---
title: RegTech BR — Model Comparison
emoji: ⚖
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: cc-by-4.0
hardware: zero-gpu
---

# RegTech BR — Model Comparison

Side-by-side comparison of two compliance reasoning systems on the same query and RAG context:

| | Model A | Model B |
|---|---|---|
| **System** | RAG + Claude Sonnet | Fine-tuned Mixtral-8x7B |
| **Adapter** | — | AutoScientist LoRA (r=32, alpha=64) |
| **Dataset** | — | adaption-brazilian-crypto-compliance (19 examples, Grade A) |
| **Win rate** | — | 63% vs 37% base model |

## Setup

1. Upload `chunks_meta.jsonl`, `embeddings.npy`, `faiss_index.bin` to Space root
2. Add `ANTHROPIC_API_KEY` as Space Secret
3. Hardware: ZeroGPU (HF PRO account required for Mixtral inference)

## Links

- Main Space (RAG only): [Fernandosr85/regtech-br](https://huggingface.co/spaces/Fernandosr85/regtech-br)
- LoRA adapter: [Fernandosr85/regtech-br-legal-adapter](https://huggingface.co/Fernandosr85/regtech-br-legal-adapter)
- Dataset: [Fernandosr85/adaption-brazilian-crypto-compliance](https://huggingface.co/datasets/Fernandosr85/adaption-brazilian-crypto-compliance)
- Challenge: [AutoScientist Challenge 2026](https://adaptionlabs.ai/blog/autoscientist-challenge)