quic/efficient-transformers
Nov 2025 – Feb 2026
4 Months active
Languages Used
Python
Technical Skills
Deep LearningMachine LearningNatural Language ProcessingONNXPyTorchModel Optimization
vjanfaza
PROFILE
Vjanfaza
Vahid Janfaza developed and optimized Compute-Context-Length (CCL) features for the quic/efficient-transformers repository, focusing on improving large language model throughput and deployment flexibility on Qualcomm devices. He introduced dynamic context-length specialization using ONNX and PyTorch, enabling efficient memory and attention computation during token generation. Vahid enhanced usability by automating CCL configuration and validation, reducing manual setup and misconfiguration risks. He extended the framework to support dense models distilled from mixture-of-experts architectures, broadening compatibility for model transformations. His work included backend development, algorithm optimization, and data validation in Python, resulting in robust, hardware-aware model optimization and more reliable inference workflows.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
7Total
Bugs
2
Commits
7
Features
4
Lines of code
7,479
Activity Months4
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: delivered key features and fixed critical issues to enhance model compatibility, reliability, and deployment options across dense model transformations and disaggregated serving workflows. Key features delivered: - Dense Model Support in QEfficient: Added support for dense models distilled from mixture of experts (MoE), enabling integration of meta-llama/Llama-Guard-4-12B. Extends QEfficient to accommodate diverse dense models with similar architectures, improving compatibility for model transformations. Major bugs fixed: - Disaggregated Serving: CCL Decoding Fix: Resolved compilation errors when enabling CCL during decoding in the gpt-oss model. Adjusted decoding to handle appropriate context lengths and attention masks, and added a new example script demonstrating decoding with CCL enabled. Overall impact and accomplishments: - Expanded deployment options by enabling dense-model transformations in QEfficient, accelerating experimentation with MoE-derived dense models. - Increased reliability of Disaggregated Serving workflows by eliminating decode-time compilation blockers and clarifying CCL-enabled decoding paths. - Strengthened end-to-end model transformation pipelines, reducing integration effort for dense models and improving operational stability in production scenarios. Technologies/skills demonstrated: - PyTorch-based transforms and model distillation workflows, attention mask handling, and context-length management. - CCL integration and debugging in disaggregated serving pipelines. - Clear commit-level traceability with changes tied to concrete model architectures and usage scenarios.
2 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for quic/efficient-transformers: delivered key features and fixed critical issues to enhance model compatibility, reliability, and deployment options across dense model transformations and disaggregated serving workflows. Key features delivered: - Dense Model Support in QEfficient: Added support for dense models distilled from mixture of experts (MoE), enabling integration of meta-llama/Llama-Guard-4-12B. Extends QEfficient to accommodate diverse dense models with similar architectures, improving compatibility for model transformations. Major bugs fixed: - Disaggregated Serving: CCL Decoding Fix: Resolved compilation errors when enabling CCL during decoding in the gpt-oss model. Adjusted decoding to handle appropriate context lengths and attention masks, and added a new example script demonstrating decoding with CCL enabled. Overall impact and accomplishments: - Expanded deployment options by enabling dense-model transformations in QEfficient, accelerating experimentation with MoE-derived dense models. - Increased reliability of Disaggregated Serving workflows by eliminating decode-time compilation blockers and clarifying CCL-enabled decoding paths. - Strengthened end-to-end model transformation pipelines, reducing integration effort for dense models and improving operational stability in production scenarios. Technologies/skills demonstrated: - PyTorch-based transforms and model distillation workflows, attention mask handling, and context-length management. - CCL integration and debugging in disaggregated serving pipelines. - Clear commit-level traceability with changes tied to concrete model architectures and usage scenarios.
February 2026
January 2026
2 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for quic/efficient-transformers: Delivered focused CCL handling enhancements and safety improvements to reduce misconfigurations, increasing robustness of context length processing and inference defaults.
January 2026
2 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for quic/efficient-transformers: Delivered focused CCL handling enhancements and safety improvements to reduce misconfigurations, increasing robustness of context length processing and inference defaults.
December 2025
2 Commits • 1 Features
Dec 1, 2025December 2025: Feature-focused month for quic/efficient-transformers centered on Compute-Context-Length (CCL) improvements. Delivered a ccl_enabled flag during model loading and moved passing of CCL lists to the compilation stage to enable dynamic context-length tuning across model types. Added automatic generation of CCL lists for prefill and decode when users do not provide them, enhancing usability and reducing manual configuration. No distinct bug fixes reported this month; primary value comes from hardware-aware performance tuning and deployment flexibility. Business impact includes faster optimal configurations, easier deployment, and broader applicability of CCL optimization across workloads. Technologies demonstrated include flag-based configuration, build/compile pipeline integration, and automated list generation, with collaboration evidenced by co-authored commits (#623, #663).
2 Commits • 1 Features
Dec 1, 2025December 2025: Feature-focused month for quic/efficient-transformers centered on Compute-Context-Length (CCL) improvements. Delivered a ccl_enabled flag during model loading and moved passing of CCL lists to the compilation stage to enable dynamic context-length tuning across model types. Added automatic generation of CCL lists for prefill and decode when users do not provide them, enhancing usability and reducing manual configuration. No distinct bug fixes reported this month; primary value comes from hardware-aware performance tuning and deployment flexibility. Business impact includes faster optimal configurations, easier deployment, and broader applicability of CCL optimization across workloads. Technologies demonstrated include flag-based configuration, build/compile pipeline integration, and automated list generation, with collaboration evidenced by co-authored commits (#623, #663).
December 2025
November 2025
1 Commits • 1 Features
Nov 1, 2025Month: 2025-11. Delivered a performance-focused feature for on-device LLM throughput on Qualcomm devices by introducing Compute-Context-Length (CCL) and dynamic context-length specialization. The work centers on the quic/efficient-transformers repository and leverages ONNX variables to optimize token generation during prefilling and decoding, reducing unnecessary memory reads and expensive attention computations. No major bug fixes were completed this month; the emphasis was on robust feature delivery and performance gains with clear business value for on-device inference.
November 2025
1 Commits • 1 Features
Nov 1, 2025Month: 2025-11. Delivered a performance-focused feature for on-device LLM throughput on Qualcomm devices by introducing Compute-Context-Length (CCL) and dynamic context-length specialization. The work centers on the quic/efficient-transformers repository and leverages ONNX variables to optimize token generation during prefilling and decoding, reducing unnecessary memory reads and expensive attention computations. No major bug fixes were completed this month; the emphasis was on robust feature delivery and performance gains with clear business value for on-device inference.
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Quality Metrics
Correctness88.6%
Maintainability80.0%
Architecture85.8%
Performance82.8%
AI Usage45.8%
Skills & Technologies
Programming Languages
Python
Technical Skills
Deep LearningMachine LearningModel OptimizationNatural Language ProcessingONNXPyTorchPythonPython programmingalgorithm optimizationbackend developmentdata validation
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