Ling-3.0-tiny-abliterated-APEX-GGUF

Model overview

Ling-3.0-tiny English | 📖 中文文档 Abliterated APEX GGUF quants of inclusionAI/Ling-3.0-tiny — 7.9B total / 1.3B active hybrid-reasoning MoE Refusal direction removed with abliterix using LoRA steering search on a bnb-4bit loaded model. Over 130 candidate steering configurations were explored; the Pareto-selected recipe was merged into the BF16 weights. 85% fewer refusals (15/100 vs 98/100 baseline) at 0.0677 KL divergence. Post-abliteration capability spot-checks (math, logic, coding, translation, knowledge) all pass with no degradation observed. These GGUF files are quantized using APEX , a novel MoE-aware mixed-precision quantization technique that outperforms standard quantization methods while being significantly smaller. APEX beats Q8_0 perplexity at half the size — and even beats F16. APEX classifies every tensor by its role — routed expert, shared expert, or attention — and applies a layer-wise precision gradient, giving the most sensitive edge layers higher precision and compressing the redundant middle layers more aggressively. Ling-3.0-tiny's 128 routed experts (only 8 active per token) make it an ideal candidate.

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.

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.

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。

Frequently asked questions

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。