You can deploy this model and start earning money today!

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

Name: gemma-4-26B-A4B-it-assistant-GGUF
Rating: 2.5 (3454 reviews)

google/gemma-4-26B-A4B-it-assistant

quantized

apache-2.0

endpoints_compatible

Views

No views yet

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

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

bash

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

bash

./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

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