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

Libraries

How to use agentlans/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="agentlans/gemma-3-4b-it-GGUF",
	filename="Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use agentlans/gemma-3-4b-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 agentlans/gemma-3-4b-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf agentlans/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 serve -hf agentlans/gemma-3-4b-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf agentlans/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 agentlans/gemma-3-4b-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf agentlans/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 agentlans/gemma-3-4b-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf agentlans/gemma-3-4b-it-GGUF:Q4_K_M

Use Docker

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

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

Unsloth Studio

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

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

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull agentlans/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 Instruct Quantized Models

This repo offers quantized versions of google/gemma-3-4b-it for use with llama.cpp. Quantization was done using an unofficial Docker image and calibrated on 100 rows from the agentlans/LinguaNova dataset to maintain coherence and multilingual support. The importance matrix file is included.

Limitations

Optimized for multilingual natural language tasks.
May underperform on math, coding, and untested multimodal features.
Shares all limitations and biases of the original Gemma 3 models.

Notes

Ideal for resource-constrained environments.
Test on your data for best results.
See the original google/gemma-3-4b-it page for full details and guidelines.

This card covers only the quantized models.

Downloads last month: 6

GGUF

Model size

4B params

Architecture

gemma3

Hardware compatibility

4-bit

5-bit

8-bit

Inference Providers NEW

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Model tree for agentlans/gemma-3-4b-it-GGUF

Base model

google/gemma-3-4b-pt

Finetuned

google/gemma-3-4b-it

Quantized

(468)

this model

agentlans
/

gemma-3-4b-it-GGUF

Gemma 3 4B Instruct Quantized Models

Limitations

Notes

Model tree for agentlans/gemma-3-4b-it-GGUF

Dataset used to train agentlans/gemma-3-4b-it-GGUF