Instructions to use dahus/gemma-4-e2b-it-Q4_K_S-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 dahus/gemma-4-e2b-it-Q4_K_S-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 dahus/gemma-4-e2b-it-Q4_K_S-GGUF:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf dahus/gemma-4-e2b-it-Q4_K_S-GGUF:Q4_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dahus/gemma-4-e2b-it-Q4_K_S-GGUF:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf dahus/gemma-4-e2b-it-Q4_K_S-GGUF:Q4_K_S

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 dahus/gemma-4-e2b-it-Q4_K_S-GGUF:Q4_K_S
# Run inference directly in the terminal:
./llama-cli -hf dahus/gemma-4-e2b-it-Q4_K_S-GGUF:Q4_K_S

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 dahus/gemma-4-e2b-it-Q4_K_S-GGUF:Q4_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dahus/gemma-4-e2b-it-Q4_K_S-GGUF:Q4_K_S

Use Docker

docker model run hf.co/dahus/gemma-4-e2b-it-Q4_K_S-GGUF:Q4_K_S

LM Studio
Jan

vLLM

How to use dahus/gemma-4-e2b-it-Q4_K_S-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dahus/gemma-4-e2b-it-Q4_K_S-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": "dahus/gemma-4-e2b-it-Q4_K_S-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dahus/gemma-4-e2b-it-Q4_K_S-GGUF:Q4_K_S

Ollama
How to use dahus/gemma-4-e2b-it-Q4_K_S-GGUF with Ollama:
```
ollama run hf.co/dahus/gemma-4-e2b-it-Q4_K_S-GGUF:Q4_K_S
```

Unsloth Studio

How to use dahus/gemma-4-e2b-it-Q4_K_S-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 dahus/gemma-4-e2b-it-Q4_K_S-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 dahus/gemma-4-e2b-it-Q4_K_S-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dahus/gemma-4-e2b-it-Q4_K_S-GGUF to start chatting

How to use dahus/gemma-4-e2b-it-Q4_K_S-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dahus/gemma-4-e2b-it-Q4_K_S-GGUF:Q4_K_S

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": "dahus/gemma-4-e2b-it-Q4_K_S-GGUF:Q4_K_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use dahus/gemma-4-e2b-it-Q4_K_S-GGUF with Docker Model Runner:
```
docker model run hf.co/dahus/gemma-4-e2b-it-Q4_K_S-GGUF:Q4_K_S
```

Lemonade

How to use dahus/gemma-4-e2b-it-Q4_K_S-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dahus/gemma-4-e2b-it-Q4_K_S-GGUF:Q4_K_S

Run and chat with the model

lemonade run user.gemma-4-e2b-it-Q4_K_S-GGUF-Q4_K_S

List all available models

lemonade list

Hermes Agent

How to use dahus/gemma-4-e2b-it-Q4_K_S-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 dahus/gemma-4-e2b-it-Q4_K_S-GGUF:Q4_K_S

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 dahus/gemma-4-e2b-it-Q4_K_S-GGUF:Q4_K_S

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use dahus/gemma-4-e2b-it-Q4_K_S-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dahus/gemma-4-e2b-it-Q4_K_S-GGUF:Q4_K_S

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 "dahus/gemma-4-e2b-it-Q4_K_S-GGUF:Q4_K_S" \
  --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"

Gemma 4 E2B it — Q4_K_S GGUF

4-bit small quantized GGUF version of google/gemma-4-e2b-it.
Slightly smaller and faster than Q4_K_M with near-identical output quality.

Other quantizations in this series:
Q2_K · Q3_K_S · Q3_K_M · Q4_K_M · Q5_K_S · Q5_K_M · Q6_K · Q8

File Info

Property	Value
Format	GGUF Q4_K_S
File size	3.37 GB
Bits per weight	~4
Size vs F16	2.8× smaller

Benchmark Results

Tested across 4 categories (Math, Logic, Code, Science), 3 prompts each.
Greedy decoding, 200 max new tokens. Metrics compare logit distributions vs F16 baseline.

Results by Category

Category	Speed (tok/s)	SQNR	Top-1 Agreement	KL Divergence
🔢 Math	25.0	19.0 dB	84.1%	0.2770
🧠 Logic	25.0	19.2 dB	78.7%	0.4684
💻 Code	25.2	19.4 dB	81.2%	0.3213
🔬 Science	24.9	18.8 dB	79.4%	0.3157
Overall	25.0	19.10 dB	80.9%	0.3456

Quantization Comparison

Model	Size	Speed (tok/s)	vs F16 speed	SQNR	Top-1 Agree	KL Div
F16 (baseline)	8.67 GB	5.7	1.0×	baseline	baseline	baseline
Q2_K	2.99 GB	31.6	5.6×	5.85 dB	32.0%	4.1149
Q3_K_S	3.11 GB	28.9	5.1×	10.12 dB	63.2%	1.2605
Q3_K_M	2.98 GB	27.4	4.8×	13.93 dB	63.2%	1.6747
Q4_K_S (this)	3.37 GB	25.0	4.4×	19.10 dB	80.9%	0.3456
Q4_K_M	3.43 GB	24.0	4.2×	20.33 dB	82.4%	0.3356
Q5_K_S	3.6 GB	21.9	3.9x	23.32 dB	87.7%	0.1547
Q5_K_M	3.63 GB	22.0	3.9×	23.25 dB	86.9%	0.1248
Q8	4.97 GB	16.2	2.9×	37.11 dB	96.0%	0.0171

Key Findings

Quality: 80.9% Top-1 agreement — strong and coherent outputs across all task types
Speed: 25.0 tok/s — slightly faster than Q4_K_M (24.0 tok/s)
Size: 3.13 GB — 60 MB smaller than Q4_K_M
vs Q4_K_M: Marginally lower quality metrics (1.2 dB SQNR, 1.5% Top-1), but faster and smaller; the difference is imperceptible in practice
Best for: Same use case as Q4_K_M; prefer Q4_K_S if you want a touch more speed or are tight on the 4 GB boundary

Usage

# llama.cpp CLI
./llama-cli -m gemma-4-e2b-q4ks.gguf -p "Write a Python function for binary search." -n 200

# llama-cpp-python
from llama_cpp import Llama

llm = Llama(model_path="gemma-4-e2b-q4ks.gguf", n_ctx=2048)
output = llm("Write a Python function for binary search.", max_tokens=200)
print(output["choices"][0]["text"])

Hardware

Tested on: CPU inference (llama.cpp)
Context: 2048 tokens | Greedy decoding

Downloads last month: 142

GGUF

Model size

5B params

Architecture

gemma4

Hardware compatibility

4-bit