quic/efficient-transformers
Feb 2025 – Feb 2025
1 Month active
Languages Used
Python
Technical Skills
Cloud AI 100Deep LearningFull Stack DevelopmentMachine LearningModel OptimizationONNX
kdulla
PROFILE
Kdulla
Developed Whisper model support within the quic/efficient-transformers repository, enabling compilation and execution of the OpenAI Whisper architecture on Cloud AI 100 hardware. This work involved integrating Whisper into the QEfficient framework, updating model handling, export, and generation processes to address Whisper-specific requirements, and preparing the pipeline for Whisper-based inference. Leveraging Python, ONNX, and deep learning techniques, the developer focused on enhancing model coverage and deployment scalability for speech recognition tasks. The integration laid the groundwork for broader OpenAI model compatibility, reflecting a deep understanding of model optimization and full stack development in cloud-based machine learning environments.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,851
Activity Months1
Your Network
41 peopleShared Repositories
41
Abukhoyer ShaikMember
Amit RajMember
Anuj GuptaMember
asmigoswMember
Dhiraj Kumar SahMember
Dipankar SarkarMember
Hem AgnihotriMember
kdullaMember
sudheepm-wqMember
Work History
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered Whisper model support in QEfficient and prepared the pipeline for Whisper-based inference on Cloud AI 100, enhancing model coverage and deployment scalability. This work includes integration of Whisper architecture into QEfficient, updates to handling, export, and generation to accommodate Whisper-specific requirements, and groundwork for broader OpenAI model compatibility.
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered Whisper model support in QEfficient and prepared the pipeline for Whisper-based inference on Cloud AI 100, enhancing model coverage and deployment scalability. This work includes integration of Whisper architecture into QEfficient, updates to handling, export, and generation to accommodate Whisper-specific requirements, and groundwork for broader OpenAI model compatibility.
February 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Cloud AI 100Deep LearningFull Stack DevelopmentMachine LearningModel OptimizationONNXSpeech RecognitionTransformers
Repositories Contributed To
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