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@@ -44,7 +44,7 @@ Pretrained on a mix of open English text plus synthetic distillation data:
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  - **Math/reasoning:** Orca-Math
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  - **Synthetic fact distillation:** Q&A and short-fact data generated by a stronger teacher model (Ling 2.6 Flash) to densify factual coverage.
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- > Total training tokens are on the order of a few billion — **far fewer** than models like SmolLM/Pythia (hundreds of billions). This is a hobbyist/research model; capability scales with tokens, and this one is intentionally token-limited. *(Update this section with your exact token count.)*
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  ## Intended use
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  - **Math/reasoning:** Orca-Math
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  - **Synthetic fact distillation:** Q&A and short-fact data generated by a stronger teacher model (Ling 2.6 Flash) to densify factual coverage.
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+ > Total training tokens are on the order of a few billion — **far fewer** than models like SmolLM/Pythia (hundreds of billions). This is a hobbyist/research model; capability scales with tokens, and this one is intentionally token-limited. *(4.7B)*
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  ## Intended use
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