| --- |
| 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 |
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| Side-by-side comparison of two compliance reasoning systems on the same query and RAG context: |
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| | | 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 | |
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|
| ## Setup |
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|
| 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) |
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| ## Links |
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| - 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) |
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