AGILLM4.1 — community-trained open LLM

AGILLM4.1 is an open ~1.1B-parameter Transformer trained continuously across a volunteer compute network. Architecture: DiffusionBlocks (4 blocks × 7 layers = 28 layers), MoE FFN (2 experts, top-1, 4× MLP), tied embeddings, AR/SAT/NAT heads, sub-linear attention (1024–2048 ctx). The DiffusionBlock split is what makes distributed training/inference across ordinary internet links practical — each node owns a block.

Join the network — contribute CPU or GPU

The worker is outbound-HTTPS only and sandboxed: it pulls a layer-block lease, trains it locally, and submits the result to a quarantine pool that is validated server-side before it can touch the live checkpoint. No account, no SSH, no access to anyone else's machine. Lease size auto-adapts to your hardware (VRAM/RAM).

git clone https://github.com/Marxist-Leninist/AGILLM4.1.git
cd AGILLM4.1
python -m venv .venv && . .venv/bin/activate
python -m pip install --upgrade pip torch     # CUDA build for GPU
python public_join/agillm41_join_worker.py \
  --coordinator-url https://join.opentransformers.online --loop
#   --device auto   (default: detects CUDA / DirectML / CPU)
#   add --device cuda to force GPU

A single GPU contributor outweighs dozens of CPU ones — GPUs train ~1024–2048-token context at batch 4–24 sized to their VRAM; CPUs contribute smaller blocks sized to their RAM.

Contribution points → distributed inference

Validated contributions earn points, redeemable for distributed inference of the latest model:

• Your balance: https://join.opentransformers.online/api/v1/points/<your-participant-id>
• Leaderboard: https://join.opentransformers.online/api/v1/leaderboard
• Live network monitor (nodes / stages / economy): https://monitor.opentransformers.online

Points are credited only after server-side validation of your submitted update (finite, norm-bounded, structurally sane); junk earns nothing and can never execute on the coordinator.

Links

• Code + worker: https://github.com/Marxist-Leninist/AGILLM4.1
• Coordinator: https://join.opentransformers.online · Monitor: https://monitor.opentransformers.online