AMD-AGI/Primus
Sep 2025 – Jan 2026
4 Months active
Languages Used
yamlYAMLPython
Technical Skills
Configuration ManagementDeep LearningModel Trainingconfiguration managementdata preprocessingmachine learning
clairesonglee
PROFILE
Clairesonglee
Claire Lee contributed to the AMD-AGI/Primus repository by engineering performance and stability improvements for large-scale deep learning model training. She optimized training workflows for Llama 3.1 and DeepSeek V3 models, introducing float8 and BF16/FP8 precision support, batch size tuning, and Turbo Attention integration to maximize throughput on MI355X hardware. Her work involved Python and YAML for configuration management, model training, and automated testing, addressing both feature delivery and critical bug fixes. By refining YAML-based pretraining configurations and enhancing test coverage, Claire improved training reliability, efficiency, and reproducibility, demonstrating a strong grasp of scalable machine learning infrastructure and workflow optimization.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
5Total
Bugs
1
Commits
5
Features
3
Lines of code
530
Activity Months4
Your Network
1313 people
Work History
January 2026
2 Commits • 1 Features
Jan 1, 2026January 2026 performance sprint for AMD-AGI/Primus focused on enhancing MI355X DeepSeek V3 throughput. Implemented batch-size maximization with separate BF16 and FP8 configurations and activated Turbo Attention to improve TGS performance. Updated tests to cover the new configurations and transitions. These changes are reflected in the commit history and position Primus for higher-throughput inference on MI355X. No major bug fixes were required this month; stabilization work continues in follow-up sprints.
2 Commits • 1 Features
Jan 1, 2026January 2026 performance sprint for AMD-AGI/Primus focused on enhancing MI355X DeepSeek V3 throughput. Implemented batch-size maximization with separate BF16 and FP8 configurations and activated Turbo Attention to improve TGS performance. Updated tests to cover the new configurations and transitions. These changes are reflected in the commit history and position Primus for higher-throughput inference on MI355X. No major bug fixes were required this month; stabilization work continues in follow-up sprints.
January 2026
December 2025
1 Commits • 1 Features
Dec 1, 2025Month: 2025-12 – Summary for AMD-AGI/Primus focusing on training performance optimizations. Delivered a DeepSeek-V3-16B BF16 training throughput improvement by increasing the batch size, enabling faster experimentation and better GPU utilization. Change tracked under commit 4bccca9052548db927f1f7dcfff25f0cd6c5c4e7 with message 'Increase DeepSeek-V3-16B BF16 batch size (#367)'. No major bugs fixed this month; efforts concentrated on stability, efficiency, and scalable training in Primus.
December 2025
1 Commits • 1 Features
Dec 1, 2025Month: 2025-12 – Summary for AMD-AGI/Primus focusing on training performance optimizations. Delivered a DeepSeek-V3-16B BF16 training throughput improvement by increasing the batch size, enabling faster experimentation and better GPU utilization. Change tracked under commit 4bccca9052548db927f1f7dcfff25f0cd6c5c4e7 with message 'Increase DeepSeek-V3-16B BF16 batch size (#367)'. No major bugs fixed this month; efforts concentrated on stability, efficiency, and scalable training in Primus.
November 2025
1 Commits
Nov 1, 2025November 2025 monthly summary for AMD-AGI/Primus: Delivered a critical stability improvement for pretraining configurations by disabling cross-entropy flags across YAML files, addressing convergence loss/divergence in large-model training setups. This change reduces failed runs and improves training reliability for large-scale experiments, enhancing research throughput.
1 Commits
Nov 1, 2025November 2025 monthly summary for AMD-AGI/Primus: Delivered a critical stability improvement for pretraining configurations by disabling cross-entropy flags across YAML files, addressing convergence loss/divergence in large-model training setups. This change reduces failed runs and improves training reliability for large-scale experiments, enhancing research throughput.
November 2025
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for AMD-AGI/Primus focused on delivering performance-oriented enhancements in the Primus-Turbo and Llama 3.1 training workflow. Key delivery includes Primus-Turbo support integrated into the torchtitan framework, enabling optimized training configurations for Llama models; float8 precision configured for Llama 3.1 (70B and 8B variants); and training parameter tuning (batch size and steps) to improve throughput and convergence. The work is anchored by commit 94878414b44964bf38c7d2fd2965875e392f5bbe. This milestone drives faster, more cost-efficient model training and readiness for production use.
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for AMD-AGI/Primus focused on delivering performance-oriented enhancements in the Primus-Turbo and Llama 3.1 training workflow. Key delivery includes Primus-Turbo support integrated into the torchtitan framework, enabling optimized training configurations for Llama models; float8 precision configured for Llama 3.1 (70B and 8B variants); and training parameter tuning (batch size and steps) to improve throughput and convergence. The work is anchored by commit 94878414b44964bf38c7d2fd2965875e392f5bbe. This milestone drives faster, more cost-efficient model training and readiness for production use.
Activity
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Quality Metrics
Correctness92.0%
Maintainability84.0%
Architecture84.0%
Performance88.0%
AI Usage32.0%
Skills & Technologies
Programming Languages
PythonYAMLyaml
Technical Skills
Configuration ManagementDeep LearningModel Trainingconfiguration managementdata preprocessingdeep learningmachine learningmodel trainingtesting
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