Dashboard / Repos / Loco Llm

Loco Llm

B+ 86 completed
Ai Ml
cli / markdown · tiny
16
Files
1,432
LOC
0
Frameworks
4
Languages
OverviewFiles & MetricsGit ActivityCall GraphSecurityReports

Pipeline State

completed
Run ID
#360360
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
43.11
Framework unique
—
Isolation
—
Last stage change
2026-05-10 03:34:51
Deduplication group #50778
Member of a group with 6 similar repo(s) — canonical #68846 view group →
Top concepts (2)
Project DescriptionDocumentation
Provenance: Repobility (https://repobility.com) — every score reproducible from /scan/

AI Prompt

Create a command-line interface (CLI) tool based on the LocoLLM framework. I need it to allow users to run local, collaborative large language models on consumer hardware. The core functionality should involve a router that classifies a user's query and directs it to the best-suited specialist adapter. This system should combine a single quantized base model with multiple lightweight LoRA adapters for specific tasks like Math, Code Generation, Writing Assistance, Domain QA, and Reasoning. The CLI should handle the entire inference process, including any necessary enhancements like RE2 or voting mechanisms, all while keeping the entire system local.
cli llm local ai python markdown adapter router quantization
Generated by gemma4:latest

Catalog Information

This project enables local collaborative large language models, making frontier AI accessible to students on a budget.

Description

Locollm is an initiative that allows for the creation and utilization of local collaborative large language models. This approach makes cutting-edge AI technology more accessible to students who may not have the resources to deploy it in the cloud. By leveraging local computing power, locollm aims to democratize access to advanced AI capabilities.

الوصف

هذا المشروع يسمح بإنشاء وتشغيل لغة اللغة الكبيرة المحلية التعاونية، مما يجعل التكنولوجيا المتقدمة للذكاء الاصطناعي متاحة للمرء على عتبة الطالب. من خلال الاستفادة من القدرات الحاسوبية المحلية، يهدف مشروع locollm إلى توزيع الوصول إلى قدرات الذكاء الاصطناعي المتقدمة.

Novelty

7/10

Tags

collaborative-learning large-language-models local-computation democratized-ai

Claude Models

claude-opus-4.6

Quality Score

B+
86.2/100
Structure
89
Code Quality
100
Documentation
85
Testing
70
Practices
68
Security
100
Dependencies
60

Strengths

  • Good test coverage (50% test-to-source ratio)
  • Code linting configured (ruff (possible))
  • Consistent naming conventions (snake_case)
  • Low average code complexity \u2014 well-structured code
  • Good security practices \u2014 no major issues detected
  • Properly licensed project

Weaknesses

  • No CI/CD configuration \u2014 manual testing and deployment

Recommendations

  • Set up CI/CD (GitHub Actions recommended) to automate testing and deployment

Security & Health

4.1h
Tech Debt (D)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (7)
Open data scored by Repobility · https://repobility.com
MIT
License
0.0%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

markdown
96.2%
toml
2.2%
python
1.1%
yaml
0.5%

Frameworks

None detected

Concepts (2)

Open methodology · Repobility · https://repobility.com/research/
CategoryNameDescriptionConfidence
About: code-quality intelligence by Repobility · https://repobility.com
auto_descriptionProject Description> "Crazy enough to work."80%
auto_categoryDocumentationdocs70%

Quality Timeline

1 quality score recorded.

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