Instructions to use cortexso/gemma3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use cortexso/gemma3 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 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 serve -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
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
- Atomic Chat new
- 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
| pipeline_tag: text-generation | |
| tags: | |
| - cortex.cpp | |
| - featured | |
| ## 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](https://huggingface.co/cortexso/gemma3/tree/1b) | 1b | `cortex run gemma3:1b` | | |
| | 2 | [Gemma-3-4B](https://huggingface.co/cortexso/gemma3/tree/4b) | 4b | `cortex run gemma3:4b` | | |
| | 3 | [Gemma-3-12B](https://huggingface.co/cortexso/gemma3/tree/12b) | 12b | `cortex run gemma3:12b` | | |
| | 4 | [Gemma-3-27B](https://huggingface.co/cortexso/gemma3/tree/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) | |
| 1. Install **Jan** using [Quickstart](https://jan.ai/docs/quickstart) | |
| 2. Use in Jan model Hub: | |
| ```bash | |
| cortexso/gemma3 | |
| ``` | |
| ## Use it with Cortex (CLI) | |
| 1. Install **Cortex** using [Quickstart](https://cortex.jan.ai/docs/quickstart) | |
| 2. Run the model with command: | |
| ```bash | |
| cortex run gemma3 | |
| ``` | |
| ## Credits | |
| - **Author:** Google | |
| - **Original License:** [Gemma License](https://ai.google.dev/gemma/terms) | |
| - **Papers:** [Gemma 3 Technical Report](https://storage.googleapis.com/deepmind-media/gemma/Gemma3Report.pdf) |