IBM/unitxt
May 2025 – Jan 2026
2 Months active
Languages Used
Python
Technical Skills
data processingimage processingmachine learningunit testingPerformance tuningPython programming
Assaf Toledo
PROFILE
Assaf Toledo
Assaf Toledo developed two core features for the IBM/unitxt repository over a two-month period, focusing on both benchmarking and performance optimization. He built the REAL-MM-RAG-Bench, an end-to-end cross-modal retrieval benchmark that integrates vision-language models and LLM-driven query generation to provide actionable evaluation data for product planning. In a separate effort, he improved initialization performance by introducing lazy imports for the evaluate and SciPy modules, reducing startup latency and minimizing non-actionable warning noise. His work demonstrated depth in Python programming, data processing, and software optimization, resulting in measurable improvements to both product evaluation workflows and developer experience.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
845
Activity Months2
Your Network
864 people
Work History
January 2026
2 Commits • 1 Features
Jan 1, 2026January 2026: Delivered a performance optimization feature for IBM/unitxt by introducing lazy imports for evaluate and SciPy modules, with robust handling of warnings. This reduced startup latency and lowered non-actionable warning noise, improving developer workflows and end-user experience in initialization-heavy workloads.
2 Commits • 1 Features
Jan 1, 2026January 2026: Delivered a performance optimization feature for IBM/unitxt by introducing lazy imports for evaluate and SciPy modules, with robust handling of warnings. This reduced startup latency and lowered non-actionable warning noise, improving developer workflows and end-user experience in initialization-heavy workloads.
January 2026
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 performance summary: Key features delivered include the REAL-MM-RAG-Bench benchmark for IBM/unitxt, enabling end-to-end cross-modal retrieval evaluation. The benchmark leverages a vision-language model to generate and rephrase queries with an LLM, providing real-world evaluation insights that inform the product roadmap. Major bugs fixed: none reported this month. Overall impact and accomplishments: established a measurable, real-world cross-modal retrieval benchmark that supports data-driven product decisions, reduces risk in feature prioritization, and strengthens confidence in deployment readiness. Technologies and skills demonstrated: vision-language models integration, LLM-assisted query generation, end-to-end benchmarking workflows, and dataset governance (dataset cards).
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 performance summary: Key features delivered include the REAL-MM-RAG-Bench benchmark for IBM/unitxt, enabling end-to-end cross-modal retrieval evaluation. The benchmark leverages a vision-language model to generate and rephrase queries with an LLM, providing real-world evaluation insights that inform the product roadmap. Major bugs fixed: none reported this month. Overall impact and accomplishments: established a measurable, real-world cross-modal retrieval benchmark that supports data-driven product decisions, reduces risk in feature prioritization, and strengthens confidence in deployment readiness. Technologies and skills demonstrated: vision-language models integration, LLM-assisted query generation, end-to-end benchmarking workflows, and dataset governance (dataset cards).
Activity
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Quality Metrics
Correctness93.4%
Maintainability86.6%
Architecture86.6%
Performance93.4%
AI Usage33.4%
Skills & Technologies
Programming Languages
Python
Technical Skills
Performance tuningPython programmingSoftware optimizationdata analysisdata processingimage processingmachine learningstatistical modelingunit testing
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