Instructions to use Shariyat/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 Shariyat/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 Shariyat/gemma-4-E2B-it-GGUF:IQ3_S
# Run inference directly in the terminal:
llama cli -hf Shariyat/gemma-4-E2B-it-GGUF:IQ3_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Shariyat/gemma-4-E2B-it-GGUF:IQ3_S
# Run inference directly in the terminal:
llama cli -hf Shariyat/gemma-4-E2B-it-GGUF:IQ3_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 Shariyat/gemma-4-E2B-it-GGUF:IQ3_S
# Run inference directly in the terminal:
./llama-cli -hf Shariyat/gemma-4-E2B-it-GGUF:IQ3_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 Shariyat/gemma-4-E2B-it-GGUF:IQ3_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Shariyat/gemma-4-E2B-it-GGUF:IQ3_S

Use Docker

docker model run hf.co/Shariyat/gemma-4-E2B-it-GGUF:IQ3_S

LM Studio
Jan

vLLM

How to use Shariyat/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 "Shariyat/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": "Shariyat/gemma-4-E2B-it-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Shariyat/gemma-4-E2B-it-GGUF:IQ3_S

Ollama
How to use Shariyat/gemma-4-E2B-it-GGUF with Ollama:
```
ollama run hf.co/Shariyat/gemma-4-E2B-it-GGUF:IQ3_S
```
Unsloth Desktop

How to use Shariyat/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 Shariyat/gemma-4-E2B-it-GGUF:IQ3_S

