Month: 2025-10. Focused on expanding model loader capabilities in tenstorrent/tt-forge-models, delivering support for TinyLlama variant in the Llama model loader and introducing the CodeGemma 2B model loader, enabling broader model variety for ML training and generalization tasks. No major bugs reported within the scope of this work; changes validated and integrated, laying groundwork for future model loader extensions and experiments.

Month: 2025-06. Focused on improving test reliability for Llama training with LoRA, aligning tests to final configurations and memory constraints, and strengthening test setup and documentation to reflect environment limitations. This reduces risk in training runs and improves CI feedback for future iterations.

April 2025 monthly summary for tenstorrent/tt-forge-fe: Key focus on LoRA fine-tuning testing for Llama models within tt-forge-fe. Implemented comprehensive forward/backward pass tests, updated dependencies, refactored gradient handling for PyTorch compatibility, and added training/backward-pass test files to improve validation coverage. This work establishes robust LoRA fine-tuning validation in the framework and paves the way for more reliable experiments.

March 2025 — Tenstorrent TT-Forge-Fe: Strengthened test coverage for Llama forward pass by adding parameterized tests across varying input sequence lengths, validating compilation and inference across multiple configurations. No major bugs reported this month; focus remained on test reliability and risk reduction ahead of upcoming refactors and feature work.

February 2025 monthly summary for tenstorrent/tt-forge-fe focusing on correctness fixes, test alignment, and code-path simplifications that improve backward pass reliability and Forge API integration.

January 2025 monthly summary: Focused on strengthening numerical correctness and backward-path reliability across TTNN, TT-forge-fe, and TT-tvm, aligning datatype handling with model training workflows and expanding test coverage for Llama-compatible matrices.