README.md

metadata

title: TuringDNA Assistant
emoji: 🧬
colorFrom: gray
colorTo: blue
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: apache-2.0
hardware: zero-a10g
short_description: Protein biology Q&A backend for the TuringDNA engine.
suggested_hardware: zero-a10g

TuringDNA Assistant

Self-hosted biomedical LLM that powers the in-app chat panel on turingdna.com/app. Loads BioMistral-7B (an open-source Mistral fine-tuned on biomedical corpora) on a ZeroGPU-shared NVIDIA A100, exposes a Gradio ChatInterface for direct testing, and an auto-generated /run/predict API the Flask app calls via gradio_client.

Architecture

┌─────────────────────────────────────────────────────────────────────┐
│  winter4000/syntheogenesis            (Flask + vanilla JS, CPU)     │
│  └── dee/server.py /api/chat                                        │
│        └── gradio_client.predict() ──────────────────────────┐      │
└──────────────────────────────────────────────────────────────┼──────┘
                                                                ▼
┌─────────────────────────────────────────────────────────────────────┐
│  winter4000/turingdna-assistant       (Gradio, ZeroGPU)             │
│  ├── app.py     Gradio ChatInterface                                │
│  └── llm.py     BioMistral-7B in bf16, @spaces.GPU(duration=60)     │
└─────────────────────────────────────────────────────────────────────┘

Two-Space split so the existing Flask engine doesn't need a rewrite and the model lives where the GPU does.

Why ZeroGPU?

ZeroGPU gives shared A100 access to HF PRO subscribers at no per-hour cost (just the $9/mo subscription). The decorator pattern:

• Model loads on CPU at import (~14 GB in bf16, fits comfortably in ZeroGPU's 60 GB host RAM)
• @spaces.GPU(duration=60) moves the model to GPU only during a generation call, then releases — so we share the A100 efficiently with other ZeroGPU Spaces

First call after the Space wakes up: ~10–30 s (cold-start + GPU acquire). Subsequent calls: ~25–80 tokens/sec.

Model

BioMistral/BioMistral-7B — Apache-2.0, biomedical-domain fine-tune of Mistral-7B-Instruct-v0.1. Knows enzyme mechanisms, active sites, conserved domains, codon optimization, expression systems, and cloning vocab better than vanilla Mistral. Same Mistral instruct template ([INST] ... [/INST]).

Fallback if BioMistral fetch fails: mistralai/Mistral-7B-Instruct-v0.2.

System prompt

Baked into llm.py. The assistant is told it's the chat backend for TuringDNA, knows the codebase's ΔLL sign convention (positive ΔLL = mutation is MORE likely than WT under ESM-2, i.e. more tolerated; negative = less likely, i.e. disruptive), and is instructed to be concise + not hallucinate domain boundaries.

Local development

pip install -r requirements.txt
python app.py

Local runs use CPU-only fp16 (~2 tok/s on Mac M1, ~1 tok/s on Intel Mac). Production runs on ZeroGPU A100. The @spaces.GPU decorator is a no-op locally so the same code works in both contexts.

Calling from outside

from gradio_client import Client

client = Client("winter4000/turingdna-assistant")
response = client.predict(
    message="What does a ΔLL of +1.2 for V8L mean?",
    history=[],
    api_name="/chat",
)
print(response)

Files

• app.py — Gradio app entry (ChatInterface + Gradio launches its own API endpoints)
• llm.py — model loading + Mistral prompt formatting + ZeroGPU inference function
• requirements.txt — Gradio, transformers, spaces, torch, accelerate
• README.md — this file (also the HF Space metadata via YAML frontmatter at the top)