I will design specialized tiny gpt bert style models for edge devices

Need AI that works locally without cloud APIs, internet access, or an external server?

I design and implement task-specific transformer-style AI models for resource-constrained embedded platforms, using conventional vector, matrix and tensor operations.

Depending on targeted task, I can develop lightweight:

• GPT style architectures for text generation or sequence prediction
• BERT style architectures for classification, embeddings and feature extraction
• Encoder-decoder architectures for specialized sequence-to-sequence tasks
• Attention-based models optimized for embedded inference
• Quantized and memory-optimized models for 32-bit MCUs
• Multi-MCU/MCU-cluster inference architectures

Target platforms can include ESP32-S3, STM32, other 32-bit MCUs, depending on available RAM, Flash, PSRAM & specific requirements.

The goal is fully offline Edge AI: inference runs directly on your device rather than depending on OpenAI APIs, cloud servers or continuous internet connectivity.

Important: These are application-specific embedded AI systems, not full-scale ChatGPT replacements. Please contact me before ordering so I can evaluate your MCU resources, dataset, model requirements and target task.