quic/efficient-transformers
Oct 2025 – Feb 2026
5 Months active
Languages Used
Python
Technical Skills
CLI DevelopmentConfiguration ManagementFile HandlingModel ExportPythonDeep Learning
Abhishek Kumar Singh
PROFILE
Abhishek Kumar Singh
Abhishek Kumar Singh contributed to the quic/efficient-transformers repository by developing and optimizing features for model export, performance, and reliability across deep learning workflows. He enhanced CLI automation and configuration management using Python, improving deployment resilience and reducing manual errors. Abhishek implemented ONNX export capabilities and optimized data processing pipelines with concurrency, enabling faster inference and scalable model handling. His work addressed stability in Vision-Language Models and introduced robust subfunction export mechanisms for Causal LMs and MOE models, focusing on cross-model compatibility and efficient expert routing. The engineering demonstrated depth in model orchestration, threading, and export optimization for production environments.
Overall Statistics
Feature vs Bugs
57%Features
Repository Contributions
11Total
Bugs
3
Commits
11
Features
4
Lines of code
2,387
Activity Months5
Your Network
205 peopleSame Organization
@qti.qualcomm.com167
Abukhoyer ShaikMember
Sam ElliottMember
Khobare, AbhijitMember
Abhilasha MannaMember
Amit RajMember
Mukherjee, AnanyaMember
Anil YadavMember
Arohi PaigavanMember
Aparna M PMember
Shared Repositories
38
Work History
February 2026
2 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for quic/efficient-transformers. Focused on delivering cross-model robustness for Causal LM exports and enhancing MOE-based routing with one-hot expert selection. Major work concentrated on fixing a set of subfunction export issues across multiple models and introducing decoder/output improvements for MOE models, with careful attention to legacy cache compatibility and attention efficiency. The work reduces export-time failures, improves cross-model reliability, and demonstrates strong software engineering practices in model orchestration and testing.
2 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for quic/efficient-transformers. Focused on delivering cross-model robustness for Causal LM exports and enhancing MOE-based routing with one-hot expert selection. Major work concentrated on fixing a set of subfunction export issues across multiple models and introducing decoder/output improvements for MOE models, with careful attention to legacy cache compatibility and attention efficiency. The work reduces export-time failures, improves cross-model reliability, and demonstrates strong software engineering practices in model orchestration and testing.
February 2026
January 2026
3 Commits • 1 Features
Jan 1, 2026In January 2026, delivered stability and exportability improvements for Vision-Language Models in quic/efficient-transformers, focusing on Qwen 2.5 VL stability and ONNX subfunction export support. Highlights include a targeted stability fix for the decoder layer extraction and rotary embedding, and the introduction of subfunction extraction across VLM classes with enhanced ONNX export compatibility.
January 2026
3 Commits • 1 Features
Jan 1, 2026In January 2026, delivered stability and exportability improvements for Vision-Language Models in quic/efficient-transformers, focusing on Qwen 2.5 VL stability and ONNX subfunction export support. Highlights include a targeted stability fix for the decoder layer extraction and rotary embedding, and the introduction of subfunction extraction across VLM classes with enhanced ONNX export compatibility.
December 2025
2 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for quic/efficient-transformers focusing on performance improvements, stability, and scalability in ONNX-based transforms. The work emphasizes business value through faster inference, lower compute costs, and improved data handling for large models.
2 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for quic/efficient-transformers focusing on performance improvements, stability, and scalability in ONNX-based transforms. The work emphasizes business value through faster inference, lower compute costs, and improved data handling for large models.
December 2025
November 2025
3 Commits • 1 Features
Nov 1, 2025Monthly summary for 2025-11: Delivered core architectural and feature enhancements to the quic/efficient-transformers repository, focusing on subfunctions framework improvements, ONNX export capabilities, and Granite-ready decoding for future CB support. These changes increase hardware efficiency, enable more flexible model deployment, and reduce time-to-market for future CB-enabled features.
November 2025
3 Commits • 1 Features
Nov 1, 2025Monthly summary for 2025-11: Delivered core architectural and feature enhancements to the quic/efficient-transformers repository, focusing on subfunctions framework improvements, ONNX export capabilities, and Granite-ready decoding for future CB support. These changes increase hardware efficiency, enable more flexible model deployment, and reduce time-to-market for future CB-enabled features.
October 2025
1 Commits
Oct 1, 2025Month: 2025-10 – quic/efficient-transformers: The month focused on enhancing CLI reliability, automation, and storage efficiency.
1 Commits
Oct 1, 2025Month: 2025-10 – quic/efficient-transformers: The month focused on enhancing CLI reliability, automation, and storage efficiency.
October 2025
Activity
Loading activity data...
Quality Metrics
Correctness84.6%
Maintainability80.0%
Architecture83.6%
Performance81.8%
AI Usage45.4%
Skills & Technologies
Programming Languages
Python
Technical Skills
CLI DevelopmentConcurrencyConfiguration ManagementData ProcessingDeep LearningFile HandlingMachine LearningModel DevelopmentModel ExportModel ExportingModel OptimizationONNXPerformance OptimizationPyTorchPython
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline