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 |
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
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
📂 Full source: github.com/Rahul-Rajpurohitk/lysos