Instructions to use AtomicChat/gemma-4-E2B-it-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use AtomicChat/gemma-4-E2B-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 AtomicChat/gemma-4-E2B-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AtomicChat/gemma-4-E2B-it-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AtomicChat/gemma-4-E2B-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AtomicChat/gemma-4-E2B-it-GGUF:Q4_K_M

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 AtomicChat/gemma-4-E2B-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AtomicChat/gemma-4-E2B-it-GGUF:Q4_K_M

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 AtomicChat/gemma-4-E2B-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AtomicChat/gemma-4-E2B-it-GGUF:Q4_K_M

Use Docker

docker model run hf.co/AtomicChat/gemma-4-E2B-it-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use AtomicChat/gemma-4-E2B-it-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AtomicChat/gemma-4-E2B-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": "AtomicChat/gemma-4-E2B-it-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AtomicChat/gemma-4-E2B-it-GGUF:Q4_K_M

Ollama
How to use AtomicChat/gemma-4-E2B-it-GGUF with Ollama:
```
ollama run hf.co/AtomicChat/gemma-4-E2B-it-GGUF:Q4_K_M
```

Unsloth Studio

How to use AtomicChat/gemma-4-E2B-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 AtomicChat/gemma-4-E2B-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 AtomicChat/gemma-4-E2B-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 AtomicChat/gemma-4-E2B-it-GGUF to start chatting

How to use AtomicChat/gemma-4-E2B-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 AtomicChat/gemma-4-E2B-it-GGUF:Q4_K_M

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": "AtomicChat/gemma-4-E2B-it-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use AtomicChat/gemma-4-E2B-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 AtomicChat/gemma-4-E2B-it-GGUF:Q4_K_M

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 AtomicChat/gemma-4-E2B-it-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use AtomicChat/gemma-4-E2B-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 AtomicChat/gemma-4-E2B-it-GGUF:Q4_K_M

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 "AtomicChat/gemma-4-E2B-it-GGUF:Q4_K_M" \
  --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 AtomicChat/gemma-4-E2B-it-GGUF with Docker Model Runner:
```
docker model run hf.co/AtomicChat/gemma-4-E2B-it-GGUF:Q4_K_M
```

Lemonade

How to use AtomicChat/gemma-4-E2B-it-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AtomicChat/gemma-4-E2B-it-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-4-E2B-it-GGUF-Q4_K_M

List all available models

lemonade list

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

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.

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.

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.

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

Download google/gemma-4-E2B-it (original weights).
Convert to f16 GGUF with llama.cpp.
Build an importance matrix over our calibration corpus, published here as imatrix-coding.gguf.
Quantize the ladder with --imatrix.