Jore
C 68 completed
Ai Ml
unknown / text · small
94
Files
18,712
LOC
1
Frameworks
9
Languages
Pipeline State
completedRun ID
#366470Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
CatalogedDecision
proceedNovelty
61.33Framework unique
—Isolation
—Last stage change
2026-05-10 03:35:17Deduplication group #52529
Member of a group with 5 similar repo(s) — canonical #90648 view group →
Top concepts (2)
Project DescriptionWeb Backend
About: code-quality intelligence by Repobility · https://repobility.com
AI Prompt
Create a small, self-contained application based on the Jore nano transformer LLM. I need a Flask web UI that allows users to interact with the model for natural language processing tasks. The UI should support both a chat interface and a quiz functionality. Since the core inference engine is in C99, please ensure the Flask backend can manage the model loading and run inference, perhaps using the provided `inference/` directory structure. Also, include the necessary setup for training, referencing the PyTorch training loop and the cron automation for periodic evaluation.
python flask llm pytorch nlp c99 web-ui transformer text-generation
Generated by gemma4:latest
Catalog Information
jore is a nano transformer LLM built from scratch, designed for natural language processing tasks.
Description
jore is an open-source, PyTorch-based transformer model that can be trained and used for various NLP tasks. It features a C99 inference engine, a Flask web UI, and supports char-level tokenization. The project includes a training loop, checkpoint management, and overnight automation using cron.
الوصف
يور هو نموذج معالجة اللغة الطبيعية من الدرجة النانوية، مصمم لتحليل اللغة الطبيعية. يعتمد على بيرش ويتش (PyTorch) ويعمل على تشغيل المعالجات المحلية (C99). يحتوي على واجهة ويب باستخدام فلاسك (Flask)، وتدعم التكامل بالحروف. يتضمن المشروع حلقة تدريب، إدارة نقاط التحقق، وتسريع الليل باستخدام الكرون.
Novelty
7/10Tags
natural-language-processing transformer-model char-level-tokenization overnight-automation cron flask-web-ui
Technologies
flask matplotlib numpy pytorch
Claude Models
claude-sonnet-4.5 claude-opus-4.6
Quality Score
C
68.5/100
Structure
76
Code Quality
74
Documentation
58
Testing
75
Practices
53
Security
70
Dependencies
60
Strengths
- CI/CD pipeline configured (github_actions)
- Good test coverage (48% test-to-source ratio)
- Consistent naming conventions (snake_case)
- Properly licensed project
Weaknesses
- 243 duplicate lines detected \u2014 consider DRY refactoring
Recommendations
- Add a linter configuration to enforce code style consistency
Security & Health
6.1h
Tech Debt (A)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (1)
Repobility · MCP-ready · https://repobility.com
Apache-2.0
License
19.6%
Duplication
Languages
Frameworks
Flask
Concepts (2)
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| auto_description | Project Description | Nano transformer LLM built from scratch. PyTorch training loop, C99 inference engine, overnight automation. | 80% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_category | Web Backend | web-backend | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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