Resource-Aware Adaptive Inference and Incremental Hybrid Retrieval for Practical On-Device Small Language Model Assistants

A resource-aware adaptive inference and incremental hybrid retrieval system for practical on-device small language model assistants.

A resource-aware adaptive inference and incremental hybrid retrieval system for practical on-device small language model assistants. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on it.

It suits learners who want to work with language models and text or speech: preparing text data, using or fine-tuning a model, evaluating it and building an interface around it.

The main parts are a conversational interface, text or speech data preparation, model training, fine-tuning or prompting, evaluation with task-appropriate metrics, and a chat or web interface to use it. The reference build uses LangChain, FAISS, LLM APIs, Python, and Hugging Face Transformers, with a clear project structure, setup instructions and sample data so that it can be run and extended.