
Over two months, Aleksandar Orlovic developed and integrated advanced model loaders and testing infrastructure in the tenstorrent/tt-forge-models repository, enabling automated validation for five new AI models with clear expected-failure signaling for hardware-limited cases. He used Python, Flax, and JAX to expand model coverage and streamline nightly and weekly test runs, improving deployment confidence and cross-repository collaboration. In tenstorrent/tt-mlir, Aleksandar enhanced tensor operation flexibility by implementing StableHLO scatter dimension support and introducing a type-safe bitcast_convert operation using C++ and MLIR. His work focused on robust validation, safer data handling, and strengthening MLIR-based model compilation pipelines.
April 2026: Delivered two major features in tenstorrent/tt-mlir, focusing on expanding tensor operation flexibility and safe type reinterpretation. Implemented StableHLO scatter flexibility and introduced bitcast_convert, with accompanying tests and robust validation. No major bug fixes reported for this repository this month. The work strengthens MLIR-based model compilation pipelines by enabling more flexible indexing, safer data reinterpretation, and stronger test coverage, improving reliability and downstream performance.
April 2026: Delivered two major features in tenstorrent/tt-mlir, focusing on expanding tensor operation flexibility and safe type reinterpretation. Implemented StableHLO scatter flexibility and introduced bitcast_convert, with accompanying tests and robust validation. No major bug fixes reported for this repository this month. The work strengthens MLIR-based model compilation pipelines by enabling more flexible indexing, safer data reinterpretation, and stronger test coverage, improving reliability and downstream performance.
In March 2026, the tt-forge-models work focused on expanding model loading and testing coverage for bounty models. The team delivered new model loaders for LLama3.1-8b, Falcon3-7b, Mixtral-8x7b, Mistral-small, and Gemma3-27b, integrated into the existing testing framework to support automated nightly and weekly test runs. The work, anchored by commit 843095e6394cefc795fe12b2420835321b8d70aa, maps each model to loader functions and links to upstream PRs for traceability. Some models are expected to fail due to hardware limitations, but the test harness now surfaces these signals clearly early in CI. No critical bug fixes were closed this month; instead, the focus was on risk-managed feature delivery and improved validation coverage, which enhances confidence for model deployment and cross-repo collaboration with tt-xla.
In March 2026, the tt-forge-models work focused on expanding model loading and testing coverage for bounty models. The team delivered new model loaders for LLama3.1-8b, Falcon3-7b, Mixtral-8x7b, Mistral-small, and Gemma3-27b, integrated into the existing testing framework to support automated nightly and weekly test runs. The work, anchored by commit 843095e6394cefc795fe12b2420835321b8d70aa, maps each model to loader functions and links to upstream PRs for traceability. Some models are expected to fail due to hardware limitations, but the test harness now surfaces these signals clearly early in CI. No critical bug fixes were closed this month; instead, the focus was on risk-managed feature delivery and improved validation coverage, which enhances confidence for model deployment and cross-repo collaboration with tt-xla.

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