LA
AGI-Edgerunners/LLM-Adapters
Code for our EMNLP 2023 Paper: "LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models"
1.2k 121 -1/wk
GitHub
adapters fine-tuning large-language-models parameter-efficient
Trend
0
Star & Fork Trend (35 data points)
Stars
Forks
Multi-Source Signals
Growth Velocity
AGI-Edgerunners/LLM-Adapters has -1 stars this period . 7-day velocity: -0.1%.
Deep analysis is being generated for this repository.
Signal-backed technical analysis will be available soon.
| Metric | LLM-Adapters | tango | ToolUniverse | parallax |
|---|---|---|---|---|
| Stars | 1.2k | 1.2k | 1.2k | 1.2k |
| Forks | 121 | 107 | 191 | 122 |
| Weekly Growth | -1 | +1 | +5 | +0 |
| Language | Python | Python | Python | Python |
| Sources | 1 | 1 | 1 | 1 |
| License | Apache-2.0 | NOASSERTION | Apache-2.0 | Apache-2.0 |
Capability Radar vs tango
LLM-Adapters
tango
Maintenance Activity 0
Last code push 760 days ago.
Community Engagement 49
Fork-to-star ratio: 9.8%. 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 95
Licensed under Apache-2.0. Permissive — safe for commercial use.
Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.