Instructions to use OpenTransformer/AGILLM4.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenTransformer/AGILLM4.1 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OpenTransformer/AGILLM4.1", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| library_name: transformers | |
| tags: | |
| - distributed-training | |
| - volunteer-computing | |
| - mixture-of-experts | |
| - diffusion | |
| - llm | |
| - agillm | |
| # AGILLM4.1 β community-trained open LLM | |
| AGILLM4.1 is an open ~1.1B-parameter Transformer trained **continuously across a volunteer compute network**. | |
| Architecture: **DiffusionBlocks** (4 blocks Γ 7 layers = 28 layers), **MoE** FFN (2 experts, top-1, 4Γ MLP), | |
| tied embeddings, AR/SAT/NAT heads, sub-linear attention (1024β2048 ctx). The DiffusionBlock split is what makes | |
| distributed training/inference across ordinary internet links practical β each node owns a block. | |
| ## Join the network β 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). | |
| ```bash | |
| 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 β GPUs train ~1024β2048-token context at batch 4β24 | |
| sized to their VRAM; CPUs contribute smaller blocks sized to their RAM. | |
| ## Contribution points β 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 Β· Monitor: https://monitor.opentransformers.online | |