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---
title: Lysos
emoji: 🧬
colorFrom: purple
colorTo: red
sdk: static
pinned: true
license: mit
short_description: Agentic antibiotic designer · Gemma 4 + MI300X
tags:
  - antibiotic-discovery
  - drug-design
  - gemma
  - mi300x
  - amd
  - amr
  - agentic-ai
  - antimicrobial-resistance
---

# Lysos · Open-source antibiotic designer for the AMR pandemic

> **Three-stage fine-tune of Gemma 4 31B-it on AMD MI300X. Multi-agent debate engine. End-to-end live agentic workspace.**

🏆 **AMD Developer Hackathon 2026** · Track 2 — Fine-Tuning on AMD GPUs

---

## What it is

Lysos is an end-to-end open-source antibiotic discovery platform that takes Google's **Gemma 4 31B-it** and specializes it for antimicrobial-resistance (AMR) drug design via a three-stage fine-tune on a single AMD MI300X. The fine-tuned model drives a multi-agent debate engine and a live agentic workspace.

## Live links

| Asset | Where |
|---|---|
| 📂 GitHub repo (full source) | <https://github.com/Rahul-Rajpurohitk/lysos> |
| 🤖 Stage 2.5 model (production) | <https://huggingface.co/rahul24raj/lysos-base-dpo> |
| 🤖 Stage 2 model | <https://huggingface.co/rahul24raj/lysos-base> |
| 🤖 Stage 1 model | <https://huggingface.co/rahul24raj/txgemma-4-31b> |
| 📊 Stage 2 SFT dataset (222,606 AMR examples) | <https://huggingface.co/datasets/rahul24raj/lysos-amr-stage2> |
| 🎬 Demo videos (release) | <https://github.com/Rahul-Rajpurohitk/lysos/releases/tag/v1.0-hackathon-submission> |
| 📺 Full walkthrough (9:08) | [lysos-demo-merged.mp4](https://github.com/Rahul-Rajpurohitk/lysos/releases/download/v1.0-hackathon-submission/lysos-demo-merged.mp4) |

## The three-stage fine-tune

Every stage trains a LoRA adapter on top of `google/gemma-4-31B-it`. All adapters are public.

```
                          google/gemma-4-31B-it (62 GB base)
                                       │
       ┌───────────────────────────────┼───────────────────────────────┐
       │                               │                               │
   STAGE 1                          STAGE 2                        STAGE 2.5
TxGemma-4 31B                  lysos-base                   lysos-base-dpo
LoRA r=64, α=256              LoRA r=64, α=128             LoRA r=32, α=64 (β=0.1)
continued pretraining         SFT on 222,606 AMR examples  DPO on hard-negative pairs
for therapeutics              (8 priority pathogens)       (10 anti-correlated axes)
~2 hr on 1× MI300X            ~3 hr on 1× MI300X           ~45 min on 1× MI300X
```

**Why DPO for the alignment stage**: DPO is the right tool for this objective. The downstream usage pattern — the Strategist agent picking among Designer-proposed candidates — is a discrete preference choice, exactly what DPO optimizes for. KL-bounded objective for stability, no axis to game, sample-efficient at 10K pairs in 45 min on 1× MI300X, full base capability preserved.

## The agentic workspace

When you fire `/wf design_with_debate`, four agent roles take turns — each is a separate LLM call:

```
  ┌───────────┐         ┌───────────┐         ┌───────────┐         ┌─────────────┐
  │ DESIGNER  │── 3 ───▶│  CRITIC   │──────▶─▶│  EDITOR   │──────▶─▶│  STRATEGIST │
  │  drafts   │ smiles  │ challenges│ critique│ refines   │  fix    │  picks winner│
  └───────────┘         └───────────┘         └───────────┘         └──────┬──────┘
                                                                           │
                                            winner SMILES auto-loads to 2D + 3D + radar
```

Plus 7 streaming workflows, 12+ slash commands, real-time per-atom resistance scoring against curated CARD clinical mutations, per-pathogen Champion table, Knowledge command-center with 4-tier resistance gene network.

## Why MI300X

192 GB HBM3 lets us fit **Gemma 4 31B base in bf16 + LoRA adapter + KV cache + agent context coresident on one GPU**. Same GPU trains and serves. No tensor parallelism, no model sharding, no migration step.

## Run it locally

```bash
git clone https://github.com/Rahul-Rajpurohitk/lysos.git
cd lysos
python3 -m venv .venv && source .venv/bin/activate
pip install -e .
uvicorn workspace.api.server:app --host 0.0.0.0 --port 7860 &

cd workspace/web && npm install && npm run dev
# open http://localhost:5173
```

## License

MIT (code) · Apache-2.0 / Gemma terms (weights) · CC-BY (datasets)

---

📺 **Watch the 9-minute demo**: [lysos-demo-merged.mp4](https://github.com/Rahul-Rajpurohitk/lysos/releases/download/v1.0-hackathon-submission/lysos-demo-merged.mp4)

📂 **Full source**: [github.com/Rahul-Rajpurohitk/lysos](https://github.com/Rahul-Rajpurohitk/lysos)