---
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
---
**Gemma 4 E4B**, self-quantized to GGUF by [Atomic Chat](https://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](https://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
```bash
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
```
```bash
./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](https://github.com/ggerganov/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.
