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Jiunsong/
SuperGemma-4-12b-abliterated

Name: SuperGemma-4-12b-abliterated
Availability: InStock

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SuperGemma-4-12b-abliterated

SuperGemma-4-12b-abliterated is a fused 12B checkpoint derived from google/gemma-4-12B-it.

The release combines two post-training stages:

Abliteration pass - a weight-space refusal-direction pass designed to suppress unnecessary refusal behavior and improve direct task completion.
Supertune post-training - targeted post-training for instruction following, coding, Korean technical answers, JSON/tool formatting, and regression resistance.

The result is a single checkpoint with no runtime adapter requirement.

Benchmark Growth

The comparison target is the original Gemma4 12B instruction checkpoint.

Benchmark	Gemma4 12B original	SuperGemma-4-12b-abliterated	Delta
Overall public top-5 500	23.8	44.6	+20.8
GPQA Diamond	10.0	19.0	+9.0
MMLU-Pro	17.0	18.0	+1.0
IFEval	61.0	59.0	-2.0
HumanEval+	18.0	46.0	+28.0
MBPP+	13.0	81.0	+68.0

Additional internal validation:

Check	Result
Public benchmark prompts completed	500 / 500
Blank response ratio	0.0
Hidden-thought leak ratio	0.0
Release-surface response audit findings	0
Release bugcheck	6 / 6
Quickbench full20 overall	95.4
Mega 103 overall	88.7
Original reference Mega 103 overall	84.1

Quantized Variants

Quantized builds are published as separate Hub repos and are linked back to this model through Hub metadata.

Variant	Repository	Notes
Original BF16	Jiunsong/SuperGemma-4-12b-abliterated	This repository
NVF4 / NVFP4 4-bit	Jiunsong/SuperGemma-4-12b-abliterated-nvf4	MLX NVFP4 4-bit quantization
MLX 4-bit	Jiunsong/SuperGemma-4-12b-abliterated-mlx-4bit	MLX affine 4-bit quantization
GGUF 4-bit	Jiunsong/SuperGemma-4-12b-abliterated-gguf-4bit	llama.cpp GGUF Q4_K_M

Usage

python

from transformers import AutoModelForMultimodalLM, AutoProcessor

model_id = "Jiunsong/SuperGemma-4-12b-abliterated"
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained(
    model_id,
    dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)

messages = [{"role": "user", "content": "Write a concise release checklist."}]
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
    add_generation_prompt=True,
    enable_thinking=False,
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

Notes

This release is optimized for direct task completion and reduced unnecessary refusals.
Benchmarks are measured against the original Gemma4 12B instruction checkpoint using the recorded 500-prompt public top-5 suite.
Use the quantized builds when runtime size is more important than exact BF16 fidelity.