
Ssalice contributed to model validation and integration across tenstorrent’s tt-torch, tt-forge-models, and tt-xla repositories, focusing on deep learning workflows and model reliability. They implemented bring-up and integration tests for Microsoft’s Phi-4 and Phi-3 models in tt-torch, configuring nightly CI pipelines using Python and YAML to automate regression validation and document runtime constraints. In tt-forge-models, Ssalice added Stable Diffusion model support and resolved a caching bug in the Qwen3 loader, improving test stability. Their work in tt-xla centered on unit testing Rotary Embeddings for Llama and Qwen models, enabling future performance optimizations and reducing inference risk.

Concise monthly summary for 2025-10 focusing on the tenstorrent/tt-xla repo and the delivered work in Rotary Embeddings testing. The month centered on validating the Rotary Embedding operation for Llama and Qwen models and setting up tests to enable performance optimizations via operator fusion.
Concise monthly summary for 2025-10 focusing on the tenstorrent/tt-xla repo and the delivered work in Rotary Embeddings testing. The month centered on validating the Rotary Embedding operation for Llama and Qwen models and setting up tests to enable performance optimizations via operator fusion.
August 2025 monthly summary for tenstorrent/tt-forge-models focused on delivering model compatibility improvements and stability for upcoming tests. Key outcomes include support for three Stable Diffusion models and resolution of a PCC-related caching issue in the Qwen3 model loader, reinforcing reliability of tt-forge-models across test environments and speeding test cycles.
August 2025 monthly summary for tenstorrent/tt-forge-models focused on delivering model compatibility improvements and stability for upcoming tests. Key outcomes include support for three Stable Diffusion models and resolution of a PCC-related caching issue in the Qwen3 model loader, reinforcing reliability of tt-forge-models across test environments and speeding test cycles.
July 2025 monthly summary for tenstorrent/tt-torch: Implemented bring-up tests for Phi-4 and Phi-3 model variants, integrated with nightly CI, and added test scaffolding to support future validation. Full evaluation remains constrained by runtime OOM issues, which are documented and prioritized for remediation. This work establishes automated validation for new model variants and informs data/compute planning.
July 2025 monthly summary for tenstorrent/tt-torch: Implemented bring-up tests for Phi-4 and Phi-3 model variants, integrated with nightly CI, and added test scaffolding to support future validation. Full evaluation remains constrained by runtime OOM issues, which are documented and prioritized for remediation. This work establishes automated validation for new model variants and informs data/compute planning.
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