Ant Ling (@AntLingAGI)
@AntLingAGI
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Financial work depends on trustworthy sources, consistent definitions, accurate calculations and auditable outputs.
Introducing Ling-3.0-flash-Fin, a finance-enhanced version of Ling-3.0-flash, developed with financial institutions and domain experts.
With 124B total and 5.1B active parameters, it supports information retrieval, research, valuation modeling and report preparation across long reports, research materials and complex workbooks.
The model showed competitive results across FinFIRST, FinSearchComp Verified, FinCRAFT, FinanceAgent v1.1/v2, APEX-Agents, SpreadsheetBench v1/v2 and τ³-Banking.
We will open-source the model weights next week.
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According to Artificial Analysis, Ling-3.0-tiny sits on the mobile intelligence–speed Pareto frontier: 59 at 16K and 5.7s on iPhone 17 Pro. It also ranks first in the 64K intelligence evaluation with a score of 66.
Bringing stronger intelligence to smaller devices.
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Today, we’re releasing Ling-3.0-tiny: 7.9B total parameters, with only 1.3B active per token.
A native hybrid reasoning model built for real-world tasks, math, instruction following, and resource-sensitive deployment.
More intelligence with less compute. 🧵
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We recently released a paper showing that UFP4, our uniform-grid FP4 training recipe, stays closer to BF16 than strong E2M1 baselines across Dense 1.5B, MoE 7.9B, and MoE 124B long-run pretraining.
The key insight: FP4 training quality is not only about bit width, but also grid geometry.
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