Instructions to use cortexso/gemma3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use cortexso/gemma3 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="cortexso/gemma3", filename="gemma-3-12b-it-q2_k.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 cortexso/gemma3 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf cortexso/gemma3:Q4_K_M # Run inference directly in the terminal: llama-cli -hf cortexso/gemma3:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf cortexso/gemma3:Q4_K_M # Run inference directly in the terminal: llama-cli -hf cortexso/gemma3: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 cortexso/gemma3:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf cortexso/gemma3: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 cortexso/gemma3:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf cortexso/gemma3:Q4_K_M
Use Docker
docker model run hf.co/cortexso/gemma3:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use cortexso/gemma3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cortexso/gemma3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cortexso/gemma3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cortexso/gemma3:Q4_K_M
- Ollama
How to use cortexso/gemma3 with Ollama:
ollama run hf.co/cortexso/gemma3:Q4_K_M
- Unsloth Studio new
How to use cortexso/gemma3 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 cortexso/gemma3 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 cortexso/gemma3 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for cortexso/gemma3 to start chatting
- Docker Model Runner
How to use cortexso/gemma3 with Docker Model Runner:
docker model run hf.co/cortexso/gemma3:Q4_K_M
- Lemonade
How to use cortexso/gemma3 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cortexso/gemma3:Q4_K_M
Run and chat with the model
lemonade run user.gemma3-Q4_K_M
List all available models
lemonade list
Overview
Google developed and released the Gemma 3 series, featuring multiple model sizes with both pre-trained and instruction-tuned variants. These multimodal models handle both text and image inputs while generating text outputs, making them versatile for various applications. Gemma 3 models are built from the same research and technology used to create the Gemini models, offering state-of-the-art capabilities in a lightweight and accessible format.
The Gemma 3 models include four different sizes with open weights, providing excellent performance across tasks like question answering, summarization, and reasoning while maintaining efficiency for deployment in resource-constrained environments such as laptops, desktops, or custom cloud infrastructure.
Variants
Gemma 3
| No | Variant | Branch | Cortex CLI command |
|---|---|---|---|
| 1 | Gemma-3-1B | 1b | cortex run gemma3:1b |
| 2 | Gemma-3-4B | 4b | cortex run gemma3:4b |
| 3 | Gemma-3-12B | 12b | cortex run gemma3:12b |
| 4 | Gemma-3-27B | 27b | cortex run gemma3:27b |
Each branch contains a default quantized version.
Key Features
- Multimodal capabilities: Handles both text and image inputs
- Large context window: 128K tokens
- Multilingual support: Over 140 languages
- Available in multiple sizes: From 1B to 27B parameters
- Open weights: For both pre-trained and instruction-tuned variants
Use it with Jan (UI)
- Install Jan using Quickstart
- Use in Jan model Hub:
cortexso/gemma3
Use it with Cortex (CLI)
- Install Cortex using Quickstart
- Run the model with command:
cortex run gemma3
Credits
- Author: Google
- Original License: Gemma License
- Papers: Gemma 3 Technical Report
- Downloads last month
- 607
2-bit
3-bit
4-bit
5-bit
6-bit
8-bit