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Sonnet Salice

PROFILE

Sonnet Salice

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.

Overall Statistics

Feature vs Bugs

75%Features

Repository Contributions

5Total
Bugs
1
Commits
5
Features
3
Lines of code
849
Activity Months3

Work History

October 2025

2 Commits • 1 Features

Oct 1, 2025

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

2 Commits • 1 Features

Aug 1, 2025

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

1 Commits • 1 Features

Jul 1, 2025

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.

Activity

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Quality Metrics

Correctness86.0%
Maintainability88.0%
Architecture84.0%
Performance72.0%
AI Usage24.0%

Skills & Technologies

Programming Languages

JinjaPythonYAML

Technical Skills

CI/CDDebuggingDeep LearningLLMLLM Performance OptimizationMachine LearningMachine Learning OperationsModel IntegrationModel LoadingModel TestingPyTorchTransformersUnit Testing

Repositories Contributed To

3 repos

Overview of all repositories you've contributed to across your timeline

tenstorrent/tt-forge-models

Aug 2025 Aug 2025
1 Month active

Languages Used

Python

Technical Skills

DebuggingDeep LearningMachine LearningModel IntegrationModel LoadingPyTorch

tenstorrent/tt-xla

Oct 2025 Oct 2025
1 Month active

Languages Used

JinjaPython

Technical Skills

LLMLLM Performance OptimizationPyTorchTransformersUnit Testing

tenstorrent/tt-torch

Jul 2025 Jul 2025
1 Month active

Languages Used

PythonYAML

Technical Skills

CI/CDMachine Learning OperationsModel Testing

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