saurabhzx / lora-slm-training
This is not a production-ready solution, but a comprehensive exploration of the challenges, solutions, and iterative improvements in fine-tuning LLMs on consumer hardware with AI assistance.
Code health
saurabhzx/lora-slm-training scores 9.6 out of 10 on defect risk, which Repowise rates Excellent. Maintainability scores 9.2 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
- 10
- Symbols
- 160
- Modules
- 10
- Doc pages
- 0
- Dead exports
- 0
Read more about saurabhzx/lora-slm-training
- 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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Average health across every file, straight from the latest index.
[](https://repowise.dev/repo/saurabhzx/lora-slm-training)How saurabhzx/lora-slm-training works
This is not a production-ready solution, but a comprehensive exploration of the challenges, solutions, and iterative improvements in fine-tuning LLMs on consumer hardware with AI assistance. This page is an auto-generated, always-fresh map of the saurabhzx/lora-slm-training 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 10 source files, 160 symbols, and 2 languages, organised into 10 modules, led by python, markdown. Static analysis leaves 20 open findings across the three health dimensions.
Use the links above to open the interactive dashboards, or connect this repo to your editor via the Repowise MCP server for grounded answers inside Claude, Cursor, or VS Code.