quic/aimet
Nov 2024 – Mar 2025
4 Months active
Languages Used
JinjaPythonGit Attributesrst
Technical Skills
Code RefactoringFull Stack DevelopmentGraph ManipulationMachine Learning EngineeringModel OptimizationPyTorch
Ashvin Kumar
PROFILE
Ashvin Kumar
Worked on the quic/aimet repository to enhance quantization workflows for large language models, focusing on mixed-precision support and LoRa integration. Developed APIs for precise input and output quantization control, refactored graph traversal logic for maintainability, and improved code quality through linting and type hint updates in Python. Introduced documentation for LoRa-enabled quantized models and implemented repository hygiene measures using Git. Delivered quantization tooling for LoRa models, including a BlockwiseSampler in PyTorch to optimize sequential block processing and dynamic parameter adjustment. These contributions improved model optimization, edge deployment readiness, and developer onboarding, emphasizing robust code organization and maintainable engineering practices.
Overall Statistics
Feature vs Bugs
57%Features
Repository Contributions
12Total
Bugs
3
Commits
12
Features
4
Lines of code
4,664
Activity Months4
Your Network
232 peopleShared Repositories
47
Khobare, AbhijitMember
Mukherjee, AnanyaMember
Kumar, AshvinMember
Sonawane, BhushanMember
Xu, BozheMember
Yuan, ChaoMember
Dinh, Dao Lan VyMember
Gajula, Sai ChaitanyaMember
Mehta, HitarthMember
Work History
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025: Delivered quantization tooling enhancements for LoRa models in quic/aimet, enabling compatibility with quantization simulation checks (quantsim) via updated LoRa instantiation, configuration, and layer selection. Introduced BlockwiseSampler for PyTorch to efficiently sample inputs for sequential blocks with input caching and dynamic quantization parameter adjustments, plus a helper for block-by-block inference and a generator yielding blocks with FP and QT inputs. Updated examples demonstrate end-to-end usage (QW-lora and QWA-lora). These changes improve edge deployment readiness, quantization accuracy, and iteration speed, driving business value in edge AI deployments.
2 Commits • 1 Features
Mar 1, 2025March 2025: Delivered quantization tooling enhancements for LoRa models in quic/aimet, enabling compatibility with quantization simulation checks (quantsim) via updated LoRa instantiation, configuration, and layer selection. Introduced BlockwiseSampler for PyTorch to efficiently sample inputs for sequential blocks with input caching and dynamic quantization parameter adjustments, plus a helper for block-by-block inference and a generator yielding blocks with FP and QT inputs. Updated examples demonstrate end-to-end usage (QW-lora and QWA-lora). These changes improve edge deployment readiness, quantization accuracy, and iteration speed, driving business value in edge AI deployments.
March 2025
February 2025
2 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for quic/aimet focused on documentation and repository hygiene. Delivered concise, actionable documentation for Low-Rank Adaptation workflows (QW-LoRa and QWA-LoRa) and implemented safeguards to prevent macOS-specific artifacts from polluting the repo. These changes facilitate faster adoption of LoRa-enabled quantized LLM workflows and improve repository cleanliness for easier collaboration and maintenance.
February 2025
2 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for quic/aimet focused on documentation and repository hygiene. Delivered concise, actionable documentation for Low-Rank Adaptation workflows (QW-LoRa and QWA-LoRa) and implemented safeguards to prevent macOS-specific artifacts from polluting the repo. These changes facilitate faster adoption of LoRa-enabled quantized LLM workflows and improve repository cleanliness for easier collaboration and maintenance.
December 2024
5 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary focusing on key accomplishments, business value, and technical impact within quic/aimet.
5 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary focusing on key accomplishments, business value, and technical impact within quic/aimet.
December 2024
November 2024
3 Commits • 1 Features
Nov 1, 2024Monthly summary for 2024-11 focused on QuantSim mixed-precision improvements and graph-structure robustness in quic/aimet. Deliveries centered on upstream request handling for MPC workflows and preservation/recovery of input/output mappings in Torch ConnectedGraph, enabling reliable optimization for complex tensor structures. The month also included code-quality hardening (pylint/warnings) to reduce maintenance friction and surface issues early.
November 2024
3 Commits • 1 Features
Nov 1, 2024Monthly summary for 2024-11 focused on QuantSim mixed-precision improvements and graph-structure robustness in quic/aimet. Deliveries centered on upstream request handling for MPC workflows and preservation/recovery of input/output mappings in Torch ConnectedGraph, enabling reliable optimization for complex tensor structures. The month also included code-quality hardening (pylint/warnings) to reduce maintenance friction and surface issues early.
Activity
Loading activity data...
Quality Metrics
Correctness92.6%
Maintainability90.8%
Architecture90.0%
Performance84.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
Git AttributesJinjaPythonrst
Technical Skills
Code LintingCode OrganizationCode QualityCode RefactoringConfiguration ManagementData LoadingDocumentationFull Stack DevelopmentGitGraph ManipulationGraph TraversalHugging Face TransformersLarge Language ModelsLoRaMachine Learning
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline