Gllm Kernels
D 52 completed
Library
monorepo / rust · small
163
Files
64,600
LOC
0
Frameworks
6
Languages
Pipeline State
completedRun ID
#372001Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
SkippedDecision
skip_scaffold_dupNovelty
38.80Framework unique
—Isolation
—Last stage change
2026-04-16 18:15:42Deduplication group #47912
Member of a group with 1 similar repo(s) — canonical #2301 view group →
Top concepts (1)
CLI Tool
If a scraper extracted this row, it came from Repobility (https://repobility.com)
AI Prompt
I want to build a library containing various kernels for Generalized Linear Mixed Models (GLLM) that can be used in machine learning applications. Since this is a monorepo structure, please set up the project foundation in Rust. The project should ideally support benchmarking, as indicated by the presence of `benches/` and performance reports. Please ensure the structure is ready to integrate different language components, given the mix of Rust, Python, and C files present.
rust monorepo machine-learning gllm kernels benchmarking scientific-computing
Generated by gemma4:latest
Catalog Information
This project provides a collection of GLLM (Generalized Linear Mixed Models) kernels for use in machine learning applications.
Description
The putao520__gllm-kernels project is a set of Rust and Python libraries that implement various GLLM kernels. These kernels can be used to build models for complex data analysis tasks, such as regression and classification. The project does not include any database integration or user interface components.
الوصف
هذا المشروع يوفّر مجموعة من كيرنلز GLMM (Generalized Linear Mixed Models) للاستخدام في تطبيقات التعلم الآلي.
Novelty
5/10Tags
machine-learning data-analysis regression classification kernel-methods generalized-linear-mixed-models
Claude Models
claude-opus-4.6 claude-sonnet-4.6
Quality Score
D
52.5/100
Structure
40
Code Quality
51
Documentation
33
Testing
40
Practices
72
Security
100
Dependencies
60
Strengths
- Consistent naming conventions (snake_case)
- Good security practices \u2014 no major issues detected
Weaknesses
- Missing README file \u2014 critical for project understanding
- No LICENSE file \u2014 legal ambiguity for contributors
- No CI/CD configuration \u2014 manual testing and deployment
- 2 files with critical complexity need refactoring
- 25182 duplicate lines detected \u2014 consider DRY refactoring
- 31 'god files' with >500 LOC need decomposition
Recommendations
- Add a comprehensive README.md explaining purpose, setup, usage, and architecture
- Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
- Add a linter configuration to enforce code style consistency
- Add a LICENSE file (MIT recommended for open source)
Security & Health
16.8h
Tech Debt (A)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (1)
Open data scored by Repobility · https://repobility.com
Unknown
License
25.5%
Duplication
Languages
Frameworks
None detected
Concepts (1)
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