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Shariyat/gemma-4-E2B-it-GGUF:IQ3_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Shariyat/gemma-4-E2B-it-GGUF with Docker Model Runner:
```
docker model run hf.co/Shariyat/gemma-4-E2B-it-GGUF:IQ3_S
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Shariyat/gemma-4-E2B-it-GGUF:IQ3_S

Run and chat with the model

lemonade run user.gemma-4-E2B-it-GGUF-IQ3_S

List all available models

lemonade list

Hermes Agent

How to use Shariyat/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 Shariyat/gemma-4-E2B-it-GGUF:IQ3_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 Shariyat/gemma-4-E2B-it-GGUF:IQ3_S

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Shariyat/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 Shariyat/gemma-4-E2B-it-GGUF:IQ3_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 "Shariyat/gemma-4-E2B-it-GGUF:IQ3_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 Instruct — GGUF Quantizations

Quantized versions of google/gemma-4-E2B-it, converted to GGUF format and optimized for local deployment on PC and mobile devices.

All variants were produced using an importance matrix (imatrix) to preserve model quality as much as possible at each compression level.

Available Files

File	Size	Min RAM	Recommended For
`gemma-4-E2B-it-Q4_0-imat.gguf`	3.4 Go	5 Go	Desktop / laptop
`gemma-4-E2B-it-Q4_0.gguf`	3.4 Go	5 Go	Desktop / laptop
`gemma-4-E2B-it-IQ4_XS-imat.gguf`	3.3 Go	4.5 Go	Best quality/size ratio ✅
`gemma-4-E2B-it-Q3_K_M-imat.gguf`	3.2 Go	4 Go	Good balance
`gemma-4-E2B-it-Q3_K_M.gguf`	3.2 Go	4 Go	Good balance
`gemma-4-E2B-it-Q3_K_S-imat.gguf`	3.1 Go	4 Go	Mobile mid-range
`gemma-4-E2B-it-IQ3_S-imat.gguf`	3.1 Go	4 Go	Mobile mid-range
`gemma-4-E2B-it-Q2_K-imat.gguf`	3.0 Go	3.5 Go	Low-end devices

Not sure which to pick? Start with IQ4_XS-imat on PC, and Q3_K_S-imat or Q2_K-imat on mobile.

What is Quantization?

Quantization reduces the precision of a model's numerical weights — for example from 16-bit floating point down to 3 or 4 bits per value. This dramatically shrinks the file size and RAM usage, making it possible to run large language models on consumer hardware.

The trade-off is a small loss in output quality, which is more noticeable at lower bit levels (Q2) and barely perceptible at higher ones (Q4).

imatrix (importance matrix) is a technique that analyzes which weights matter most for the model's behavior and preserves them more carefully during compression. The -imat variants in this repo use this method and consistently outperform standard quantizations of the same bit level.

Quick Start — Desktop / Laptop

1. Download llama.cpp

Pre-built binaries for Windows, macOS, and Linux are available on the llama.cpp releases page. Download the latest release for your platform.

2. Download a model file

Pick one file from the list above and download it from this repository.

3. Run

./llama-cli \
  -m gemma-4-E2B-it-IQ4_XS-imat.gguf \
  -p "<start_of_turn>user\nHello, how are you?<end_of_turn>\n<start_of_turn>model\n" \
  -n 512 \
  --temp 1.0 --top-p 0.95 --top-k 64 \
  --no-display-prompt

Run as a local chat server (OpenAI-compatible API)

./llama-server \
  -m gemma-4-E2B-it-IQ4_XS-imat.gguf \
  --port 8080 \
  --ctx-size 4096

Then open http://localhost:8080 in your browser for a built-in chat interface, or connect any OpenAI-compatible client to http://localhost:8080/v1.

Quick Start — Ollama (Easiest on Desktop)

Ollama is the simplest way to run GGUF models locally with a single command.

# 1. Install Ollama (Linux/macOS)
curl -fsSL https://ollama.com/install.sh | sh

# 2. Create a Modelfile
cat > Modelfile << 'EOF'
FROM ./gemma-4-E2B-it-IQ4_XS-imat.gguf

TEMPLATE """<start_of_turn>system
{{ .System }}<end_of_turn>
<start_of_turn>user
{{ .Prompt }}<end_of_turn>
<start_of_turn>model
{{ .Response }}<end_of_turn>
"""

SYSTEM "You are a helpful assistant."
PARAMETER temperature 1.0
PARAMETER top_p 0.95
PARAMETER top_k 64
PARAMETER num_ctx 4096
EOF

# 3. Import and run
ollama create gemma4-e2b -f Modelfile
ollama run gemma4-e2b

Quick Start — Mobile (Android)

Several Android apps support GGUF models and run inference fully offline:

MLC Chat — open source, supports GGUF
ChatterUI — simple chat UI, llama.cpp backend
Pocketpal AI — polished interface, easy model import

General steps:

Transfer the GGUF file to your Android device (via USB or a file manager app)
Open the app and point it to the downloaded file
Start chatting — no internet connection required

Recommended files for mobile:

Device Type	Recommended File
Flagship (8+ Go RAM)	`IQ4_XS-imat` or `Q3_K_M-imat`
Mid-range (4–6 Go RAM)	`Q3_K_S-imat` or `IQ3_S-imat`
Low-end (3–4 Go RAM)	`Q2_K-imat`

Chat Template

Gemma 4 uses the following conversation format. Make sure your client applies it correctly:

<start_of_turn>system
Your system prompt here.<end_of_turn>
<start_of_turn>user
User message here.<end_of_turn>
<start_of_turn>model

Most modern clients (llama.cpp, Ollama, LM Studio, etc.) handle this automatically when loading a Gemma 4 GGUF file.

Recommended sampling parameters:

Parameter	Value
Temperature	1.0
Top-P	0.95
Top-K	64

Model Capabilities

Gemma 4 E2B is a 2.3B effective parameter multimodal model (text + image input, text output). Key strengths:

Multilingual — trained on 140+ languages, strong on French, English, Arabic, and more
Reasoning — configurable thinking mode for step-by-step problem solving
Coding — solid performance on code generation and completion
Long context — up to 128K token context window
Compact — designed from the ground up for on-device deployment

Note: Image input requires a multimodal-capable runtime. The GGUF files in this repo support text-only inference via llama.cpp.

Quantization Details

Step	Tool	Notes
HF → GGUF	`convert_hf_to_gguf.py`	F16 base file
Imatrix computation	`llama-imatrix`	Calibration dataset
Final quantization	`llama-quantize --imatrix`	Per variant

Base model: google/gemma-4-E2B-it (Apache 2.0)

License

These quantized files inherit the license of the original model: Apache 2.0. See the Gemma 4 license for full terms.

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GGUF

Model size

5B params

Architecture

gemma4

Hardware compatibility

2-bit

3-bit

4-bit

Model tree for Shariyat/gemma-4-E2B-it-GGUF

Base model

google/gemma-4-E2B

Finetuned

google/gemma-4-E2B-it

Quantized

(371)

this model