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@@ -29,6 +29,6 @@ The B, C, and D classes are derived from the tokens per model ratio from LLaMA,
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  | GerbilLab/Gerbil-A-15m | 15m | A-Class | 20 | 280M | 131k | 4.9999 |
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- | GerbilLab/Gerbil-A-32m | 32m | A-Class | 20 | 640M | 262K | coming soon |
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  The only application where I can imagine these being useful in the slightest is warm-starting very small encoder-decoder models or fitting a new scaling law that takes into account smaller models. Every model was trained on a singular GPU, either a RTX2060, RTX3060, or a T4.
 
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  | --- | --- | --- | --- | --- | --- | --- |
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  | GerbilLab/Gerbil-A-15m | 15m | A-Class | 20 | 280M | 131k | 4.9999 |
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  | --- | --- | --- | --- | --- | --- | --- |
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+ | GerbilLab/Gerbil-A-32m | 32m | A-Class | 20 | 640M | 262K | 4.048700 |
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  The only application where I can imagine these being useful in the slightest is warm-starting very small encoder-decoder models or fitting a new scaling law that takes into account smaller models. Every model was trained on a singular GPU, either a RTX2060, RTX3060, or a T4.