AlexAtomic/gemma4-e4b-it-GGUF overview

---

license: apache-2.0

license_link: https://ai.google.dev/gemma/docs/gemma_4_license

thumbnail: https://huggingface.co/AlexAtomic/gemma4-e4b-it-GGUF/resolve/main/hero.png

base_model:

• google/gemma-4-E4B-it

base_model_relation: quantized

quantized_by: AlexAtomic

pipeline_tag: image-text-to-text

library_name: gguf

tags:

• atomic-chat
• gemma
• gemma4
• google
• gguf
• imatrix
• quantized
• llama.cpp

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<img src="https://huggingface.co/AlexAtomic/gemma4-e4b-it-GGUF/resolve/main/hero.png" alt="Gemma 4 E4B" style="width:100%; max-width:100%; height:auto; margin-bottom:0.6em;"/>

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<a href="https://huggingface.co/google/gemma-4-E4B-it"><strong>Base model: google/gemma-4-E4B-it</strong></a>

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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.

<img src="https://huggingface.co/AlexAtomic/gemma4-e4b-it-GGUF/resolve/main/benchmark.png" alt="Gemma 4 E4B benchmark scores" style="width:100%; max-width:900px;"/>

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

1. Download google/gemma-4-E4B-it (original weights).
2. Convert to f16 GGUF with llama.cpp.
3. Build an importance matrix over calibration_datav3 (100 chunks).
4. Quantize the full ladder with --imatrix.
5. 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.