Instructions to use AlexAtomic/gemma4-e4b-it-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AlexAtomic/gemma4-e4b-it-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AlexAtomic/gemma4-e4b-it-GGUF",
	filename="gemma4-e4b-it-IQ3_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use AlexAtomic/gemma4-e4b-it-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AlexAtomic/gemma4-e4b-it-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf AlexAtomic/gemma4-e4b-it-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AlexAtomic/gemma4-e4b-it-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf AlexAtomic/gemma4-e4b-it-GGUF:UD-Q4_K_XL

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf AlexAtomic/gemma4-e4b-it-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf AlexAtomic/gemma4-e4b-it-GGUF:UD-Q4_K_XL

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf AlexAtomic/gemma4-e4b-it-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AlexAtomic/gemma4-e4b-it-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/AlexAtomic/gemma4-e4b-it-GGUF:UD-Q4_K_XL

LM Studio
Jan

vLLM

How to use AlexAtomic/gemma4-e4b-it-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AlexAtomic/gemma4-e4b-it-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AlexAtomic/gemma4-e4b-it-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/AlexAtomic/gemma4-e4b-it-GGUF:UD-Q4_K_XL

Ollama
How to use AlexAtomic/gemma4-e4b-it-GGUF with Ollama:
```
ollama run hf.co/AlexAtomic/gemma4-e4b-it-GGUF:UD-Q4_K_XL
```

Unsloth Studio

How to use AlexAtomic/gemma4-e4b-it-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for AlexAtomic/gemma4-e4b-it-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for AlexAtomic/gemma4-e4b-it-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AlexAtomic/gemma4-e4b-it-GGUF to start chatting

How to use AlexAtomic/gemma4-e4b-it-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AlexAtomic/gemma4-e4b-it-GGUF:UD-Q4_K_XL

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "AlexAtomic/gemma4-e4b-it-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use AlexAtomic/gemma4-e4b-it-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AlexAtomic/gemma4-e4b-it-GGUF:UD-Q4_K_XL

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default AlexAtomic/gemma4-e4b-it-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use AlexAtomic/gemma4-e4b-it-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AlexAtomic/gemma4-e4b-it-GGUF:UD-Q4_K_XL

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "AlexAtomic/gemma4-e4b-it-GGUF:UD-Q4_K_XL" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use AlexAtomic/gemma4-e4b-it-GGUF with Docker Model Runner:
```
docker model run hf.co/AlexAtomic/gemma4-e4b-it-GGUF:UD-Q4_K_XL
```

Lemonade

How to use AlexAtomic/gemma4-e4b-it-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AlexAtomic/gemma4-e4b-it-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.gemma4-e4b-it-GGUF-UD-Q4_K_XL

List all available models

lemonade list

gemma4-e4b-it-GGUF / README.md

AlexAtomic

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	---
	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
	---

	<center>

	<div style="display:flex; justify-content:center; align-items:center; gap:2%; max-width:560px; margin:0 auto;">
	<a href="https://atomic.chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AlexAtomic/gemma4-e4b-it-GGUF/resolve/main/pill_atomic_v3.png" alt="Atomic Chat" style="width:100%; height:auto; max-width:186px;"></a>
	<a href="https://discord.gg/8wGSsvmg4V" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AlexAtomic/gemma4-e4b-it-GGUF/resolve/main/pill_discord_v3.png" alt="Join Discord" style="width:100%; height:auto; max-width:184px;"></a>
	<a href="https://github.com/AtomicBot-ai/Atomic-Chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AlexAtomic/gemma4-e4b-it-GGUF/resolve/main/pill_github_v3.png" alt="GitHub" style="width:100%; height:auto; max-width:141px;"></a>
	</div>

	<br/>

	<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;"/>

	<div style="display:flex; justify-content:center; gap:0.5em;">
	<a href="https://huggingface.co/google/gemma-4-E4B-it"><strong>Base model: google/gemma-4-E4B-it</strong></a>
	</div>
	</center>

	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.

	<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](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.