MODEL PROFILE

Ling-3.0-flash-GGUF

AtomicChat · Ling-3.0-flash-GGUF

Ling 3.0 flash is a 124B MoE with 5.1B active parameters; its GGUF chat template includes thinking mode and tool calling. Our AD layouts have 31–41% lower mean KL than stock llama.cpp presets at comparable sizes. The raw measurements and controls are public. From the team behind Atomic Chat , the local AI app for chat and agents. These Ling files require our TurboQuant build; use the CLI instructions below . The tests documented below cover the CLI workflow, not app compatibility. Quantizations of `inclusionAI/Ling-3.0-flash`: 124B total, 5.1B active, hybrid linear attention (35 KDA blocks interleaved 5:1 with 7 gated MLA blocks) over a 512-expert MoE. Bits are placed by hand rather than by the default rules, and the controls that prove it is worth something are published next to the files. At the same size, our layout sits 31 to 41 % closer to BF16 than what `llama-quantize` produces on its own

Text generationlicense:mit
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/11/2 19:16:11
OVERVIEW

Model overview

Ling 3.0 flash is a 124B MoE with 5.1B active parameters; its GGUF chat template includes thinking mode and tool calling. Our AD layouts have 31–41% lower mean KL than stock llama.cpp presets at comparable sizes. The raw measurements and controls are public. From the team behind Atomic Chat , the local AI app for chat and agents. These Ling files require our TurboQuant build; use the CLI instructions below . The tests documented below cover the CLI workflow, not app compatibility. Quantizations of `inclusionAI/Ling-3.0-flash`: 124B total, 5.1B active, hybrid linear attention (35 KDA blocks interleaved 5:1 with 7 gated MLA blocks) over a 512-expert MoE. Bits are placed by hand rather than by the default rules, and the controls that prove it is worth something are published next to the files. At the same size, our layout sits 31 to 41 % closer to BF16 than what `llama-quantize` produces on its own

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

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Estimate monthly base token costs

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

Do open weights permit free commercial use?

Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.

Are these real-time billing prices?

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Sources & traceability

许可证:mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。