system combines summarization, reasoning trace generation, and distillation, allowing a compact 1.5B parameter model to rival much larger models on medical domain tasks, while keeping costs low and outputs transparent.

Why This Matters to Your Industry

• On-Premise Control & Privacy

  Keeps sensitive data internal and compliant, ideal for healthcare, finance, and legal sectors.

• Efficiency That Scales

  Smaller models save on compute and infrastructure costs without compromising outcomes.

• Explainability & Auditability

  Built-in reasoning traces make every step transparent, which is crucial for regulated sectors.

• Domain-Specific Accuracy

  Tailored synthetic queries ensure the system understands specialized language and contexts.

How the System Works:

1. Summarize & Retrieve:

Long documents (e.g., medical entries) are compressed to ~15% of the original using summarization techniques, preserving core info while boosting retrieval speed.

2. Generate Synthetic Queries:

AI generates realistic, domain-specific queries (e.g., symptoms) for improved coverage and training without manual labor.

3. Reasoning via DeepSeek-R1:

A reinforcement-trained model generates reasoning traces that smaller models can mimic for explainable logic chains.

4. Fine-Tune & Distill:

• A 32B model trained on synthetic data and reasoning traces reaches about 56% accuracy on condition identification and 51% on treatment guidance.
• A distilled 1.5B model delivers nearly identical performance (about 53% and 54%) in a much leaner package.