October 2025 monthly summary focused on stabilizing core UNIDAD workflows, expanding model coverage, and strengthening testability across tt-forge-models, tt-xla, and tt-mlir. Key efforts reduced build fragility, enabled next steps for autonomous driving models, and laid groundwork for full inference under constrained resources.

September 2025 monthly summary for tenstorrent/tt-forge-models focusing on delivering end-to-end UNIAD PyTorch autonomous driving model and stabilizing testing workflow. Key contributions include implementing UNIAD PyTorch model with ModelLoader and integrated heads enabling end-to-end autonomous driving functionality with reduced external dependencies, and addressing stability issues by removing unnecessary CPU transfers and detach() calls to resolve TorchRuntimeError and memory allocation problems during tests. This work improves model throughput, testing reliability, and readiness for deployment in a production-like environment.