Peter Smith
⬤ OpenMed just dropped the second dataset in its 8-day release series — and it's a big one. CalledMedical-Reasoning-SFT-GLM_4.5_Air, this Day 2 release is built specifically for supervised fine-tuning of medical and healthcare-focused language models. It's part of a growing push to make open-source training data more accessible for the AI healthcare space.
⬤ The dataset packs roughly225,000 samplesand spans around441 million tokens. It's tailored for the zai-org/GLM-4.5-Air model and structured to handle medical reasoning tasks — think complex healthcare queries, multi-step logic chains, and domain-specific problem solving. Pretty solid toolkit for anyone working on medical AI.
The goal is to maximize diversity across model architectures and training styles, enabling broader experimentation and more robust fine-tuning for medical AI research.
⬤ This is just one piece of a larger plan. Over8 consecutive days, OpenMed intends to release datasets each aligned with a different large language model. The idea is to cover a wide range of model architectures and training approaches, making it easier for researchers to run experiments and fine-tune models for medical use. The project also signals compliance readiness with frameworks likeHIPAA and GDPR, which matters a lot when you're dealing with health data.
⬤ OpenMed's move fits into a bigger trend: specialized, transparent training data is becoming essential for serious AI development. By releasing datasets built for multiple models, they're strengthening the open medical AI ecosystem and giving researchers more tools to work with.
Peter Smith
