AtomicChat/gemma-4-E2B-it-GGUF overview

---

license: apache-2.0

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

thumbnail: https://huggingface.co/AtomicChat/gemma-4-E2B-it-GGUF/resolve/main/hero.png

base_model:

• google/gemma-4-E2B-it

base_model_relation: quantized

quantized_by: AtomicChat

pipeline_tag: text-generation

library_name: gguf

tags:

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

---

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<img src="https://huggingface.co/AtomicChat/gemma-4-E2B-it-GGUF/resolve/main/hero.png" alt="Gemma 4 E2B" style="width:100%; max-width:100%; height:auto; margin-bottom:0.6em;"/>

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<a href="https://huggingface.co/google/gemma-4-E2B-it"><strong>Base model: google/gemma-4-E2B-it</strong></a>

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Gemma 4 E2B, 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

• 2.3B effective (5.1B with embeddings) parameters: the weights this repo quantizes.
• Context length: 128K tokens, as published by Google.
• 35 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, 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 E2B chat template is applied. Without it the model can emit malformed turns.

Model Overview

| Property | Value |

|---|---|

| Base model | google/gemma-4-E2B-it |

| Parameters | 2.3B effective (5.1B with embeddings) |

| Layers | 35 |

| Sliding window | 512 tokens |

| Context length | 128K tokens |

| Vocabulary | 262K |

| Modalities | Text, Image, Audio in the base model; text only in this repo, it ships no vision projector |

| Architecture | Dense decoder, hybrid sliding-window (512) and global attention, 8 attention heads over 1 KV head, Gemma4ForConditionalGeneration |

| This repo | GGUF quants (imatrix); 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, Q6_K, Q8_0 |

Benchmarks

| Benchmark | Score |

|---|---|

| MMLU Pro | 60.0% |

| AIME 2026 no tools | 37.5% |

| LiveCodeBench v6 | 44.0% |

| Codeforces ELO | 633 |

| GPQA Diamond | 43.4% |

| Tau2 (average over 3) | 24.5% |

| BigBench Extra Hard | 21.9% |

| MMMLU | 67.4% |

| MMMU Pro | 44.2% |

| OmniDocBench 1.5 (average edit distance, lower is better) | 0.290 |

| MATH-Vision | 52.4% |

| MedXPertQA MM | 23.5% |

| CoVoST | 33.47 |

| FLEURS (lower is better) | 0.09 |

| MRCR v2 8 needle 128k (average) | 19.1% |

Scores are Google's published results for the base google/gemma-4-E2B-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 | 3.0 GB | Smallest K-quant. Minimal RAM, clear quality drop. |

| IQ3_M | 3.1 GB | Beats Q3 at a similar size thanks to imatrix. Best low-RAM pick. |

| Q3_K_M | 3.2 GB | Low quality but usable. |

| Q3_K_L | 3.3 GB | A step above Q3_K_M. |

| IQ4_XS | 3.3 GB | Excellent quality for size. Recommended low-bit. |

| Q4_K_S | 3.4 GB | Compact 4-bit, fast. |

| Q4_K_M | 3.4 GB | Recommended default. Best balance of size, speed and quality. |

| Q5_K_S | 3.6 GB | Higher quality, slightly more compact than Q5_K_M. |

| Q5_K_M | 3.6 GB | Higher quality, low loss. |

| Q6_K | 3.8 GB | Near lossless, noticeably lighter than Q8_0. |

| Q8_0 | 5.0 GB | Effectively lossless, reference quality. |

> [!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 E2B locally with:

• Atomic Chat: the easiest path. Open the app, search AtomicChat/gemma-4-E2B-it-GGUF, pick a quant, hit Use this model.
• llama.cpp: llama-server -hf AtomicChat/gemma-4-E2B-it-GGUF:Q4_K_M --jinja -c 8192
• Ollama: ollama run hf.co/AtomicChat/gemma-4-E2B-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-E2B-it.

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-E2B-it-GGUF:Q4_K_M \
    --jinja -ngl 99 -c 8192 -fa on

How these were made

1. Download google/gemma-4-E2B-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.

License

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