Instructions to use tanujrai/gemma-3-1b-it-Q4_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use tanujrai/gemma-3-1b-it-Q4_0 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 tanujrai/gemma-3-1b-it-Q4_0:Q4_0
# Run inference directly in the terminal:
llama cli -hf tanujrai/gemma-3-1b-it-Q4_0:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tanujrai/gemma-3-1b-it-Q4_0:Q4_0
# Run inference directly in the terminal:
llama cli -hf tanujrai/gemma-3-1b-it-Q4_0:Q4_0

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 tanujrai/gemma-3-1b-it-Q4_0:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf tanujrai/gemma-3-1b-it-Q4_0:Q4_0

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 tanujrai/gemma-3-1b-it-Q4_0:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tanujrai/gemma-3-1b-it-Q4_0:Q4_0

Use Docker

docker model run hf.co/tanujrai/gemma-3-1b-it-Q4_0:Q4_0

LM Studio
Jan

vLLM

How to use tanujrai/gemma-3-1b-it-Q4_0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tanujrai/gemma-3-1b-it-Q4_0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tanujrai/gemma-3-1b-it-Q4_0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tanujrai/gemma-3-1b-it-Q4_0:Q4_0

Ollama
How to use tanujrai/gemma-3-1b-it-Q4_0 with Ollama:
```
ollama run hf.co/tanujrai/gemma-3-1b-it-Q4_0:Q4_0
```
Unsloth Desktop
Docker Model Runner
How to use tanujrai/gemma-3-1b-it-Q4_0 with Docker Model Runner:
```
docker model run hf.co/tanujrai/gemma-3-1b-it-Q4_0:Q4_0
```

Lemonade

How to use tanujrai/gemma-3-1b-it-Q4_0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tanujrai/gemma-3-1b-it-Q4_0:Q4_0

Run and chat with the model

lemonade run user.gemma-3-1b-it-Q4_0-Q4_0

List all available models

lemonade list

Atomic Chat

Gemma-3-1b-it Q4_0 Quantized Model

This is a Q4_0 quantized version of the google/gemma-3-1b-it model, converted to GGUF format and optimized for efficient inference. It was created using llama.cpp tools in Google Colab.

Model Details

Base Model: google/gemma-3-1b-it
Quantization: Q4_0 (4-bit quantization)
Format: GGUF
Size: ~1–1.5 GB
Converted Using: llama.cpp (commit from April 2025)
License: Inherits the license from google/gemma-3-1b-it

Usage

To use this model with llama.cpp:

./llama-cli -m gemma-3-1b-it-Q4_0.gguf --prompt "Hello, world!" --no-interactive

How It Was Created

Downloaded google/gemma-3-1b-it from Hugging Face.
Converted to GGUF using convert_hf_to_gguf.py.
Quantized to Q4_0 using llama-quantize from llama.cpp.
Tested in Google Colab with llama-cli.

Limitations

Quantization may reduce accuracy compared to the original model.
Requires llama.cpp or compatible software for inference.

Acknowledgments

Based on the work of bartowski for GGUF quantization.
Uses llama.cpp by Georgi Gerganov.

Downloads last month: 11

GGUF

Model size

1.0B params

Architecture

gemma3

Hardware compatibility

4-bit

Model tree for tanujrai/gemma-3-1b-it-Q4_0

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Finetuned

google/gemma-3-1b-it

Quantized

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