| --- |
| license: mit |
| library_name: transformers |
| tags: |
| - arc-agi |
| - test-time-training |
| - muon |
| - causal-lm |
| --- |
| |
| # ARC Tiny Transformer checkpoints |
|
|
| Hugging Face checkpoints for [N8python/arc-tiny-transformer](https://github.com/N8python/arc-tiny-transformer). |
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| | Folder | Parameters | Optimized tokens | Public evaluation, identity greedy | |
| |---|---:|---:|---:| |
| | `7m-3.4b` | 7,094,784 | 3,399,843,840 | 4.375% | |
| | `50m-100m` | 50,372,096 | 100,073,472 | 0.750% | |
| | `50m-500m` | 50,372,096 | 500,072,448 | 2.625% | |
| | `50m-1.13b` | 50,372,096 | 1,133,150,208 | 5.125% | |
| | `50m-3.0b` | 50,372,096 | 2,999,844,864 | 9.000% | |
| | `50m-3.4b` | 50,372,096 | 3,399,843,840 | 8.500% | |
| | `440m-0.8b` | 440,506,368 | 799,801,344 | 9.250% | |
|
|
| The headline test-time-training experiments use `50m-3.0b`. One full-model TTT replica plus 128 greedy augmented candidates reaches 47.75% top-2 task-macro accuracy on the 400-task ARC-AGI-1 public evaluation; pooling three independently adapted replicas reaches 51.50%. |
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| `verifier-50m-epoch4` is the auxiliary binary classifier trained on correct, perturbed, and on-policy sequences. It is included for reproduction but did not improve the headline vote aggregation. |
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| All causal-LM folders are standard Transformers/Qwen3-format checkpoints with the custom 19-token tokenizer. See the GitHub repository for exact tokenizer semantics, training code, model hashes, and evaluation commands. |
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