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Note: This model demonstrates a scaling limitation in Ouroboros weight sharing. Despite 4x more parameters than the 279M, it converges to nearly identical val_loss (3.180 vs 3.188). At dim=2048 with head_dim=64, the representation is wide enough for a single pass — shared loops become an echo chamber rather than iterative refinement.For inference and testing, use Auron-510M (val_loss 3.035).
| Model | Params | Final Val Loss | Scaling |
|---|---|---|---|
| Auron-279M | 279M | 3.188 | Baseline |
| Auron-510M | 510M | 3.035 | -0.153 (good) |
| Auron-1.1B | 1.1B | 3.180 | +0.145 (regression) |
1from ouro import load_model, generate
2model, tokenizer, device = load_model("nyxia/Auron-510M") # Use 510M
3generate(model, tokenizer, device, "The history of")