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

Property	Value
Base model	`google/gemma-4-E4B-it`
Parameters	4.5B effective (8B with embeddings)
Layers	42
Sliding window	512 tokens
Context length	128K tokens
Vocabulary	262K
Modalities	Text, Image, Audio
Architecture	Dense decoder, hybrid sliding-window (512) and global attention, 8 attention heads over 2 KV heads, `Gemma4ForConditionalGeneration`
This repo	GGUF quants (imatrix) and a vision mmproj; the importance matrix is published here as `imatrix-coding.gguf`. Quants: `Q2_K`, `IQ3_M`, `Q3_K_M`, `Q3_K_L`, `IQ4_XS`, `Q4_K_S`, `Q4_K_M`, `Q5_K_S`, `Q5_K_M`, `UD-Q4_K_XL`, `Q6_K`, `Q8_0`

[!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

Quant	Size	Notes
`Q2_K`	4.4 GB	Smallest K-quant. Minimal RAM, clear quality drop.
`IQ3_M`	4.7 GB	Beats Q3 at a similar size thanks to imatrix. Best low-RAM pick.
`Q3_K_M`	4.9 GB	Low quality but usable.
`Q3_K_L`	5.0 GB	A step above Q3_K_M.
`IQ4_XS`	5.1 GB	Excellent quality for size. Recommended low-bit.
`Q4_K_S`	5.2 GB	Compact 4-bit, fast.
`Q4_K_M`	5.3 GB	Recommended default. Best balance of size, speed and quality.
`Q5_K_S`	5.7 GB	Higher quality, slightly more compact than Q5_K_M.
`Q5_K_M`	5.8 GB	Higher quality, low loss.
`UD-Q4_K_XL`	6.2 GB	Dynamic. Embeddings and output kept at Q8_0 for higher quality at a Q4 footprint.
`Q6_K`	6.2 GB	Near lossless, noticeably lighter than Q8_0.
`Q8_0`	8.0 GB	Effectively lossless, reference quality.

[!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

Parameter	Value
temperature	1.0
top_p	0.95
top_k	64

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

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

How these were made

Download google/gemma-4-E4B-it (original weights).
Convert to f16 GGUF with llama.cpp.
Build an importance matrix over our calibration corpus, published here as imatrix-coding.gguf.
Quantize the ladder with --imatrix.
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.