AMD-AGI/Primus
Sep 2025 – Mar 2026
6 Months active
Languages Used
yamlYAMLPython
Technical Skills
Configuration ManagementDeep LearningModel Trainingconfiguration managementdata preprocessingmachine learning
clairesonglee
PROFILE
Clairesonglee
Over six months, contributed to AMD-AGI/Primus by building and optimizing large language model training workflows, focusing on configuration management and deep learning performance. Developed features such as Primus-Turbo integration, float8 and FP8 precision support, and batch size tuning to improve throughput and convergence for Llama and DeepSeek models. Addressed training stability by refining YAML-based configuration files and disabling problematic flags, enhancing reproducibility and reliability. Leveraged Python and YAML to implement scalable pretraining suites and hybrid model specifications, while collaborating on dockerized releases to streamline deployment. The work emphasized robust model training, efficient resource utilization, and alignment with evolving research practices.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
7Total
Bugs
1
Commits
7
Features
5
Lines of code
2,923
Activity Months6
Your Network
1603 peopleSame Organization
@amd.com1561
7b30f3f5e26d48061f873d04cc7e1d1f_amdengMember
GunaShekar, AjayMember
aasbodduMember
Abdul Lateef AttarMember
Acim MaravicMember
Adam DickinMember
Pryor, AdamMember
Pryor, AdamMember
Adel JoharMember
Shared Repositories
42
Arik GelmanMember
Alexander SuslovMember
Alexander SuslovMember
Anshu RainaMember
botaohu001Member
Vivicai1005Member
chaojhouMember
Chris CaiMember
ChengYao-amdMember
Work History
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026: Delivered Megatron training configuration enhancements for large language models within AMD-AGI/Primus, including support for hybrid model specifications, cross-entropy loss fusion, adjusted training parameters, and new Zebra Llama and Mamba configurations to boost training performance, scalability, and flexibility. Completed and released Primus docker release/v26.2 (PR #579) with broad cross-team collaboration and multi-author contributions, improving deployment reliability and reproducibility.
1 Commits • 1 Features
Mar 1, 2026March 2026: Delivered Megatron training configuration enhancements for large language models within AMD-AGI/Primus, including support for hybrid model specifications, cross-entropy loss fusion, adjusted training parameters, and new Zebra Llama and Mamba configurations to boost training performance, scalability, and flexibility. Completed and released Primus docker release/v26.2 (PR #579) with broad cross-team collaboration and multi-author contributions, improving deployment reliability and reproducibility.
March 2026
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 — AMD-AGI/Primus monthly summary: Delivered Llama 3.2 Pretraining Configuration Suite with FP8 training and Turbo features for 1B/3B variants, enabling flexible, FP8-enabled pretraining workflows. Commit: b479a2f387063fa019971a04ce8cedf2418d6104. No major bugs reported this month. Overall impact includes accelerated experiment setup, improved reproducibility, and alignment with Megatron-LM Llama 3.2 workflows. Demonstrated skills in configuration management, FP8/Turbo-enabled training, and end-to-end pretraining workflow integration in Primus.
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 — AMD-AGI/Primus monthly summary: Delivered Llama 3.2 Pretraining Configuration Suite with FP8 training and Turbo features for 1B/3B variants, enabling flexible, FP8-enabled pretraining workflows. Commit: b479a2f387063fa019971a04ce8cedf2418d6104. No major bugs reported this month. Overall impact includes accelerated experiment setup, improved reproducibility, and alignment with Megatron-LM Llama 3.2 workflows. Demonstrated skills in configuration management, FP8/Turbo-enabled training, and end-to-end pretraining workflow integration in Primus.
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
Loading activity data...
Quality Metrics
Correctness91.4%
Maintainability82.8%
Architecture85.8%
Performance85.8%
AI Usage42.8%
Skills & Technologies
Programming Languages
PythonYAMLyaml
Technical Skills
Configuration ManagementDeep LearningMachine LearningModel TrainingNLPconfiguration managementdata preprocessingdeep learningmachine learningmodel trainingtesting
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline