drmuskangarg/SLMs-in-healthcare

The project is a specialized research repository targeting Small Language Models (SLMs) in healthcare. Despite the relevance of the niche, the project shows very low quantitative signals: 24 stars and minimal fork activity over a year indicate low adoption and a lack of community momentum. From a competitive standpoint, the moat is non-existent; the techniques mentioned (compression, optimization) are now standard in the HuggingFace ecosystem (via PEFT, LoRA, and 4-bit quantization). Frontier labs and established players have already moved into this space with high-performance base SLMs like Microsoft's Phi series or Google's Gemma, which can be fine-tuned for medical tasks with superior results to older clinical models. The project functions more as a personal or academic artifact than a defensible software project. It is highly susceptible to displacement by generic but high-performing SLMs that are pre-trained on broader datasets including medical literature.