最新模型
Compile by Training Compiles Natural-Language Specifications into Locally Executable Functions on a 0.6B Model
The new method uses a large teacher model to synthesize task data, then trains a function-specific LoRA for each task so subsequent requests do not need to call a remote model. It achieves 83.6% semantic accuracy on a difficult benchmark, but compilation still requires a teacher API, about 40 GB of accelerator memory, and output validation.