license: mit base_model:

• inclusionAI/Ling-3.0-tiny base_model_relation: finetune pipeline_tag: text-generation library_name: gguf tags:
• gguf
• llama.cpp
• abliterated
• heretic
• bailingmoe3
• moe

Ling-3.0-tiny-Abliterated — GGUF

An abliterated build of inclusionAI/Ling-3.0-tiny (8B total / ~1B active MoE, hybrid KDA + MLA attention, a thinking model), produced with Heretic and quantized with llama.cpp. Other abliterated Ling-3.0 builds exist; this one is my own run, and every claim below is measured against a stock baseline built through the same pipeline.

Modification notice. These weights are a modified derivative. Directional ablation altered 1,692 tensors to reduce refusals (see What changed); nothing else was retrained. inclusionAI did not produce, endorse, or review this build. Its behavior is not theirs.

Read this first

This is an escape hatch for over-refusal, not an uncensored model. Abliteration removes a refusal direction from the residual stream — it adds no knowledge. This build knows exactly what the stock 8B knows and nothing more; what changed is its willingness to say it. If stock answers your prompt, use stock.

It is effective, and that cuts both ways. Unlike some abliterations that only dent the non-thinking path, this one drops refusals in both regimes. That makes the hazard below real, not theoretical.

The hazard worth naming: refusals double as hedging. Remove them and you get a small model answering fluently in domains where it is unreliable, with none of the cues that used to mark the boundary. Factual reliability is unchanged; only the signalling is gone. Don't trust confident output here more than you'd trust confident output from stock — especially on anything safety-critical. You are accountable for what you generate and for applicable law.

Measurements

All on Q4_K_M, greedy-ish sampling (temp 1.0 / top-p 0.95 / top-k 20, the model's own defaults), against a stock Q4_K_M built through the same pipeline. Refusals use mlabonne/harmful_behaviors test[60:] — the 44 prompts Heretic's optimiser never saw (it scored test[:60]) — classified by Heretic's marker list on the answer text.

Refusals (lower = complies more):

The compliances are substantive: of the 33/32 non-refusals, 0 were degenerate (no empty or looping output; mean ~600–2000 tokens of on-topic text). This is the opposite of what a marker metric can hide — the drop is real compliance, not scoring noise.

GSM8K, paired, n=100, thinking off, identical sampling:

Net 0. Discordant pairs split 3/3 (McNemar exact p = 1.000). Abliteration damage tends to land in math reasoning (a documented tendency for Heretic across models); at this KL, none is detectable here. n=100 excludes a large regression, not a 2–3 pp one. MMLU, long context, and non-English remain unmeasured.

Files

All quantized from one bf16 GGUF, imatrix-free (Q4_K_M and up don't require one).

Usage

llama-server -m Ling-3.0-tiny-Abliterated-Q4_K_M.gguf \
  -ngl 99 -c 32768 -fa on --jinja \
  --temp 1.0 --top-p 0.95 --top-k 20

Thinking is on by default; pass --chat-template-kwargs '{"enable_thinking":false}' to disable it. Send a large max_tokens or none — with thinking on, a small budget is consumed inside <think> and returns empty content with finish_reason: "length".

What changed

Per the merge-time delta list (computed before quantization, so no quantizer in the loop):

1,692 modules total, all in layers 8–23 (the back half); layers 0–7 untouched. This is the expected shape for refusal-direction ablation.

Reproducing

pip install -U heretic-llm
heretic inclusionAI/Ling-3.0-tiny \
  --response-prefix '</think>' \
  --system-prompt $'You are a helpful assistant.\ndetailed thinking off' \
  --max-batch-size 128 --max-response-length 64 --n-trials 100

--response-prefix '</think>' and the detailed thinking off system prompt put scoring in Ling's own non-thinking regime (byte-identical to enable_thinking=false); without them Heretic's refusal markers fire on reasoning text. Abliteration is not bit-reproducible — the TPE search lands on a different trial each run — so the selected trial 42 (6/60 refusals @ KL 0.0288, baseline 59/60, per-layer directions) is published for --reproduce:

attn.o_proj    : max_weight 1.4227  max_weight_position 18.860  min_weight 1.3238  min_weight_distance 10.697
mlp.down_proj  : max_weight 1.3516  max_weight_position 13.909  min_weight 0.6552  min_weight_distance  6.881

(attn.o_proj covers the KDA layers' o_proj and the MLA layers' dense; mlp.down_proj covers routed and shared experts.)

Note for anyone running Ling-3.0 in Transformers (not GGUF): under the default attn_implementation="sdpa", Ling's MLA layers attend non-causally on any unpadded prefill, because its code builds the 4-D causal mask only when padding is present and relies on SDPA's is_causal otherwise — which its eager path never sets. Load with attn_implementation="eager". llama.cpp (and therefore these GGUFs) is unaffected.

Verifying

941ef0a014d2c28b684a39ed77e87c4c3d4a0c107aae61949ec291402c206bd5  Ling-3.0-tiny-Abliterated-Q4_K_M.gguf
77533ffe319d50eeeec613536d604e0e1de08e85eda3942054e2f1b8378c8f51  Ling-3.0-tiny-Abliterated-Q5_K_M.gguf
cf61b3c2f1bb8fdda2e95214f2afdf4083cb3c4708a1a859c042efc5971fb2ef  Ling-3.0-tiny-Abliterated-Q6_K.gguf
091fdc479ebeabe3195508613f08bc6a200a65d0e7cc275bd9f3141cf514dd16  Ling-3.0-tiny-Abliterated-Q8_0.gguf

License

MIT, inherited from inclusionAI/Ling-3.0-tiny. The base repo declares MIT in its metadata but ships no LICENSE file; the LICENSE here is the standard MIT text with inclusionAI's own copyright line, taken verbatim from their sibling repos (Ling-V2 / Ling-V2.5) which do carry it. Abliteration and quantization change nothing about the license.

Credits

• inclusionAI — the Ling-3.0-tiny base model
• p-e-w/heretic — the abliteration method and tooling
• ggml-org/llama.cpp — conversion and quantization
• mlabonne — the prompt sets used for directions and scoring