TinyLLMs provides high-reasoning, distilled Small Language Models (SLMs) purpose-built for constrained hardware. We bridge the reasoning gap for mission-critical, offline environments.
Our pipeline requires massive compute to distill deep reasoning capabilities into models small enough to run on local vehicle hardware.
We utilize high-density H100/A100 GPU clusters to train foundational reward models. We apply advanced Reinforcement Learning (RL) techniques to teach complex spatial and persona-based reasoning.
Through proprietary knowledge distillation and quantization, we compress large model weights into highly efficient SLMs (1B-7B parameters) without sacrificing the reasoning gap.
The distilled models are deployed directly onto edge hardware. They execute autonomous logic, persona mimicry, and dynamic routing with zero cellular latency.
Our proprietary pipeline shrinks massive parameter footprints into edge-deployable formats while retaining complex reasoning pathways.
Transferring behavioral policies from 70B+ parameter teacher models to sub-3B student models using KL Divergence loss.
Reducing the precision of the network's weights to drastically reduce VRAM usage and accelerate edge ALUs.
Systematically removing non-critical neural connections to enforce sparsity, accelerating matrix multiplications.
Parameter-Efficient Fine-Tuning freezes the pre-trained model and injects trainable rank decomposition matrices.
Emergency responders cannot rely on cloud APIs in dead zones. TinyLLMs powers embedded agentic systems that handle complex traffic preemption, dynamic routing, and persona-based dispatcher mimicry—all processed locally on the vehicle's hardware.
TinyLLMs is founded by engineering leaders with deep roots in Reinforcement Learning, NLP, and High-Performance Compute. Our team brings experience from Stanford AI research, IIT, and scaling enterprise health-tech platforms.
Founder & Principal Architect