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Jackxuanxuan/Gemma-4-31B-JANG-Q8_4M-CRACK-GGUF overview

Gemma 4 31B JANG 4M CRACK GGUF GGUF quantizations of Gemma 4 31B JANG 4M CRACK for use with llama.cpp, LM Studio, Ollama, and other GGUF compatible inference e…

ggufgemma4quantized31btext-generationenlicense:gemmaendpoints_compatibleregion:usconversational

Runs locally from ~1.00 GB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

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Pipeline
text-generation

Repository Files & Downloads

9 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
gemma-4-31b-jang-crack-Q8_0-00001-of-00009.ggufGGUFQ8_03.66 GBDownload
gemma-4-31b-jang-crack-Q8_0-00002-of-00009.ggufGGUFQ8_03.67 GBDownload
gemma-4-31b-jang-crack-Q8_0-00003-of-00009.ggufGGUFQ8_03.72 GBDownload
gemma-4-31b-jang-crack-Q8_0-00004-of-00009.ggufGGUFQ8_03.62 GBDownload
gemma-4-31b-jang-crack-Q8_0-00005-of-00009.ggufGGUFQ8_03.70 GBDownload
gemma-4-31b-jang-crack-Q8_0-00006-of-00009.ggufGGUFQ8_03.69 GBDownload
gemma-4-31b-jang-crack-Q8_0-00007-of-00009.ggufGGUFQ8_03.62 GBDownload
gemma-4-31b-jang-crack-Q8_0-00008-of-00009.ggufGGUFQ8_03.72 GBDownload
gemma-4-31b-jang-crack-Q8_0-00009-of-00009.ggufGGUFQ8_01.00 GBDownload

Model Details

Model IDJackxuanxuan/Gemma-4-31B-JANG-Q8_4M-CRACK-GGUF
AuthorJackxuanxuan
Pipelinetext-generation
Licensegemma
Base modelgoogle/gemma-4-31b-it
Last modified2026-07-08T08:19:02.000Z

Model README

---

language:

- en

license: gemma

tags:

- gemma4

- gguf

- quantized

- 31b

base_model: google/gemma-4-31b-it

pipeline_tag: text-generation

---

Gemma-4-31B-JANG_4M-CRACK-GGUF

GGUF quantizations of Gemma-4-31B-JANG_4M-CRACK for use with llama.cpp, LM Studio, Ollama, and other GGUF-compatible inference engines.

About the Model

  • Base model: google/gemma-4-31b-it
  • Architecture: Gemma 4 Dense Transformer (31B parameters, 60 layers)
  • Features: Hybrid Sliding/Global Attention, Vision + Audio multimodal
  • Modification: CRACK abliteration (refusal removal) + JANG v2 mixed-precision quantization

Why This Conversion?

The original model uses JANG v2 mixed-precision MLX quantization (attention 8-bit + MLP 4-bit), which is only compatible with vMLX. Standard tools (llama.cpp, LM Studio, oMLX, mlx-lm) cannot load this format due to mixed per-layer bit widths.

This repository provides standard GGUF quantizations that work everywhere.

Conversion Process

Original (JANG v2 MLX safetensors, ~18GB)
    ↓ dequantize (attention 8-bit → f16, MLP 4-bit → f16)
Intermediate (float16 safetensors, ~60GB)
    ↓ convert_hf_to_gguf.py + quantize
GGUF (various quantizations)

Note: Since the original was already quantized (avg 5.1 bits), the dequantized f16 intermediate is an approximation. Re-quantizing to GGUF introduces minimal additional quality loss since the attention layers were preserved at 8-bit in the original.

Available Quantizations

| File | Quant | Size | Quality | Notes |

|------|-------|------|---------|-------|

| gemma-4-31b-jang-crack-Q3_K_M.gguf | Q3_K_M | ~14 GB | Acceptable | Minimum viable quality |

| gemma-4-31b-jang-crack-Q4_K_M.gguf | Q4_K_M | ~18 GB | Good | Best size/quality balance |

| gemma-4-31b-jang-crack-Q5_K_M.gguf | Q5_K_M | ~21 GB | Better | Recommended if RAM allows |

| gemma-4-31b-jang-crack-Q6_K.gguf | Q6_K | ~25 GB | Very Good | High quality |

| gemma-4-31b-jang-crack-Q8_0.gguf | Q8_0 | ~33 GB | Near lossless | Closest to original |

System Requirements

| Quantization | Minimum RAM | Recommended |

|-------------|------------|-------------|

| Q3_K_M | 20 GB | 24 GB |

| Q4_K_M | 24 GB | 32 GB |

| Q5_K_M | 28 GB | 36 GB |

| Q6_K | 32 GB | 40 GB |

| Q8_0 | 40 GB | 48 GB |

Usage

LM Studio

Download any .gguf file and open it in LM Studio.

llama.cpp

./llama-cli -m gemma-4-31b-jang-crack-Q4_K_M.gguf -p "Hello" -n 256

Ollama

echo 'FROM ./gemma-4-31b-jang-crack-Q4_K_M.gguf' > Modelfile
ollama create gemma4-crack -f Modelfile
ollama run gemma4-crack

License

Gemma License

Disclaimer

This model has had safety guardrails removed. Use responsibly and in compliance with applicable laws.

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