microsoft/olive-recipes
Oct 2025 – Nov 2025
2 Months active
Languages Used
BashMarkdownYAMLPython
Technical Skills
Configuration ManagementDevOpsGPU ComputingModel OptimizationQuantizationData Processing
vjatoth-qti
PROFILE
Vjatoth-qti
Vjatoth worked on the microsoft/olive-recipes repository, delivering GPU-accelerated model inference support for both general and non-LLM models such as BERT, ViT, and CLIP. Using Python and YAML, Vjatoth implemented QNN-GPU execution via the QNN Execution Provider, updating model configurations and documentation to reflect new optimization and compilation settings. The work included configuration-driven scripts and detailed JSON specifications for model input, evaluation metrics, and data preprocessing, enabling reproducible and scalable GPU inference. By enforcing compatibility with specific Olive commits, Vjatoth ensured stable deployments and improved throughput, demonstrating depth in configuration management, model optimization, and GPU computing.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
2,244
Activity Months2
Your Network
192 peopleSame Organization
@qti.qualcomm.com171
Work History
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025 (microsoft/olive-recipes): Delivered GPU-accelerated inference configurations for non-LLMs (BERT, ViT, CLIP) using Olive-QNN-GPU. Added configuration-driven scripts and detailed JSON specs for model input, evaluation metrics, and data preprocessing. This work enables GPU-optimized inference, improving performance and reproducibility for non-LLM tasks. No major bugs fixed this month. Impact includes reduced latency, higher throughput, and clearer operational configurations for Olive-QNN-GPU integration.
1 Commits • 1 Features
Nov 1, 2025November 2025 (microsoft/olive-recipes): Delivered GPU-accelerated inference configurations for non-LLMs (BERT, ViT, CLIP) using Olive-QNN-GPU. Added configuration-driven scripts and detailed JSON specs for model input, evaluation metrics, and data preprocessing. This work enables GPU-optimized inference, improving performance and reproducibility for non-LLM tasks. No major bugs fixed this month. Impact includes reduced latency, higher throughput, and clearer operational configurations for Olive-QNN-GPU integration.
November 2025
October 2025
1 Commits • 1 Features
Oct 1, 2025Implemented QNN-GPU execution support in Olive recipes (via QNN-EP) to enable GPU-accelerated model execution, updated docs and model configs for multiple models to reflect QNN-GPU optimization and compilation settings, and enforced compatibility with a referenced Olive commit for reliable deployments. This work, linked to commit 5a0958d9af7317f3155227cb9dde20b9b62d9d96, enhances performance, scalability, and reproducibility of Olive-based workflows.
October 2025
1 Commits • 1 Features
Oct 1, 2025Implemented QNN-GPU execution support in Olive recipes (via QNN-EP) to enable GPU-accelerated model execution, updated docs and model configs for multiple models to reflect QNN-GPU optimization and compilation settings, and enforced compatibility with a referenced Olive commit for reliable deployments. This work, linked to commit 5a0958d9af7317f3155227cb9dde20b9b62d9d96, enhances performance, scalability, and reproducibility of Olive-based workflows.
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
BashMarkdownPythonYAML
Technical Skills
Configuration ManagementData ProcessingDeep LearningDevOpsGPU ComputingMachine LearningModel OptimizationQuantization
Repositories Contributed To
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