NVIDIA-NeMo/Automodel
May 2025 – Sep 2025
5 Months active
Languages Used
PythonYAMLShell
Technical Skills
DebuggingDistributed SystemsFSDP2Machine LearningPyTorchTensor Parallelism
Boxiang Wang
PROFILE
Boxiang Wang
Boxiang Wang engineered advanced distributed training infrastructure for the NVIDIA-NeMo/Automodel and Megatron-Bridge repositories, focusing on scalable fine-tuning of large language models. He integrated and stabilized FSDP2, nvFSDP, and Megatron-FSDP strategies, enabling hybrid sharded and tensor parallelism while ensuring compatibility with Hugging Face tooling. Using Python, YAML, and PyTorch, Boxiang refactored model and optimizer construction, improved loss aggregation logic, and introduced robust configuration management. His work included end-to-end distributed training workflows, safety-checked integration, and CI/CD automation, resulting in more reliable, maintainable, and production-ready pipelines for large-scale model training and fine-tuning across diverse distributed environments.
Overall Statistics
Feature vs Bugs
82%Features
Repository Contributions
17Total
Bugs
2
Commits
17
Features
9
Lines of code
14,146
Activity Months5
Your Network
1413 people
Work History
September 2025
4 Commits • 3 Features
Sep 1, 2025September 2025 focused on scaling distributed training capabilities and improving maintainability across NVIDIA-NeMo Automodel and Megatron-Bridge. Delivered end-to-end distributed training enablement with a complete Llama-3.2-1B on HSDP config, standardized Megatron-FSDP usage through a naming refactor, and added safety-checked Megatron-FSDP integration in Megatron-Bridge. These efforts enhance training efficiency, reduce misconfigurations, and lay groundwork for scalable, production-grade workflows.
4 Commits • 3 Features
Sep 1, 2025September 2025 focused on scaling distributed training capabilities and improving maintainability across NVIDIA-NeMo Automodel and Megatron-Bridge. Delivered end-to-end distributed training enablement with a complete Llama-3.2-1B on HSDP config, standardized Megatron-FSDP usage through a naming refactor, and added safety-checked Megatron-FSDP integration in Megatron-Bridge. These efforts enhance training efficiency, reduce misconfigurations, and lay groundwork for scalable, production-grade workflows.
September 2025
August 2025
2 Commits • 2 Features
Aug 1, 2025Monthly summary for NVIDIA-NeMo/Automodel - 2025-08: Delivered two feature enhancements that significantly advance distributed training capabilities and integration with Hugging Face tooling, strengthening scalability, validation, and developer productivity.
August 2025
2 Commits • 2 Features
Aug 1, 2025Monthly summary for NVIDIA-NeMo/Automodel - 2025-08: Delivered two feature enhancements that significantly advance distributed training capabilities and integration with Hugging Face tooling, strengthening scalability, validation, and developer productivity.
July 2025
2 Commits • 2 Features
Jul 1, 2025July 2025 focused on stabilizing and expanding Automodel capabilities in NVIDIA-NeMo/Automodel, delivering API-aligned nvFSDP integration and a practical distributed fine-tuning example for Qwen3-0.6B. The work enhances training reliability, enables scalable experimentation, and improves pipeline automation across NVidia NeMo projects.
2 Commits • 2 Features
Jul 1, 2025July 2025 focused on stabilizing and expanding Automodel capabilities in NVIDIA-NeMo/Automodel, delivering API-aligned nvFSDP integration and a practical distributed fine-tuning example for Qwen3-0.6B. The work enhances training reliability, enables scalable experimentation, and improves pipeline automation across NVidia NeMo projects.
July 2025
June 2025
7 Commits • 1 Features
Jun 1, 2025June 2025: Focused on expanding distributed training capabilities in NVIDIA-NeMo/Automodel through nvFSDP integration and related enhancements. Delivered foundational scaffolding, a new distributed training manager, and sharding plan refinements to enable scalable training across TP/SP/CP, with robust import guards and CI/CD hooks to streamline nvFSDP usage. Fixed a critical issue in loss aggregation for NextTokenPrediction finetuning by switching from mean to sum to ensure correct token-wise loss accumulation. Established codebase groundwork by copying nvFSDP into the Automodel repo ahead of nvFSDP pip packaging, laying the groundwork for future packaging and broader adoption. Overall, these efforts improve model training efficiency, stability, and usability for large-scale deployments.
June 2025
7 Commits • 1 Features
Jun 1, 2025June 2025: Focused on expanding distributed training capabilities in NVIDIA-NeMo/Automodel through nvFSDP integration and related enhancements. Delivered foundational scaffolding, a new distributed training manager, and sharding plan refinements to enable scalable training across TP/SP/CP, with robust import guards and CI/CD hooks to streamline nvFSDP usage. Fixed a critical issue in loss aggregation for NextTokenPrediction finetuning by switching from mean to sum to ensure correct token-wise loss accumulation. Established codebase groundwork by copying nvFSDP into the Automodel repo ahead of nvFSDP pip packaging, laying the groundwork for future packaging and broader adoption. Overall, these efforts improve model training efficiency, stability, and usability for large-scale deployments.
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 — NVIDIA-NeMo/Automodel: Focused on stabilizing distributed training and enabling scalable fine-tuning. Delivered Tensor Parallelism (TP) support in FSDP2 and resolved critical FSDP2 strategy issues to improve training stability and correctness. These changes enable more reliable multi-GPU runs, faster iteration on large models, and reproducible experiments.
2 Commits • 1 Features
May 1, 2025May 2025 — NVIDIA-NeMo/Automodel: Focused on stabilizing distributed training and enabling scalable fine-tuning. Delivered Tensor Parallelism (TP) support in FSDP2 and resolved critical FSDP2 strategy issues to improve training stability and correctness. These changes enable more reliable multi-GPU runs, faster iteration on large models, and reproducible experiments.
May 2025
Activity
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Quality Metrics
Correctness87.2%
Maintainability85.8%
Architecture87.2%
Performance74.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonShellYAML
Technical Skills
CI/CDCI/CD ConfigurationCode RenamingConfiguration ManagementContainerizationContext ParallelismData ParallelismDebuggingDeep LearningDeep Learning FrameworksDependency ManagementDistributed SystemsDistributed TrainingDocumentation UpdateFSDP
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline
NVIDIA-NeMo/Megatron-Bridge
Sep 2025 – Sep 2025
1 Month active
Languages Used
PythonYAML
Technical Skills
Configuration ManagementData ParallelismDeep LearningDeep Learning FrameworksDistributed SystemsLarge Language Models