Distributed Continual Learning
Modular LoRA Adapter Hub & Weight Merging Engine
Fuse distributed domain expert adapters into foundation base models via TIES and DARE algorithms without catastrophic forgetting.
TIES / DARE Active5 Domain Adapters
Continual Mode
TIES / DARE
Zero catastrophic forgetting
Delta Footprint
~30-60 MB
Bandwidth-efficient P2P sync
Total Merges
5,510
On-chain consensus fusions
Verification
PoPC STARK
Mathematical delta proofs
Available Domain LoRA Adapters
Click to include or exclude adapters in the fusion recipe
Sovereign Quantitative Alpha & Orderbook LoRA
Selected48.5 MB
LoRA Delta
Target: DeAI-DeepSeek-R1-8B★ 4.98 (1,240 merges)
Hydra EVM & AST Security Auditor LoRA
Selected32.1 MB
LoRA Delta
Target: DeAI-DeepSeek-R1-8B★ 4.95 (890 merges)
Olympiad Mathematical CoT Reasoner LoRA
64.2 MB
LoRA Delta
Target: DeAI-DeepSeek-R1-8B★ 4.99 (2,310 merges)
RISC-V & NPU Hardware Synthesis LoRA
52.8 MB
LoRA Delta
Target: DeAI-LLaMA-3.3-70B★ 4.92 (650 merges)
CRISPR-Cas12 Protein Sequence LoRA
41.6 MB
LoRA Delta
Target: DeAI-DeepSeek-R1-8B★ 4.9 (420 merges)
Weight Fusion Studio (TIES / DARE)
Configure hyperparameters and execute on-chain weight merge
Weight Delta Distribution (Sparsified Top-K)Zero-Forgetting: 99.4%
-0.08
-0.04
-0.02
0.00
+0.02
+0.04
+0.08
q_proj
25% Top-K
v_proj
26% Top-K
gate_proj
22% Top-K
down_proj
23% Top-K
Task Vector Scaling: τ = 1.0 (Identity Orthogonal)TIES Fast Sign Resolv
Fusion Recipe Summary
• Base: DeAI-DeepSeek-R1-8B
• Fusing: 2 Domain Adapters
• Algorithm: TIES Merging