Instructions to use brittlewis12/gemma-3-4b-it-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use brittlewis12/gemma-3-4b-it-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="brittlewis12/gemma-3-4b-it-GGUF",
	filename="gemma-3-4b-it.IQ1_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use brittlewis12/gemma-3-4b-it-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf brittlewis12/gemma-3-4b-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf brittlewis12/gemma-3-4b-it-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 brittlewis12/gemma-3-4b-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf brittlewis12/gemma-3-4b-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 brittlewis12/gemma-3-4b-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf brittlewis12/gemma-3-4b-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 brittlewis12/gemma-3-4b-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf brittlewis12/gemma-3-4b-it-GGUF:Q4_K_M

Use Docker

docker model run hf.co/brittlewis12/gemma-3-4b-it-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use brittlewis12/gemma-3-4b-it-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "brittlewis12/gemma-3-4b-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": "brittlewis12/gemma-3-4b-it-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/brittlewis12/gemma-3-4b-it-GGUF:Q4_K_M

Ollama
How to use brittlewis12/gemma-3-4b-it-GGUF with Ollama:
```
ollama run hf.co/brittlewis12/gemma-3-4b-it-GGUF:Q4_K_M
```

Unsloth Studio new

How to use brittlewis12/gemma-3-4b-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 brittlewis12/gemma-3-4b-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 brittlewis12/gemma-3-4b-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 brittlewis12/gemma-3-4b-it-GGUF to start chatting

Docker Model Runner
How to use brittlewis12/gemma-3-4b-it-GGUF with Docker Model Runner:
```
docker model run hf.co/brittlewis12/gemma-3-4b-it-GGUF:Q4_K_M
```

Lemonade

How to use brittlewis12/gemma-3-4b-it-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull brittlewis12/gemma-3-4b-it-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-3-4b-it-GGUF-Q4_K_M

List all available models

lemonade list

Gemma 3 4B IT GGUF

Original model: Gemma 3 4B IT

Model creator: Google DeepMind

Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models.

Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone.

This repo contains GGUF format model files for Google DeepMind’s Gemma 3 4B IT (instruction-tuned).

What is GGUF?

GGUF is a file format for representing AI models. It is the third version of the format, introduced by the llama.cpp team on August 21st 2023.

Converted with llama.cpp build b4875 (revision 7841fc7), using autogguf-rs.

Prompt template: Gemma Instruct

{{system_prompt}}
<start_of_turn>user
{{prompt}}<end_of_turn>
<start_of_turn>model

Download & run with cnvrs on iPhone, iPad, and Mac!

cnvrs is the best app for private, local AI on your device:

create & save Characters with custom system prompts & temperature settings
download and experiment with any GGUF model you can find on HuggingFace!
- or, use an API key with the chat completions-compatible model provider of your choice -- ChatGPT, Claude, Gemini, DeepSeek, & more!
make it your own with custom Theme colors
powered by Metal ⚡️ & Llama.cpp, with haptics during response streaming!
try it out yourself today, on Testflight!
- if you already have the app, download Gemma 3 4B IT now!
- cnvrsai:///models/search/hf?id=brittlewis12/gemma-3-4b-it-GGUF
follow cnvrs on twitter to stay up to date

Gemma 3 4B IT in cnvrs on macOS

Original Model Evaluation

These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation:

Reasoning and factuality

Benchmark	Metric	Gemma 3 PT 1B	Gemma 3 PT 4B	Gemma 3 PT 12B	Gemma 3 PT 27B
HellaSwag	10-shot	62.3	77.2	84.2	85.6
BoolQ	0-shot	63.2	72.3	78.8	82.4
PIQA	0-shot	73.8	79.6	81.8	83.3
SocialIQA	0-shot	48.9	51.9	53.4	54.9
TriviaQA	5-shot	39.8	65.8	78.2	85.5
Natural Questions	5-shot	9.48	20.0	31.4	36.1
ARC-c	25-shot	38.4	56.2	68.9	70.6
ARC-e	0-shot	73.0	82.4	88.3	89.0
WinoGrande	5-shot	58.2	64.7	74.3	78.8
BIG-Bench Hard	few-shot	28.4	50.9	72.6	77.7
DROP	1-shot	42.4	60.1	72.2	77.2

STEM and code

Benchmark	Metric	Gemma 3 PT 4B	Gemma 3 PT 12B	Gemma 3 PT 27B
MMLU	5-shot	59.6	74.5	78.6
MMLU (Pro COT)	5-shot	29.2	45.3	52.2
AGIEval	3-5-shot	42.1	57.4	66.2
MATH	4-shot	24.2	43.3	50.0
GSM8K	8-shot	38.4	71.0	82.6
GPQA	5-shot	15.0	25.4	24.3
MBPP	3-shot	46.0	60.4	65.6
HumanEval	0-shot	36.0	45.7	48.8

Multilingual

Benchmark	Gemma 3 PT 1B	Gemma 3 PT 4B	Gemma 3 PT 12B	Gemma 3 PT 27B
MGSM	2.04	34.7	64.3	74.3
Global-MMLU-Lite	24.9	57.0	69.4	75.7
WMT24++ (ChrF)	36.7	48.4	53.9	55.7
FloRes	29.5	39.2	46.0	48.8
XQuAD (all)	43.9	68.0	74.5	76.8
ECLeKTic	4.69	11.0	17.2	24.4
IndicGenBench	41.4	57.2	61.7	63.4