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Jiunsong/SuperGemma-4-12b-abliterated-gguf-4bit overview

SuperGemma 4 12b abliterated GGUF 4 bit This repository contains a llama.cpp GGUF Q4 K M quantization of the BF16 parent model. Parent model: Jiunsong/SuperGem…

ggufgemma4abliteratedq4_k_m4-bitquantizedllama.cpptext-generationenkobase_model:Jiunsong/SuperGemma-4-12b-abliteratedbase_model:quantized:Jiunsong/SuperGemma-4-12b-abliteratedlicense:apache-2.0endpoints_compatibleregion:usconversational

Runs locally from ~6.87 GB disk (8 GB VRAM class GPUs with llama.cpp / guIDE).

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Pipeline
text-generation
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Repository Files & Downloads

1 GGUF files detected
Direct downloads for local inference
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SuperGemma-4-12b-abliterated-Q4_K_M.ggufGGUFQ4_K_M6.87 GBDownload

Model Details

Model IDJiunsong/SuperGemma-4-12b-abliterated-gguf-4bit
AuthorJiunsong
Pipelinetext-generation
Licenseapache-2.0
Base modelJiunsong/SuperGemma-4-12b-abliterated
Last modified2026-06-25T09:36:22.000Z

Model README

---

license: apache-2.0

base_model: Jiunsong/SuperGemma-4-12b-abliterated

base_model_relation: quantized

library_name: gguf

pipeline_tag: text-generation

tags:

  • gemma4
  • abliterated
  • gguf
  • q4_k_m
  • 4-bit
  • quantized
  • llama.cpp

language:

  • en
  • ko

---

SuperGemma-4-12b-abliterated - GGUF 4-bit

This repository contains a llama.cpp GGUF Q4_K_M quantization of the BF16 parent model.

Parent model: Jiunsong/SuperGemma-4-12b-abliterated

Parent Benchmark Summary

| 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 |

Usage

llama-cli \
  -m SuperGemma-4-12b-abliterated-Q4_K_M.gguf \
  --single-turn \
  --reasoning off \
  --no-display-prompt \
  -p "Write a short release note."

Notes

  • This quantized build is provided for smaller local inference deployments.
  • For llama.cpp CLI smoke tests, use --single-turn --reasoning off for clean final-answer output.
  • For benchmark numbers and validation details, see the BF16 parent repository.

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