AQUA-1B: Aquaculture-Specific Small Language Model

AQUA-1B, developed by Kurma AI, is a 1-billion parameter Small Language Model (SLM) built upon Google DeepMind's Gemma 3 1B base. It is the first lightweight model specifically tailored for the aquaculture domain, focusing on real-time operations, IoT sensor data processing, and autonomous system control.

Key Capabilities

• Edge-Ready Intelligence: Optimized for low-power, real-time inference on embedded devices like Raspberry Pi and Jetson Nano.
• Agentic Task Execution: Supports multi-step, agent-based workflows for sensor checks, feeding triggers, and autonomous health checks.
• IoT-Aware Reasoning: Natively understands and reasons over sensor data inputs (e.g., temperature, pH, TDS) for rapid decision-making.
• Robotic Automation Control: Designed to interact with robotic systems, including underwater and mobile pond inspectors.
• Autonomous Alerting Systems: Powers local alert mechanisms (SMS, Telegram bots, MQTT) for critical parameter thresholds.
• Field-Deployable Decision Engine: Enables autonomous operation in remote hatcheries and ponds, even in offline conditions.

Training and Data

AQUA-1B was fine-tuned using a LoRA-based Supervised Fine-Tuning (SFT) approach on approximately 3 million real and synthetic Q&A pairs, totaling around 1 billion tokens of high-quality, domain-specific data. This dataset includes extension worker–farmer dialogues, FAO/ICAR/NOAA research, and species-specific culture methods.

Good for

• On-device decision-making in aquaculture operations.
• Real-time alert generation based on water quality parameters.
• Automating tasks like feeding routines and water exchange scheduling.
• Controlling robotic systems for inspection and monitoring in ponds and RAS.
• Deployments in low-connectivity or offline environments requiring autonomous operation.