togethercomputer/openapi
Mar 2026 – Apr 2026
2 Months active
Languages Used
YAML
Technical Skills
API DevelopmentAPI DocumentationData TypesDocumentationOpenAPI Specification
jli
PROFILE
Jli
Jli contributed to the togethercomputer/openapi repository by developing two targeted API enhancements over a two-month period. They introduced a nullable random_seed parameter to the training API, aligning data types and defaults in YAML and updating documentation to improve experiment reproducibility for data scientists. In the following month, Jli implemented a num_workers parameter, enabling users to tune inference throughput by controlling concurrent workers, and provided comprehensive OpenAPI documentation updates with usage examples. Their work focused on feature delivery and documentation clarity, leveraging skills in API development and OpenAPI Specification, and laid a foundation for improved scalability and reproducibility without addressing major bug fixes.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
2
Lines of code
47
Activity Months2
Your Network
53 peopleSame Organization
@together.ai20
Shared Repositories
33
Work History
April 2026
2 Commits • 1 Features
Apr 1, 2026April 2026 — OpenAPI throughput enhancement for togethercomputer/openapi: added a new num_workers parameter to tune the number of concurrent inference workers, enabling customers to balance throughput and latency when integrating with external APIs. Updated API docs with parameter details and example values. Changes tracked in two commits focused on implementation and documentation. No major bugs fixed this month in this repo; the changes lay groundwork for improved scalability and adoption.
2 Commits • 1 Features
Apr 1, 2026April 2026 — OpenAPI throughput enhancement for togethercomputer/openapi: added a new num_workers parameter to tune the number of concurrent inference workers, enabling customers to balance throughput and latency when integrating with external APIs. Updated API docs with parameter details and example values. Changes tracked in two commits focused on implementation and documentation. No major bugs fixed this month in this repo; the changes lay groundwork for improved scalability and adoption.
April 2026
March 2026
5 Commits • 1 Features
Mar 1, 2026March 2026: Delivered a reproducibility enhancement for the training API by introducing a nullable random_seed parameter, aligned data types and defaults, and updated documentation. The change improves experiment reproducibility and reduces variance in training runs, while providing clear guidance on seed usage. Focus this month was feature delivery and documentation improvements; no major bugs fixed were reported. Enhanced API usability, clearer guidance for users, and better onboarding for data scientists.
March 2026
5 Commits • 1 Features
Mar 1, 2026March 2026: Delivered a reproducibility enhancement for the training API by introducing a nullable random_seed parameter, aligned data types and defaults, and updated documentation. The change improves experiment reproducibility and reduces variance in training runs, while providing clear guidance on seed usage. Focus this month was feature delivery and documentation improvements; no major bugs fixed were reported. Enhanced API usability, clearer guidance for users, and better onboarding for data scientists.
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
YAML
Technical Skills
API DevelopmentAPI DocumentationData TypesDocumentationOpenAPI Specification
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline