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AtomicChat/gemma-4-E4B-it-assistant-GGUF

sourceHugging Faceapache-2.0updated 2mo agoView on Hugging Face
24likes1.8kdownloads
Model Card

<center>

<div style="display:flex; justify-content:center; align-items:center; gap:2%; max-width:560px; margin:0 auto;"> <a href="https://atomic.chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/gemma-4-E4B-it-assistant-GGUF/resolve/main/pillatomicv3.png" alt="Atomic Chat" style="width:100%; height:auto; max-width:186px;"></a> <a href="https://discord.gg/8wGSsvmg4V" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/gemma-4-E4B-it-assistant-GGUF/resolve/main/pilldiscordv3.png" alt="Join Discord" style="width:100%; height:auto; max-width:184px;"></a> <a href="https://github.com/AtomicBot-ai/Atomic-Chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/gemma-4-E4B-it-assistant-GGUF/resolve/main/pillgithubv3.png" alt="GitHub" style="width:100%; height:auto; max-width:141px;"></a> </div>

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<img src="https://huggingface.co/AtomicChat/gemma-4-E4B-it-assistant-GGUF/resolve/main/hero.png" alt="Gemma 4 E4B It Assistant" style="width:100%; max-width:100%; height:auto; margin-bottom:0.6em;"/>

<div style="display:flex; justify-content:center; gap:0.5em;"> <a href="https://huggingface.co/google/gemma-4-E4B-it-assistant"><strong>Base model: google/gemma-4-E4B-it-assistant</strong></a> </div> </center>

Gemma 4 E4B 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

  • —4.5B effective (8B with embeddings) parameters: the weights this repo quantizes.
  • —Context length: 128K tokens, as published by Google.
  • —42 layers: Dense decoder, hybrid sliding-window (512) and global attention.
  • —Modalities: the base model handles Text, Image, Audio; 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 E4B It Assistant chat template is applied. Without it the model can emit malformed turns.

Model Overview

PropertyValue
Base modelgoogle/gemma-4-E4B-it-assistant
Parameters4.5B effective (8B with embeddings)
Layers42
Sliding window512 tokens
Context length128K tokens
Vocabulary262K
ModalitiesText, Image, Audio in the base model; text only in this repo, it ships no vision projector
ArchitectureDense decoder, hybrid sliding-window (512) and global attention, 4 attention heads over 2 KV heads, Gemma4AssistantForCausalLM
This repoGGUF quants (imatrix). Quants: Q4_K_S, Q4_K_M, Q5_K_M, Q8_0, F16

Benchmarks

BenchmarkScore
MMLU Pro69.4%
AIME 2026 no tools42.5%
LiveCodeBench v652.0%
Codeforces ELO940
GPQA Diamond58.6%
Tau2 (average over 3)42.2%
BigBench Extra Hard33.1%
MMMLU76.6%
MMMU Pro52.6%
OmniDocBench 1.5 (average edit distance, lower is better)0.181
MATH-Vision59.5%
MedXPertQA MM28.7%
CoVoST35.54
FLEURS (lower is better)0.08
MRCR v2 8 needle 128k (average)25.4%

Scores are Google's published results for the base google/gemma-4-E4B-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

QuantSizeNotes
Q4_K_S78 MBCompact 4-bit, fast.
`Q4_K_M`79 MBRecommended default. Best balance of size, speed and quality.
Q5_K_M80 MBHigher quality, low loss.
Q8_0100 MBEffectively lossless, reference quality.
F16174 MBUnquantized 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 E4B It Assistant locally with:

  • —[Atomic Chat](https://atomic.chat): the easiest path. Open the app, search AtomicChat/gemma-4-E4B-it-assistant-GGUF, pick a quant, hit Use this model.
  • —llama.cpp: llama-server -hf AtomicChat/gemma-4-E4B-it-assistant-GGUF:Q4_K_M --jinja -c 8192
  • —Ollama: ollama run hf.co/AtomicChat/gemma-4-E4B-it-assistant-GGUF:Q4_K_M
  • —LM Studio / Jan: search the repo id, download any quant.

Best practices

ParameterValue
temperature1.0
top_p0.95
top_k64

Google's recommended sampling configuration for google/gemma-4-E4B-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-E4B-it-assistant-GGUF:Q4_K_M \
    --jinja -ngl 99 -c 8192 -fa on

How these were made

  1. 1.Download google/gemma-4-E4B-it-assistant (original weights).
  2. 2.Convert to f16 GGUF with llama.cpp.
  3. 3.Build an importance matrix over our calibration corpus.
  4. 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.