March 2026 monthly summary for tenstorrent/tt-forge-models: Delivered a Galaxy Tests Mesh Sharding Configuration Enhancement that switches the mesh shape to (4, 8) (DP=4, TP=8) to improve test parallelism and compatibility for Llama and gpt-oss galaxy tests. The change updates mesh_configs from (8, 4) to (4, 8) and is tied to ticket #509. This results in faster, more scalable test runs and better resource utilization without impacting existing test coverage.

February 2026 monthly summary focusing on stability, performance, and test-infra improvements across two core repos. In tt-forge, delivered a LM Head All-Gather constraint for multi-chip tensor-parallel tests, enforcing an all-gather at the end of the graph to reduce graph generation from 100+ variants to a single prefill graph and a single decode graph during llm benchmark runs. This change improves test efficiency and reliability in multi-chip scenarios (commit 48178700fff3fded9f7024141e1eed35b96a6f8c). In tt-xla, introduced filecheck validation and serialization capabilities to the testing infra, enabling robust MLIR pattern verification and artifact serialization via pytest markers and the --serialize flag, and removed obsolete serialization paths to simplify the codebase (commit 2f50fba2253120aca9d080748790759a9466da5e). The combined impact is faster CI feedback, reduced resource usage in tests, and stronger regression visibility across MLIR/test infra. Technologies demonstrated include multi-chip tensor-parallel testing, LM head sharding, MLIR/filecheck validation, pytest-based test infrastructure, and artifact serialization.

In January 2026, delivered a focused set of performance and reliability enhancements across the tt-forge, tt-xla, and tt-forge-models repositories. The work enabled robust benchmarking for large language models, expanded test coverage for multi-chip data-parallel deployments, and stabilized model loading paths, driving faster regression detection and higher reliability for large-model production workloads.

December 2025 performance summary: Strengthened scalable execution, reliability, and test coverage across the TT/XLA, TT/MLIR, and TT/Forge stacks. Delivered practical tensor-parallel capabilities, enhanced test infrastructure, and stability fixes that reduce risk in production and CI cycles while enabling faster iteration on performance-focused workloads.

November 2025: Cross-repo improvements focused on test coverage, performance, and data-parallel demonstrations. Expanded testing and benchmarking for tt-xla, introduced a data-parallel PyTorch ResNet example, and enabled faster, more selective graph testing in ForgeModel. Key bug fixes to graph tests improved reliability and CI performance.

Monthly Summary for 2025-10: Key features delivered: - BGE-M3 Encode Demo Performance Enhancement: Refactored the BGE-M3 encode demo to implement a custom encode function that tokenizes inputs and runs the model on the device. The demo has been moved to the tt-xla directory to utilize the xla_backend, reducing overhead and speeding up model processing. - Llama 3.1 405B model variant support: Added support for Llama 3.1 405B base and instruct variants in causal language modeling and sequence classification; enables loading and utilizing these larger models as requested by customers. Major bugs fixed: - No major bugs fixed this period; work focused on performance improvements and feature expansion for larger models. Overall impact and accomplishments: - Improved on-device processing throughput and lower latency for the encode demo by leveraging the tt-xla path and device-side encoding. - Expanded customer-ready model capabilities by adding 405B support, enabling deployment of larger models with existing tooling. - Demonstrated effective cross-repo collaboration between tt-forge and tt-forge-models to deliver scalable, customer-driven enhancements. Technologies/skills demonstrated: - XLA backend integration (tt-xla), on-device execution, and custom tokenization/encoding workflows. - Large-model loading and inference (Llama 3.1 405B) across causal LM and sequence classification. - Code refactoring, performance tuning, and cross-repo coordination for feature delivery.

September 2025 monthly summary focused on delivering core model-loading capabilities, end-user demos, and maintainability improvements across two repositories (tenstorrent/tt-forge-models and tenstorrent/tt-forge).

Monthly summary for 2025-08 focused on stabilizing llama model integration in tt-forge-models. Implemented a critical fix to dtype handling in tt-torch that removes an unnecessary dtype_override, enabling bfloat16 conversions and allowing llama models to pass tt-torch tests without type conversion errors. This work improved test reliability and laid groundwork for broader model compatibility across the repo.

July 2025 monthly summary for tenstorrent/tt-forge-models: Delivered expanded model catalog and test compatibility with loader support and new configurations for models migrated from tt-torch. This work enables broader experimentation and validation across a diverse model set including Mistral, Phi-3/4, RMBG, SeamlessM4T, Llama variants, BEiT, BiRNN-CRF, D-Fine, Flux, Llama_7b, Llama Causal LM, MLPMixer lucidrains, XLMRoberta Masked LM, Segformer, and UNet torch.hub. Implemented a compatibility change to propagate batch_size through load_inputs to improve testability and reliability across models. No major bugs reported; the focus was on feature delivery, cross-repo integration, and test coverage to accelerate customer readiness and internal experimentation.

June 2025 monthly summary focused on expanding model availability, optimizing data paths, and enabling scalable deployment capabilities across tt-forge-models and tt-forge. Delivered a significantly richer model zoo, improved data processing throughput, and documented pipeline parallelism for large-model experimentation, enabling faster experimentation and reduced time-to-value for model benchmarking and deployment.

Month 2025-05: Implemented automated batch parallelization tests across n300 devices for multiple models in tenstorrent/tt-torch, establishing a robust baseline for parallelization verification and test coverage.