What is Radal?
Radal is a next-generation no-code AI platform engineered for rapid, responsible fine-tuning of small language models (SLMs) — without requiring Python, GPUs, or ML engineering teams. Designed for domain experts — not data scientists — Radal transforms proprietary data into production-ready AI through visual orchestration. Whether you're a healthcare startup building HIPAA-compliant clinical assistants, an industrial automation firm deploying real-time anomaly detection on edge gateways, or a legal tech team fine-tuning case-law interpreters, Radal delivers enterprise-grade model customization in minutes, not months.
How to Use Radal
Getting started with Radal is as simple as connecting, configuring, and clicking. Begin by importing your structured or unstructured data — directly from Hugging Face datasets, cloud storage (S3, GCS), or via secure local upload. Then, build your training pipeline visually: drag-and-drop modules for data cleaning, prompt engineering, LoRA configuration, quantization settings, and evaluation logic. The embedded AI Copilot observes your inputs and constraints — dataset size, target latency, hardware profile — and surfaces actionable suggestions: “Try QLoRA with 4-bit quantization for mobile deployment” or “Add synthetic instruction tuning for low-resource domains.” Launch training with one click, track convergence live in the interactive dashboard, and compare versions side-by-side using built-in metrics like perplexity, accuracy, and inference speed.
Go beyond first-run success with Radal’s iterative refinement loop: adjust prompts, reweight samples, swap adapters, or toggle preprocessing steps — all visually — then retrain and validate without redeploying infrastructure. Export models in ONNX, GGUF, or safetensors formats; deploy natively to iOS/Android, Raspberry Pi clusters, or air-gapped servers; or publish automatically to Hugging Face with full lineage tracking, commit history, and access controls.