Heidi used Fireworks’ training API to supervised‑fine‑tune (SFT) and then apply reinforcement learning (RL) to NVIDIA’s Nemotron 3 Ultra, a 550‑billion‑parameter open model. After quantizing to NVIDIA’s NVFP4 precision, they deployed the checkpoint on dedicated GPUs. In live side‑by‑side clinician comparisons, the post‑trained model beat the existing production model 54% of the time, up from about 42% for the base checkpoint.
Why it matters
Clinicians can rely on a model they control, allowing updates and on‑premises deployment for hospitals with strict data‑residency rules.
