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  <title>Lysos · Open-source antibiotic designer · Gemma 4 + AMD MI300X</title>
  <meta name="description" content="Three-stage fine-tune of Gemma 4 31B-it on AMD MI300X. Multi-agent debate engine. End-to-end live agentic workspace for antimicrobial-resistance drug design." />
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<div class="wrap">

  <div class="hero">
    <h1>Lysos</h1>
    <div class="tagline">
      Open-source generative antibiotic designer for the AMR pandemic.<br/>
      Three-stage fine-tune of Gemma 4 31B-it on AMD MI300X. Multi-agent debate engine. End-to-end live agentic workspace.
    </div>
    <div class="badges">
      <span class="badge amd">🏆 AMD Developer Hackathon 2026</span>
      <span class="badge">Track 2 · Fine-Tuning on AMD GPUs</span>
      <span class="badge green">MIT License</span>
    </div>
  </div>

  <h2>Live links</h2>
  <div class="grid">
    <div class="card">
      <div class="label">Source</div>
      <h3><a href="https://github.com/Rahul-Rajpurohitk/lysos">GitHub repo →</a></h3>
      <p>FastAPI backend · React+Vite frontend · training pipeline · agentic harness · MIT</p>
    </div>
    <div class="card">
      <div class="label">Production model</div>
      <h3><a href="https://huggingface.co/rahul24raj/lysos-base-dpo">rahul24raj/lysos-base-dpo →</a></h3>
      <p>Stage 2.5 DPO · LoRA r=32 · adapter on Gemma 4 31B-it</p>
    </div>
    <div class="card">
      <div class="label">SFT model</div>
      <h3><a href="https://huggingface.co/rahul24raj/lysos-base">rahul24raj/lysos-base →</a></h3>
      <p>Stage 2 SFT · LoRA r=64 · 222,606 AMR examples</p>
    </div>
    <div class="card">
      <div class="label">Pretraining model</div>
      <h3><a href="https://huggingface.co/rahul24raj/txgemma-4-31b">rahul24raj/txgemma-4-31b →</a></h3>
      <p>Stage 1 · continued pretraining for therapeutics · LoRA r=64 α=256</p>
    </div>
    <div class="card">
      <div class="label">Training dataset</div>
      <h3><a href="https://huggingface.co/datasets/rahul24raj/lysos-amr-stage2">lysos-amr-stage2 →</a></h3>
      <p>222,606 AMR records · 8 priority pathogens · ChEMBL + literature + curated negatives</p>
    </div>
    <div class="card">
      <div class="label">Demo videos</div>
      <h3><a href="https://github.com/Rahul-Rajpurohitk/lysos/releases/tag/v1.0-hackathon-submission">v1.0 release →</a></h3>
      <p>Merged 9:08 walkthrough + 3 individual scenes (agentic flow, system tour, full)</p>
    </div>
  </div>

  <div class="video-card">
    <div class="label" style="color: var(--text-dim); font-size: 10px; letter-spacing: 1px; text-transform: uppercase; font-weight: 700;">9-MINUTE FULL WALKTHROUGH</div>
    <h3 style="margin: 8px 0; font-size: 22px; color: var(--text);">Watch the demo</h3>
    <p style="margin: 0 0 12px; color: var(--text-dim); font-size: 13px;">
      Multi-agent debate · live workspace · real-time scoring · resistance escape map · Pareto frontier
    </p>
    <a href="https://github.com/Rahul-Rajpurohitk/lysos/releases/download/v1.0-hackathon-submission/lysos-demo-merged.mp4">▶ Play (41 MB)</a>
  </div>

  <h2>The three-stage fine-tune</h2>
  <p style="color: var(--text-dim);">Every stage trains a LoRA adapter on top of <code style="color: var(--purple-2);">google/gemma-4-31B-it</code>. All adapters are public on Hugging Face.</p>
  <div class="stages">
    <div class="stage">
      <div class="num">STAGE 1</div>
      <div class="name">TxGemma-4 31B</div>
      <div class="meta">LoRA r=64, α=256 · ~2 hr on 1× MI300X<br/>Continued pretraining for therapeutics</div>
    </div>
    <div class="stage">
      <div class="num">STAGE 2</div>
      <div class="name">lysos-base</div>
      <div class="meta">LoRA r=64, α=128 · ~3 hr on 1× MI300X<br/>SFT on 222,606 AMR examples</div>
    </div>
    <div class="stage">
      <div class="num">STAGE 2.5</div>
      <div class="name">lysos-base-dpo</div>
      <div class="meta">LoRA r=32, α=64, β=0.1 · ~45 min on 1× MI300X<br/>DPO on 10K hard-negative Pareto pairs</div>
    </div>
  </div>

  <h2>The multi-agent debate</h2>
  <p style="color: var(--text-dim);">When you fire <code style="color: var(--purple-2);">/wf design_with_debate</code>, four agent roles take turns — each is a separate LLM call.</p>
  <div class="agent-flow">
    <span class="agent a-d">DESIGNER</span>
    <span class="arr">─ proposes 3 ──▶</span>
    <span class="agent a-c">CRITIC</span>
    <span class="arr">── critiques ──▶</span>
    <span class="agent a-e">EDITOR</span>
    <span class="arr">── refines ──▶</span>
    <span class="agent a-s">STRATEGIST</span>
  </div>
  <p style="color: var(--text-dim); font-size: 13px; text-align: center; margin-top: 14px;">
    Strategist picks winner + runner-up + next action.<br/>
    Winner SMILES auto-loads to 2D builder + 3D pocket viewer + 12-axis reward radar + resistance escape map.
  </p>

  <h2>Why MI300X is load-bearing</h2>
  <p style="color: var(--text-dim);">
    192 GB HBM3 lets us fit Gemma 4 31B base in bf16 + LoRA adapter + KV cache + agent context
    <strong style="color: var(--text);">coresident on one GPU</strong>. Same GPU trains and serves.
    No tensor parallelism, no model sharding, no migration step.
    Total wall-clock for the three-stage fine-tune: <strong style="color: var(--purple-2);">~6 hours on 1 GPU</strong>.
  </p>

  <h2>Run it locally</h2>
  <pre>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 &amp;

cd workspace/web && npm install && npm run dev
# open http://localhost:5173</pre>

  <footer>
    <p>
      <strong>License</strong>: MIT (code) · Apache-2.0 / Gemma terms (weights) · CC-BY (datasets)<br/>
      Built for the <a href="https://lablab.ai">AMD Developer Hackathon 2026</a> · Track 2: Fine-Tuning on AMD GPUs
    </p>
  </footer>

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