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LatestinclusionAI3.0-flash-Fin
inclusionAIText / LLM

inclusionAI Tunes Ling-3.0-flash for Finance

A finance-focused variant of the Ling-3.0-flash MoE model targets financial research and agentic tool use.

Sep 3, 2026
UpdateOther
Ling-3.0-flash-Fin

inclusionAI has released Ling-3.0-flash-Fin, a finance-focused variant of its Ling-3.0-flash language model. The new build is aimed squarely at financial research and agentic tool use, according to the model's Hugging Face page.

Like its base model, Ling-3.0-flash-Fin uses a mixture-of-experts (MoE) architecture, which activates only a subset of the network's parameters per query. That design lets developers pursue larger effective model capacity while keeping inference costs in check — a practical consideration for domain deployments where models may be run repeatedly across documents and data feeds.

Why it matters

Finance is one of the more demanding proving grounds for language models: the work rewards precise reasoning, structured document handling, and the ability to chain tools such as calculators, retrieval systems, and data APIs. A model explicitly tuned for these tasks signals inclusionAI's interest in vertical, workflow-oriented AI rather than general chat.

Key points from the release:

  • Built on the Ling-3.0-flash family with an MoE design
  • Oriented toward financial research and agentic tool use
  • Distributed on Hugging Face under a custom ("other") license

The repository does not publish parameter counts, context length, or benchmark figures, so teams evaluating the model will want to test it against their own financial tasks. The custom license also warrants a close read before any commercial deployment.

Sources

  • inclusionAI/Ling-3.0-flash-Fin

    Hugging Face

    Visit
OlderLLaDA-Image: A Fully Open 6B Image GeneratorinclusionAI · Text → Image · 2 months agoNewerNeoMME: a single-tower multilingual multimodal encoderOpenBMB · Embeddings · 2 months ago

Get the model

Hugging Face

Specs

Context window262K tokens
Size255 GB
PrecisionBF16
ArchitectureBailingMoeV3ForCausalLM
LicenseOTHER
Downloads1.9K
Likes127

Can you run it?

Needs a workstation GPU (96 GB) at 4-bit.

  • BF16 (as published)

    4×H100 80 GB · 512 GB Mac Studio

    256.4 GB
  • 8-bit

    2×H100 80 GB · 256 GB Mac Studio

    136 GB
  • 4-bit

    80 GB GPU (A100 / H100) · 128 GB Mac Studio

    77.6 GB
Your machine
GGUF builds
  • Mixture-of-experts: every expert must be loaded, so memory follows total parameters, not the active slice.
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Text / LLM

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