4 papers
Pretraining and adapting a language model on a dependency-free stack: GPT-2 124M from random weights, reproduced against llm.c, and a clinical adapter for Qwen3-0.6B
Thang Tran, Lan Dang
Almost every language model in service was trained by one family of software. That concentration makes a question hard to settle: how much of what is known about training a languag…
woma: a real-time foundation model and its fine-tuned models for endoscopy
Thang Tran, Lan Dang
woma is a real-time foundation model for gastrointestinal endoscopy: a network trained without labels on about a million endoscopy frames, from which task models are fine-tuned. We…
Numbat: Building and Verifying a Self-Contained Machine-Learning Stack
Thang Tran, Lan Dang
Machine-learning systems are built almost exclusively on a few large Python-orchestrated frameworks, and they inherit those stacks' engineering costs: environments of hundreds of v…
Cross-Stack Validation of Language-Model Training: A Clinical Fine-Tuning Case Study
Thang Tran, Lan Dang
Neural network training has an oracle problem: a run can converge normally and yield a usable model while the software beneath it computes something other than specified. Almost al…