AtomicChat/gemma-4-26B-A4B-it-assistant-GGUF overview

---

license: apache-2.0

license_link: https://ai.google.dev/gemma/docs/gemma_4_license

thumbnail: https://huggingface.co/AtomicChat/gemma-4-26B-A4B-it-assistant-GGUF/resolve/main/hero.png

base_model:

• google/gemma-4-26B-A4B-it-assistant

base_model_relation: quantized

quantized_by: AtomicChat

pipeline_tag: text-generation

library_name: gguf

tags:

• atomic-chat
• gemma
• gemma4
• google
• gguf
• llama.cpp
• quantized

---

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<a href="https://atomic.chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/gemma-4-26B-A4B-it-assistant-GGUF/resolve/main/pill_atomic_v3.png" alt="Atomic Chat" style="width:100%; height:auto; max-width:186px;"></a>

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<img src="https://huggingface.co/AtomicChat/gemma-4-26B-A4B-it-assistant-GGUF/resolve/main/hero.png" alt="Gemma 4 26B A4B It Assistant" style="width:100%; max-width:100%; height:auto; margin-bottom:0.6em;"/>

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<a href="https://huggingface.co/google/gemma-4-26B-A4B-it-assistant"><strong>Base model: google/gemma-4-26B-A4B-it-assistant</strong></a>

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Gemma 4 26B A4B It Assistant, self-quantized to GGUF by Atomic Chat. Built straight from Google's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline.

Highlights

• 25.2B total / 3.8B active per token parameters: the weights this repo quantizes.
• Context length: 256K tokens, as published by Google.
• 30 layers: Dense decoder, hybrid sliding-window (1024) and global attention.
• Modalities: the base model handles Text, Image; this repo ships text-only quants, it carries no vision projector.
• Full imatrix ladder: every quant is calibrated with an importance matrix.
• Reasoning: All models in the family are designed as highly capable reasoners, with configurable thinking modes.
• Diverse & Efficient Architectures: Offers Dense and Mixture-of-Experts (MoE) variants of different sizes for scalable deployment.

> [!NOTE]

> These GGUFs are self-quantized from the original weights, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.

> [!IMPORTANT]

> Always pass --jinja so the Gemma 4 26B A4B It Assistant chat template is applied. Without it the model can emit malformed turns.

Model Overview

| Property | Value |

|---|---|

| Base model | google/gemma-4-26B-A4B-it-assistant |

| Parameters | 25.2B total / 3.8B active per token |

| Layers | 30 |

| Sliding window | 1024 tokens |

| Context length | 256K tokens |

| Vocabulary | 262K |

| Modalities | Text, Image in the base model; text only in this repo, it ships no vision projector |

| Architecture | Dense decoder, hybrid sliding-window (1024) and global attention, 16 attention heads over 8 KV heads, Gemma4AssistantForCausalLM |

| This repo | GGUF quants (imatrix). Quants: Q4_K_S, Q4_K_M, Q5_K_M, Q8_0, F16 |

Benchmarks

| Benchmark | Score |

|---|---|

| MMLU Pro | 82.6% |

| AIME 2026 no tools | 88.3% |

| LiveCodeBench v6 | 77.1% |

| Codeforces ELO | 1718 |

| GPQA Diamond | 82.3% |

| Tau2 (average over 3) | 68.2% |

| HLE no tools | 8.7% |

| HLE with search | 17.2% |

| BigBench Extra Hard | 64.8% |

| MMMLU | 86.3% |

| MMMU Pro | 73.8% |

| OmniDocBench 1.5 (average edit distance, lower is better) | 0.149 |

| MATH-Vision | 82.4% |

| MedXPertQA MM | 58.1% |

| MRCR v2 8 needle 128k (average) | 44.1% |

Scores are Google's published results for the base google/gemma-4-26B-A4B-it-assistant, not our own measurements. Quantization preserves the large majority of this; Q4_K_M and up stay close to full precision.

Choosing a quant

| Quant | Size | Notes |

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

| Q4_K_S | 321 MB | Compact 4-bit, fast. |

| Q4_K_M | 325 MB | Recommended default. Best balance of size, speed and quality. |

| Q5_K_M | 342 MB | Higher quality, low loss. |

| Q8_0 | 462 MB | Effectively lossless, reference quality. |

| F16 | 0.9 GB | Unquantized reference, twice the size of Q8_0. |

> [!TIP]

> Pick the largest file that fits your (V)RAM with room for context. Q4_K_M is the sweet spot for most setups; Q6_K or Q8_0 for maximum fidelity.

Get started

Run Gemma 4 26B A4B It Assistant locally with:

• Atomic Chat: the easiest path. Open the app, search AtomicChat/gemma-4-26B-A4B-it-assistant-GGUF, pick a quant, hit Use this model.
• llama.cpp: llama-server -hf AtomicChat/gemma-4-26B-A4B-it-assistant-GGUF:Q4_K_M --jinja -c 8192
• Ollama: ollama run hf.co/AtomicChat/gemma-4-26B-A4B-it-assistant-GGUF:Q4_K_M
• LM Studio / Jan: search the repo id, download any quant.

Best practices

| Parameter | Value |

|---|---|

| temperature | 1.0 |

| top_p | 0.95 |

| top_k | 64 |

Google's recommended sampling configuration for google/gemma-4-26B-A4B-it-assistant.

Run in llama.cpp

git clone https://github.com/ggml-org/llama.cpp
cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j --target llama-cli llama-server

./llama.cpp/build/bin/llama-server \
    -hf AtomicChat/gemma-4-26B-A4B-it-assistant-GGUF:Q4_K_M \
    --jinja -ngl 99 -c 8192 -fa on

How these were made

1. Download google/gemma-4-26B-A4B-it-assistant (original weights).
2. Convert to f16 GGUF with llama.cpp.
3. Build an importance matrix over our calibration corpus.
4. Quantize the ladder with --imatrix.

License

Original model by Google, released under the Apache 2.0 license. Full terms: Apache 2.0. Quantized by Atomic Chat.