huggingface/transformers
Apr 2026 – May 2026
2 Months active
Languages Used
Python
Technical Skills
Configuration ManagementDeep LearningMachine LearningModel OptimizationPythondata processing
AmineDiro
PROFILE
Aminediro
Worked on the huggingface/transformers repository to implement expert parallelism configuration for Qwen3 Mixture-of-Experts (MoE) models, enabling sharding of MoE experts while keeping attention layers unsharded to improve scalability and resource utilization. Leveraged Python and deep learning techniques to add base_model_ep_plan support, introduce expert-only EP plans, and align configuration outputs with modular converters. Subsequently focused on distributed training reliability by fixing loss over-counting in multi-process setups, ensuring accurate loss calculation under tensor parallelism and expert parallelism-as-TP. This work enhanced experiment reproducibility and training stability, demonstrating depth in configuration management, model optimization, and distributed machine learning workflows.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
42
Activity Months2
Your Network
398 peopleSame Organization
@huggingface.co63
Work History
May 2026
1 Commits
May 1, 2026May 2026 monthly summary for huggingface/transformers. Focused on reliability and accuracy improvements in multi-process training. No new features released; primary work was a bug fix improving loss calculation under tensor parallelism (TP) and expert parallelism-as-TP (EP-as-TP) in the Trainer, ensuring loss reflects actual process count and avoids inflated metrics. This enhances experiment reproducibility, model comparison, and training stability across distributed setups.
1 Commits
May 1, 2026May 2026 monthly summary for huggingface/transformers. Focused on reliability and accuracy improvements in multi-process training. No new features released; primary work was a bug fix improving loss calculation under tensor parallelism (TP) and expert parallelism-as-TP (EP-as-TP) in the Trainer, ensuring loss reflects actual process count and avoids inflated metrics. This enhances experiment reproducibility, model comparison, and training stability across distributed setups.
May 2026
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026: Implemented Expert Parallelism (EP) configuration for Qwen3 MoE models in huggingface/transformers, enabling sharding of MoE experts while keeping attention layers unsharded. This change increases scalability and resource utilization for large model configurations, allowing deployments beyond previous constraints. Key changes include adding base_model_ep_plan support to Qwen3MoE and Qwen3VLMoe configs, introducing an expert-only EP plan, removing duplicate base_model_ep_plan entries, and aligning generated configs with modular converter outputs. The work leverages FSDP2 for attention distribution and was coordinated with co-authors. Commit: 7cf4241f2ec97688edefb15bebe24ff6f7de29f5.
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026: Implemented Expert Parallelism (EP) configuration for Qwen3 MoE models in huggingface/transformers, enabling sharding of MoE experts while keeping attention layers unsharded. This change increases scalability and resource utilization for large model configurations, allowing deployments beyond previous constraints. Key changes include adding base_model_ep_plan support to Qwen3MoE and Qwen3VLMoe configs, introducing an expert-only EP plan, removing duplicate base_model_ep_plan entries, and aligning generated configs with modular converter outputs. The work leverages FSDP2 for attention distribution and was coordinated with co-authors. Commit: 7cf4241f2ec97688edefb15bebe24ff6f7de29f5.
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Configuration ManagementDeep LearningMachine LearningModel OptimizationPythondata processingdeep learningmachine learning
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