AlexAtomic/gemma4-e4b-it-GGUF

Base model: google/gemma-4-E4B-it

Gemma 4 E4B, self-quantized to GGUF by Atomic Chat. Built straight from Google's original weights with a per-tensor importance matrix. Runs fully offline.

Highlights

Natively multimodal — handles text, image, and audio input and generates text output.
4.5B effective parameters (8B with embeddings) — the "E" stands for "effective", using Per-Layer Embeddings (PLE) for on-device efficiency.
128K-token context window built on a hybrid local/global attention mechanism.
Built-in thinking mode — configurable step-by-step reasoning, triggered with the <|think|> token.
Native function calling for structured tool use and agentic workflows.
Multilingual — out-of-the-box support for 35+ languages, pre-trained on 140+ languages.

[!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); uses Per-Layer Embeddings (PLE)
Layers	42
Context length	128K tokens
Vocabulary	262K
Modalities	Text, Image, Audio
Architecture	Dense, hybrid local sliding-window (512) + global attention with p-RoPE
This repo	GGUF quants (imatrix) + vision mmproj

[!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. Quantization preserves the large majority of this; Q4_K_M and up sit within a point or two of full precision.

Choosing a quant

Quant	Size	Notes
`Q2_K`	4.4 GB	Smallest. Minimal RAM, clear quality drop.
`IQ3_M`	4.7 GB	Beats Q3 at 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 Q4, fast.
`Q4_K_M`	5.3 GB	Recommended default. Best balance of size, speed and quality.
`UD-Q4_K_XL`	6.2 GB	Dynamic. Embeddings and output kept at Q8_0 for higher quality at a Q4 footprint.
`Q5_K_S`	5.7 GB	Higher quality.
`Q5_K_M`	5.8 GB	Higher quality, low loss.
`Q6_K`	6.2 GB	Near lossless.
`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 AlexAtomic/gemma4-e4b-it-GGUF, pick a quant, hit Use this model.
llama.cpp: llama-server -hf AlexAtomic/gemma4-e4b-it-GGUF:Q4_K_M --jinja -c 8192
Ollama: ollama run hf.co/AlexAtomic/gemma4-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 standardized sampling configuration recommended across all use cases.

Run in llama.cpp

git clone https://github.com/ggerganov/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 AlexAtomic/gemma4-e4b-it-GGUF:UD-Q4_K_XL \
    --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 calibration_datav3 (100 chunks).
Quantize the full ladder with --imatrix.
UD-Q4_K_XL additionally pins the token-embedding and output tensors to Q8_0.

License

Original model by Google DeepMind, released under the Apache 2.0 license. Quantized by Atomic Chat.