AbdelrhmanEbied/Tiny-MoE

Tiny-MoE is a lightweight Mixture-of-Experts language model built entirely from scratch in native PyTorch and trained end-to-end on Kaggle using free 2× NVIDIA T4 GPUs. The project implements modern LLM techniques—including MLA, RoPE, YaRN, streaming pre-training, and efficient inference—without relying on existing model implementations.

18
Stars
1
Forks
22.8
Radar Score
Stable
Momentum

Scoring Breakdown

Impact11.3
Velocity20.1
Health44.7

Growth Metrics

7-Day Stars
+0
30-Day Stars
+0
Acceleration
x
Language
Python

Why it ranks

Modest but positive momentum.

LLM Frameworks · inference engineTop 95% globally

🛡️ Enterprise Readiness & Commercial Safety

Organic Growth
95%
High Confidence
Growth pattern looks organic.
License
Permissive (Commercial Friendly)
Safe for commercial use, modification, and distribution.
Health Grade
B
Based on maintenance, issue response, and release cadence.
Radar Score
22.8
Composite of Impact, Velocity, and Health.
Disclaimer: This is an automated heuristic assessment based on public GitHub signals. Always perform your own security audit, dependency review, and legal compliance check before production deployment.

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