MODEL PROFILE

Ling-3.0-flash-abliterated-APEX-GGUF

SC117 · Ling-3.0-flash-abliterated-APEX-GGUF

Ling-3.0-flash-abliterated-APEX-GGUF English | 📖 中文文档 inclusionAI Ling-3.0-flash · 124B-A5.1B hybrid MoE · abliterix v6 trial 21 · APEX GGUF Ling-3.0-flash is inclusionAI's next-generation native hybrid reasoning model: 124B total / 5.1B active (~12.4% of their previous 1T-class flagship). It uses a native hybrid-linear stack from pretraining — 5:1 Kimi Delta Attention (KDA) + gated MLA , 1/64 sparse MoE, 512 routed experts (top-8) + 1 shared expert, 2 dense layers, and a trained MTP head. Official context schedule is 8K → 32K → 256K. Thinking is on by default in the official card. This community package is not an official inclusionAI release . It applies two extra steps on top of the official weights: Refusal edit (mergeable recipe) via abliterix v6 trial 21 LoRA, stream-merged back to BF16. Write path: MPOA weight_normalization=full , LoRA rank 3, o_proj + down_proj only. This is the static merge, not the later runtime-only gate (v36). GGUF : BF16 convert + APEX mixed-precision tensor-type files (no imatrix on these tiers). Architecture in the GGUF is bailingmoe3 .

Text generationreasoninglicense:mit
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/10/31 05:06:21
OVERVIEW

Model overview

Ling-3.0-flash-abliterated-APEX-GGUF English | 📖 中文文档 inclusionAI Ling-3.0-flash · 124B-A5.1B hybrid MoE · abliterix v6 trial 21 · APEX GGUF Ling-3.0-flash is inclusionAI's next-generation native hybrid reasoning model: 124B total / 5.1B active (~12.4% of their previous 1T-class flagship). It uses a native hybrid-linear stack from pretraining — 5:1 Kimi Delta Attention (KDA) + gated MLA , 1/64 sparse MoE, 512 routed experts (top-8) + 1 shared expert, 2 dense layers, and a trained MTP head. Official context schedule is 8K → 32K → 256K. Thinking is on by default in the official card. This community package is not an official inclusionAI release . It applies two extra steps on top of the official weights: Refusal edit (mergeable recipe) via abliterix v6 trial 21 LoRA, stream-merged back to BF16. Write path: MPOA weight_normalization=full , LoRA rank 3, o_proj + down_proj only. This is the static merge, not the later runtime-only gate (v36). GGUF : BF16 convert + APEX mixed-precision tensor-type files (no imatrix on these tiers). Architecture in the GGUF is bailingmoe3 .

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

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

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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?

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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。