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README.md

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+---
+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).
+
+| 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%.
+
+`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.
+
+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.
+