AMD-AGI/Primus
Sep 2025 – Jul 2026
9 Months active
Languages Used
yamlYAMLPython
Technical Skills
Configuration ManagementDeep LearningModel Trainingconfiguration managementdata preprocessingmachine learning
clairesonglee
PROFILE
Clairesonglee
Worked on the AMD-AGI/Primus repository, delivering features and stability improvements for large language model training and deployment. Focused on configuration management, model optimization, and dependency management, the work included implementing performance enhancements for Llama and DeepSeek models, enabling FP8 and BF16 precision, and introducing supervised fine-tuning for Mamba models. Addressed training stability by refining YAML-based workflows and managing Python dependencies to ensure reproducibility and compatibility. Collaborated across teams to update training scripts, optimize batch sizing, and improve TFLOPS estimation using Python and YAML. The contributions supported scalable, efficient machine learning infrastructure and streamlined experimentation for production-scale NLP workloads.
Overall Statistics
Feature vs Bugs
70%Features
Repository Contributions
11Total
Bugs
3
Commits
11
Features
7
Lines of code
4,946
Activity Months9
Your Network
1701 peopleSame Organization
@amd.com1657
7b30f3f5e26d48061f873d04cc7e1d1f_amdengMember
GunaShekar, AjayMember
aasbodduMember
Abdul Lateef AttarMember
Acim MaravicMember
Adam DickinMember
Pryor, AdamMember
Pryor, AdamMember
Adel JoharMember
Work History
July 2026
1 Commits
Jul 1, 2026July 2026 monthly summary for AMD-AGI/Primus focusing on stabilizing Megatron pretraining workflows by aligning dependencies with the current environment and improving reproducibility. Delivered a compatibility fix in the Megatron pretraining environment to ensure alignment with the GatedDeltaNet path, reducing runtime issues and environment drift.
1 Commits
Jul 1, 2026July 2026 monthly summary for AMD-AGI/Primus focusing on stabilizing Megatron pretraining workflows by aligning dependencies with the current environment and improving reproducibility. Delivered a compatibility fix in the Megatron pretraining environment to ensure alignment with the GatedDeltaNet path, reducing runtime issues and environment drift.
July 2026
June 2026
2 Commits • 1 Features
Jun 1, 2026Month: 2026-06. Delivered SFT (supervised fine-tuning) support for Mamba models via Megatron-Bridge in AMD-AGI/Primus, including new configuration files for multiple model sizes and training script updates to enable efficient SFT and improved downstream task performance. Addressed compatibility risk by upgrading Flash-Linear-Attention to 0.5.1 to align with Megatron core GatedDeltaNet, preventing runtime issues.
June 2026
2 Commits • 1 Features
Jun 1, 2026Month: 2026-06. Delivered SFT (supervised fine-tuning) support for Mamba models via Megatron-Bridge in AMD-AGI/Primus, including new configuration files for multiple model sizes and training script updates to enable efficient SFT and improved downstream task performance. Addressed compatibility risk by upgrading Flash-Linear-Attention to 0.5.1 to align with Megatron core GatedDeltaNet, preventing runtime issues.
May 2026
1 Commits • 1 Features
May 1, 2026May 2026 performance summary for AMD-AGI/Primus: Delivered a focused feature to improve TFLOPS estimation for hybrid models using Multi-Latent Attention (MLA). Implemented a patch to TFLOPS calculation, updated configuration files, and added a new Python script to produce accurate FLOPs estimates, enabling more reliable performance metrics and faster optimization cycles. No major bugs reported; the patch underwent a collaborative review (PR #594) with contributions from multiple engineers across teams.
1 Commits • 1 Features
May 1, 2026May 2026 performance summary for AMD-AGI/Primus: Delivered a focused feature to improve TFLOPS estimation for hybrid models using Multi-Latent Attention (MLA). Implemented a patch to TFLOPS calculation, updated configuration files, and added a new Python script to produce accurate FLOPs estimates, enabling more reliable performance metrics and faster optimization cycles. No major bugs reported; the patch underwent a collaborative review (PR #594) with contributions from multiple engineers across teams.
May 2026
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.
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.
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.
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
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.
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.
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.
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
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.
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.
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.
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
Activity
Loading activity data...
Quality Metrics
Correctness92.8%
Maintainability85.4%
Architecture85.4%
Performance83.6%
AI Usage52.8%
Skills & Technologies
Programming Languages
PythonYAMLyaml
Technical Skills
CI/CDConfiguration ManagementDeep LearningMachine LearningModel OptimizationModel TrainingNLPPythonPython Developmentconfiguration managementdata preprocessingdeep learningdependency managementmachine learningmachine learning infrastructure
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline