stanford-crfm/helm
Jul 2025 – Jul 2025
1 Month active
Languages Used
C++PythonShell
Technical Skills
C++ DevelopmentCode AnalysisData EngineeringLLM EvaluationMachine LearningPython Development
Kazf28
PROFILE
Kazf28
Developed an LLM Code Replication Evaluation Framework for the stanford-crfm/helm repository, focusing on benchmarking large language models in replicating undergraduate student code. The work introduced new evaluation scenarios and metrics to assess code correctness, efficiency, and stylistic mimicry, addressing the need for robust model comparison. Leveraging Python and C++, the framework incorporated configuration files and automation scripts to streamline experiment setup and execution. This approach enabled configuration-driven, automated evaluations, supporting faster iteration for research teams. The contribution provided a foundation for more systematic LLM benchmarking, emphasizing code analysis and data engineering to facilitate reproducible and scalable evaluation of code-generation models.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
2,217
Activity Months1
Your Network
136 peopleShared Repositories
23
Work History
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for stanford-crfm/helm focused on the LLM Code Replication Evaluation Framework development. Highlights include new evaluation scenarios and metrics for evaluating LLMs in replicating undergraduate student code, along with configuration assets and automation scripts. This work delivers clear business value by enabling more robust benchmarking of code-generation models and supporting faster iteration across teams.
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for stanford-crfm/helm focused on the LLM Code Replication Evaluation Framework development. Highlights include new evaluation scenarios and metrics for evaluating LLMs in replicating undergraduate student code, along with configuration assets and automation scripts. This work delivers clear business value by enabling more robust benchmarking of code-generation models and supporting faster iteration across teams.
July 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
C++PythonShell
Technical Skills
C++ DevelopmentCode AnalysisData EngineeringLLM EvaluationMachine LearningPython DevelopmentSoftware Engineering
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