This blog post details a fine-tuning workflow for the gpt-oss model that recovers post-training accuracy while retaining the performance benefits of FP4. It involves supervised fine-tuning (SFT) on an upcasted BF16 version of the model, followed by quantization-aware training (QAT) using NVIDIA TensorRT Model Optimizer. The article also discusses the benefits of using NVFP4 for even better convergence and accuracy recovery.
