Genetics AI Platform Blog

1. Genetics LLM

A Domain-adapted large language model specialized for genetics and molecular biology research. The model demonstrates improved accuracy and contextual understanding on domain-specific queries compared to general-purpose LLMs.

Training:

• Base Model: Qwen2-1.5B with LoRA fine-tuning (rank=16, alpha=32) for parameter-efficient training without catastrophic forgetting
• Dataset: Curated 98K Q&A pairs covering CRISPR mechanisms, DNA sequencing technologies, hereditary diseases, gene expression, and molecular diagnostics
• Infrastructure: 3 epochs on NVIDIA A100 GPU (40GB VRAM) via HuggingFace training infrastructure with mixed-precision (bf16)

2. Genetics API (inference)

Production-grade REST API providing scalable, low-latency access to the Genetic LLM for real-time inference.

Architecture:

• Framework: FastAPI with async request handling and automatic OpenAPI documentation
• Backend: HuggingFace Inference Endpoints with GPU acceleration (NVIDIA T4)
• Security: API key-based authentication with rate limiting and request validation

Infrastructure:

• Compute: AWS EKS (Kubernetes) with 2-node managed cluster
• Network: Application Load Balancer with SSL/TLS termination via ACM
• CI/CD: GitHub Actions pipeline with automated ECR push and kubectl rollout

3. Genetics AI Assistant

Interactive web application enabling natural language conversations with the Genetic LLM for researchers and students.

Stack:

• Frontend: React 18 with TypeScript for type-safe development, built with Vite
• Styling: Bootstrap CSS with custom design system
• Deployment: S3 + CloudFront for global CDN distribution

Features:

• Real-time streaming responses with typing indicators
• Conversation history with local storage persistence
• Mobile-responsive layout with example prompts