DO
NVlabs/DoRA
[ICML2024 (Oral)] Official PyTorch implementation of DoRA: Weight-Decomposed Low-Rank Adaptation
956 63 +0/wk
GitHub
commonsense-reasoning deep-learning deep-neural-networks instruction-tuning large-language-models large-vision-language-models lora parameter-efficient-fine-tuning parameter-efficient-tuning vision-and-language
Trend
3
Star & Fork Trend (18 data points)
Stars
Forks
Multi-Source Signals
Growth Velocity
NVlabs/DoRA has +0 stars this period . 7-day velocity: 0.1%.
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Signal-backed technical analysis will be available soon.
| Metric | DoRA | papers-I-read | Efficient-Deep-Learning | bolt |
|---|---|---|---|---|
| Stars | 956 | 956 | 955 | 957 |
| Forks | 63 | 80 | 132 | 164 |
| Weekly Growth | +0 | +0 | +0 | +0 |
| Language | Python | HTML | N/A | C++ |
| Sources | 1 | 1 | 1 | 1 |
| License | NOASSERTION | N/A | MIT | MIT |
Capability Radar vs papers-I-read
DoRA
papers-I-read
Maintenance Activity 95
Last code push 16 days ago.
Community Engagement 33
Fork-to-star ratio: 6.6%. Lower fork ratio may indicate passive usage.
Issue Burden 70
Issue data not yet available.
Growth Momentum 30
No measurable growth in the current period (first-day cold start expected).
License Clarity 30
No clear license detected — proceed with caution.
Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.