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
Contributed to IBM/unitxt by developing the REAL-MM-RAG-Bench benchmark, enabling end-to-end evaluation of cross-modal retrieval systems using vision-language models and LLM-driven query generation. This work established a measurable workflow for benchmarking, supporting data-driven product decisions and improved dataset governance through the creation of dataset cards. Additionally, introduced performance optimizations by implementing lazy imports for the evaluate and SciPy modules, which reduced startup latency and minimized non-actionable warning noise. Leveraged Python for software optimization, data processing, and machine learning tasks, focusing on enhancing both developer experience and product reliability in initialization-heavy and real-world evaluation scenarios.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
845
Activity Months2
Your Network
1025 peopleSame Organization
@ibm.com997
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Aayush KumarMember
Abhijit DeokarMember
Abhina SreeMember
Abhishek KumarMember
Abhishek Kr SrivastavMember
Abraham Lara GranadosMember
Adapa ChathuryaMember
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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