Instructions to use GestaltLabs/Gemma-4-E4B-SABER-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GestaltLabs/Gemma-4-E4B-SABER-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="GestaltLabs/Gemma-4-E4B-SABER-GGUF",
	filename="Gemma-4-E4B-SABER-BF16.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

llama.cpp

How to use GestaltLabs/Gemma-4-E4B-SABER-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf GestaltLabs/Gemma-4-E4B-SABER-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf GestaltLabs/Gemma-4-E4B-SABER-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf GestaltLabs/Gemma-4-E4B-SABER-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf GestaltLabs/Gemma-4-E4B-SABER-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 GestaltLabs/Gemma-4-E4B-SABER-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf GestaltLabs/Gemma-4-E4B-SABER-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 GestaltLabs/Gemma-4-E4B-SABER-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf GestaltLabs/Gemma-4-E4B-SABER-GGUF:Q4_K_M

Use Docker

docker model run hf.co/GestaltLabs/Gemma-4-E4B-SABER-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use GestaltLabs/Gemma-4-E4B-SABER-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GestaltLabs/Gemma-4-E4B-SABER-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": "GestaltLabs/Gemma-4-E4B-SABER-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/GestaltLabs/Gemma-4-E4B-SABER-GGUF:Q4_K_M

Ollama
How to use GestaltLabs/Gemma-4-E4B-SABER-GGUF with Ollama:
```
ollama run hf.co/GestaltLabs/Gemma-4-E4B-SABER-GGUF:Q4_K_M
```

Unsloth Studio new

How to use GestaltLabs/Gemma-4-E4B-SABER-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 GestaltLabs/Gemma-4-E4B-SABER-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 GestaltLabs/Gemma-4-E4B-SABER-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for GestaltLabs/Gemma-4-E4B-SABER-GGUF to start chatting

Pi new

How to use GestaltLabs/Gemma-4-E4B-SABER-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf GestaltLabs/Gemma-4-E4B-SABER-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": "Gemma-4-E4B-SABER-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use GestaltLabs/Gemma-4-E4B-SABER-GGUF with Docker Model Runner:
```
docker model run hf.co/GestaltLabs/Gemma-4-E4B-SABER-GGUF:Q4_K_M
```

Lemonade

How to use GestaltLabs/Gemma-4-E4B-SABER-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull GestaltLabs/Gemma-4-E4B-SABER-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Gemma-4-E4B-SABER-GGUF-Q4_K_M

List all available models

lemonade list

Gemma-4-E4B-SABER GGUF

This repository contains GGUF conversions of GestaltLabs/Gemma-4-E4B-SABER for llama.cpp-compatible runtimes.

The source model is a Gemma 4 E4B instruction model modified with the SABER/refusal-ablation workflow. These files preserve the source tokenizer and chat template metadata in GGUF form.

Files

File	Quantization	Approx. size
`Gemma-4-E4B-SABER-BF16.gguf`	BF16	13.92 GiB
`Gemma-4-E4B-SABER-Q8_0.gguf`	Q8_0	7.43 GiB
`Gemma-4-E4B-SABER-Q6_K.gguf`	Q6_K	5.75 GiB
`Gemma-4-E4B-SABER-Q5_K_M.gguf`	Q5_K_M	5.33 GiB
`Gemma-4-E4B-SABER-Q4_K_M.gguf`	Q4_K_M	4.94 GiB
`Gemma-4-E4B-SABER-Q3_K_M.gguf`	Q3_K_M	4.49 GiB
`Gemma-4-E4B-SABER-Q2_K.gguf`	Q2_K	4.08 GiB

Quantization Notes

The BF16 GGUF was converted from the original Hugging Face safetensors checkpoint using a local llama.cpp build with Gemma 4 support. The quantized GGUF files were produced from that BF16 GGUF using llama-quantize.

Recommended starting points:

Q8_0: highest-quality quantized option.
Q6_K: strong quality/size tradeoff.
Q4_K_M: compact general-purpose option.
Q2_K and Q3_K_M: smallest files, with larger quality tradeoffs.

No importance matrix was used.

Example

llama-cli \
  -m Gemma-4-E4B-SABER-Q4_K_M.gguf \
  -p "Explain quantum computing in simple terms."

Use a current llama.cpp build with Gemma 4 support.

Source

Source model: GestaltLabs/Gemma-4-E4B-SABER
Base model: google/gemma-4-E4B-it
License: Gemma license. See the source model and base model license terms.

Conversion Details

Source format: Hugging Face safetensors, BF16.
GGUF converter: llama.cpp convert_hf_to_gguf.py.
GGUF quantizer: llama.cpp llama-quantize.
Quant types: BF16, Q8_0, Q6_K, Q5_K_M, Q4_K_M, Q3_K_M, Q2_K.

Downloads last month: 764

GGUF

Model size

7B params

Architecture

gemma4

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for GestaltLabs/Gemma-4-E4B-SABER-GGUF

Base model

google/gemma-4-E4B

Finetuned

google/gemma-4-E4B-it

Finetuned

GestaltLabs/Gemma-4-E4B-SABER

Quantized

(6)

this model