gemma-4-E4B-it-GGUF

Gemma 4 E4B, 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: Text, Image, Audio.
• Full imatrix ladder: every quant is calibrated with an importance matrix, published here alongside the quants.
• 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 chat template is applied. Without it the model can emit malformed turns.

Model Overview

[!NOTE] Gemma 4 E4B is multimodal. This repo ships the mmproj-gemma4-e4b-it-f16.gguf vision projector. With -hf it is pulled automatically; otherwise pass --mmproj. Use llama-mtmd-cli or llama-server to feed images.

Scores are Google's published results for the base google/gemma-4-E4B-it, not our own measurements. Quantization preserves the large majority of this; Q4_K_M and up stay close to full precision.

Choosing a quant

[!TIP] Pick the largest file that fits your (V)RAM with room for context. Q4_K_M or UD-Q4_K_XL is the sweet spot for most setups; Q6_K or Q8_0 for maximum fidelity.

Get started

Run Gemma 4 E4B locally with:

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

Best practices

Google's recommended sampling configuration for google/gemma-4-E4B-it. Pass images through llama-mtmd-cli or llama-server with the projector.

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-E4B-it-GGUF:Q4_K_M \
    --jinja -ngl 99 -c 8192 -fa on

How these were made

1. Download google/gemma-4-E4B-it (original weights).
2. Convert to f16 GGUF with llama.cpp.
3. Build an importance matrix over our calibration corpus, published here as imatrix-coding.gguf.
4. Quantize the ladder with --imatrix.
5. UD-Q4_K_XL additionally pins the token-embedding and output tensors to Q8_0.

License

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