qwenlm / parscale
Parallel Scaling Law for Language Model — Beyond Parameter and Inference Time Scaling
Code health
qwenlm/parscale scores 8.3 out of 10 on defect risk, which Repowise rates Excellent. Maintainability scores 8.2 out of 10. Static performance risk scores 10.0 out of 10. The three are scored separately and never blended into a single number.
Full health report- Files
- 5
- Symbols
- 69
- Modules
- 4
- Doc pages
- 12
- Dead exports
- 4
Read more about qwenlm/parscale
- OverviewFiles, symbols, languages, packages, git intelligence
- ArchitectureDependency graph, layered architecture, symbol index, and third-party dependencies
- Code healthPer-file health scores, hotspots, test coverage, dead code, and refactoring targets
- AI docsProModule-by-module documentation generated from source
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[](https://repowise.dev/repo/qwenlm/parscale)How qwenlm/parscale works
Parallel Scaling Law for Language Model — Beyond Parameter and Inference Time Scaling This page is an auto-generated, always-fresh map of the qwenlm/parscale repository, written primarily in Python. Repowise indexes the source, parses every symbol, computes a dependency graph, scores per-file code health from complexity, duplication, test coverage and churn, mines git history for hotspots and ownership, and lifts the resulting architectural decisions into a wiki you can read or query through MCP.
The codebase has 5 source files, 69 symbols, and 2 languages, organised into 4 modules, led by python, markdown. Static analysis leaves 12 open findings across the three health dimensions.
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