---

license: gemma

base_model:

• Gryphe/Gemma-4-26B-A4B-StyleTune-V2
• google/gemma-4-26B-A4B-it

base_model_relation: quantized

pipeline_tag: text-generation

library_name: gguf

language:

• en
• pt

tags:

• gemma-4
• gguf
• llama.cpp
• mixture-of-experts
• quantized
• unofficial
• prism-pro-dq-map

---

Gemma 4 26B A4B StyleTune V2 — PRISM-PRO-DQ Map Reapplication

StyleTune V2 finetune converted to GGUF and quantized with an unofficial reapplication of the PRISM-PRO-DQ per-tensor type map.

Important provenance notice

> This is not an official PRISM-DQ or PRISM-PRO-DQ model. This repository is not affiliated with Ex0bit, PRISM-DQ, or the authors of the source finetune.

The underlying trained weights come from Gryphe/Gemma-4-26B-A4B-StyleTune-V2. No trained tensor values from Ex0bit's PRISM model were copied, merged, or transplanted into this model.

The quantized file was produced by reconstructing the per-tensor GGML quantization-type assignment visible in the public Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF release and then applying that assignment to this separate finetune. This is therefore a map reapplication, not a reproduction of the original PRISM-DQ calibration, optimization procedure, dataset, or proprietary pipeline.

The Unsloth importance matrix imatrix_unsloth.gguf_file was used during quantization. Conversion and quantization were performed with llama.cpp.

Quantization layout

For the 658 tensors shared with the reference Gemma 4 PRISM-PRO-DQ GGUF, the final distribution is:

| GGML type | Tensor count |

|---|---:|

| F32 | 392 |

| Q5_1 | 30 |

| Q5_K | 91 |

| Q6_K | 115 |

| Q8_0 | 30 |

This source checkpoint contains a separate output.weight, resulting in 659 tensors. That tensor was preserved and stored as Q5_K rather than discarded or tied during conversion.

Files and integrity

| File | Size | SHA-256 | Purpose |

|---|---:|---|---|

| Gemma-4-26B-A4B-StyleTune-V2-PRISM-DQ-I.gguf | 17.32 GiB | 4003254dd5a009e2b20e9ee55d3094d07e2fd0cf5a31572ab3511017e0fea4d5 | Unofficial PRISM-PRO-DQ tensor-map reapplication with Unsloth I-matrix |

Template and tokenizer

• The tokenizer vocabulary and special-token IDs were checked against the source Hugging Face tokenizer.
• The embedded chat template was updated to the current official template from google/gemma-4-26B-A4B-it at conversion time.

Recommended sampling

The original StyleTune V2 author describes Gemma as flexible, but reports personally using the following sampler configuration:

• temperature = 1.0
• min_p = 0.10
• DRY sampler enabled

Source: Gryphe/Gemma-4-26B-A4B-StyleTune-V2

This GGUF embeds temperature = 1.0, top_p = 0.95, and top_k = 64. It does not embed min_p = 0.10 or the DRY sampler configuration, so those two settings must be selected manually in the inference frontend or passed through the corresponding llama.cpp options.

Usage with llama.cpp

llama-cli -m Gemma-4-26B-A4B-StyleTune-V2-PRISM-DQ-I.gguf --jinja -p "Hello"

For server use:

llama-server -m Gemma-4-26B-A4B-StyleTune-V2-PRISM-DQ-I.gguf --jinja --host 127.0.0.1 --port 8080

References

1. Source finetune: Gryphe/Gemma-4-26B-A4B-StyleTune-V2
2. Official Gemma 4 base: google/gemma-4-26B-A4B-it
3. PRISM-PRO-DQ public GGUF used only as the tensor-map reference: Ex0bit/Gemma4-26B-A4B-PRISM-PRO-DQ-GGUF
4. Unsloth importance matrix: imatrix_unsloth.gguf_file
5. GGUF conversion and quantization tooling: ggml-org/llama.cpp
6. Gemma Terms of Use: https://ai.google.dev/gemma/terms

License and responsibility

Use of this repository remains subject to the original source model's license and the Gemma Terms of Use. The behavior and safety characteristics of the source finetune are inherited; quantization does not make the model safer or change its intended-use restrictions. Users are responsible for evaluating outputs and complying with applicable laws and licenses.