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Cataloged from SC117/Ling-3.0-tiny-abliterated-APEX-GGUF
All models by SC117 βEnglish | π δΈζζζ‘£
Abliterated APEX GGUF quants of inclusionAI/Ling-3.0-tiny β 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 β and even beats F16.
APEX classifies every tensor by its role β routed expert, shared expert, or attention β 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.
All tiers quantized from a single BF16 source (15.07 GB) with the same diverse imatrix. Expert tensor layout: edge layers (L0β4, L19β23) keep higher precision than middle layers (L10β13); shared experts stay at Q8_0; the router is never quantized.
All tiers use a diverse calibration dataset spanning chat, code, reasoning, and tool-calling β no Wikipedia (500 chunks). This produces higher accuracy on real-world benchmarks, lower KL divergence, and only a tiny perplexity increase on wikitext.
llama.cpp
hf download SC117/Ling-3.0-tiny-abliterated-APEX-GGUF --include "*.gguf" --local-dir ./models ./llama-server -m ./models/Ling-3.0-tiny-abliterated-APEX-I-Balanced.gguf -ngl 99 -c 32768 --flash-attn on --jinja
β οΈ bailingmoe3 architecture support: BailingMoE3 (PR #26608) was merged into llama.cpp master on 2026-08-17 β the first release containing it is b10470. Use llama.cpp b10470 or newer. If you see unknown model architecture: 'bailingmoe3', your build is too old β update and it will load.
Ollama
echo 'FROM ./Ling-3.0-tiny-abliterated-APEX-I-Balanced.gguf' > Modelfile ollama create ling-tiny-abliterated -f Modelfile && ollama run ling-tiny-abliterated
@misc{ling3tiny,title = {{Ling-3.0-tiny}: A Lightweight Hybrid Reasoning MoE Model},url = {https://huggingface.co/inclusionAI/Ling-3.0-tiny},author = {{inclusionAI}},year = {2026}}