huggingface/gorilla
Mar 2025 – May 2025
3 Months active
Languages Used
PythonMarkdown
Technical Skills
Backend DevelopmentCode OrganizationCode RefactoringData ManagementDocumentationFile Structure Management
Catherine Ruoxi Wu
PROFILE
Catherine Ruoxi Wu
Catherine Wu contributed to the huggingface/gorilla repository by enhancing the Berkeley Function-Call Leaderboard’s reliability and maintainability. She reorganized configuration constants and evaluation data, implemented strict model-name validation, and expanded model support to include Gemini-2.5 Pro, Grok-3, Phi-4, GPT-4.1, and Qwen 3-series models. Her work involved Python module refactoring, backend development, and robust error handling, with careful updates to documentation and configuration files. By introducing offline inference capabilities and standardizing data management, Catherine reduced technical debt and improved onboarding for new models, demonstrating depth in code organization, validation, and Machine Learning Operations (MLOps) throughout her contributions.
Overall Statistics
Feature vs Bugs
82%Features
Repository Contributions
13Total
Bugs
2
Commits
13
Features
9
Lines of code
2,806
Activity Months3
Your Network
3 peopleShared Repositories
3
Work History
May 2025
3 Commits • 1 Features
May 1, 2025In May 2025, focused on stabilizing the Berkeley Function-Call Leaderboard (BFCL) in huggingface/gorilla. Key work included implementing strict model-name validation, aligning error handling with MODEL_CONFIG_MAPPING, expanding model coverage with Qwen 3-series models, and updating docs/config to support these changes. These changes improve reliability, reduce user friction, and enable quicker onboarding for new models.
3 Commits • 1 Features
May 1, 2025In May 2025, focused on stabilizing the Berkeley Function-Call Leaderboard (BFCL) in huggingface/gorilla. Key work included implementing strict model-name validation, aligning error handling with MODEL_CONFIG_MAPPING, expanding model coverage with Qwen 3-series models, and updating docs/config to support these changes. These changes improve reliability, reduce user friction, and enable quicker onboarding for new models.
May 2025
April 2025
7 Commits • 7 Features
Apr 1, 2025April 2025 monthly summary for huggingface/gorilla: Key business outcomes include more reliable evaluation data pipelines, broader model coverage on the Berkeley Function Calling Leaderboard, offline inference capability, and reduced maintenance cost by retiring deprecated models. This work improves evaluation reliability, accelerates model iteration, and enables secure/offline deployments.
April 2025
7 Commits • 7 Features
Apr 1, 2025April 2025 monthly summary for huggingface/gorilla: Key business outcomes include more reliable evaluation data pipelines, broader model coverage on the Berkeley Function Calling Leaderboard, offline inference capability, and reduced maintenance cost by retiring deprecated models. This work improves evaluation reliability, accelerates model iteration, and enables secure/offline deployments.
March 2025
3 Commits • 1 Features
Mar 1, 2025March 2025 highlights: Reorganized and standardized configuration constants and metadata for the huggingface/gorilla repo to improve maintainability, readability, and onboarding. Implemented a dedicated constants directory and relocated model_metadata to bfcl/constants, with import updates across the codebase. Completed a targeted cleanup of the BFCL evaluation runner by relocating executable test ground-truth data to ./data/possible_answer, updating the evaluation prompt to include execution_result_type, and adjusting cleanup logic. These changes reduce technical debt, streamline testing, and enable more reliable, scalable feature development. Technologies demonstrated include Python module refactoring, repository hygiene, test data management, and prompt/data handling for evaluation tooling.
3 Commits • 1 Features
Mar 1, 2025March 2025 highlights: Reorganized and standardized configuration constants and metadata for the huggingface/gorilla repo to improve maintainability, readability, and onboarding. Implemented a dedicated constants directory and relocated model_metadata to bfcl/constants, with import updates across the codebase. Completed a targeted cleanup of the BFCL evaluation runner by relocating executable test ground-truth data to ./data/possible_answer, updating the evaluation prompt to include execution_result_type, and adjusting cleanup logic. These changes reduce technical debt, streamline testing, and enable more reliable, scalable feature development. Technologies demonstrated include Python module refactoring, repository hygiene, test data management, and prompt/data handling for evaluation tooling.
March 2025
Activity
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Quality Metrics
Correctness95.4%
Maintainability93.8%
Architecture93.0%
Performance93.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPython
Technical Skills
API IntegrationBackend DevelopmentBug FixCode MaintenanceCode OrganizationCode RefactoringCommand Line Interface (CLI)Configuration ManagementData ManagementDocumentationDocumentation ManagementError HandlingFile ManagementFile Structure ManagementLeaderboard Management
Repositories Contributed To
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