Instructions to use marcelone/Gemma-3-Gaia-PT-BR-4b-it-gguf 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 marcelone/Gemma-3-Gaia-PT-BR-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 marcelone/Gemma-3-Gaia-PT-BR-4b-it-gguf:BF16 # Run inference directly in the terminal: llama cli -hf marcelone/Gemma-3-Gaia-PT-BR-4b-it-gguf:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf marcelone/Gemma-3-Gaia-PT-BR-4b-it-gguf:BF16 # Run inference directly in the terminal: llama cli -hf marcelone/Gemma-3-Gaia-PT-BR-4b-it-gguf:BF16
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 marcelone/Gemma-3-Gaia-PT-BR-4b-it-gguf:BF16 # Run inference directly in the terminal: ./llama-cli -hf marcelone/Gemma-3-Gaia-PT-BR-4b-it-gguf:BF16
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 marcelone/Gemma-3-Gaia-PT-BR-4b-it-gguf:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf marcelone/Gemma-3-Gaia-PT-BR-4b-it-gguf:BF16
Use Docker
docker model run hf.co/marcelone/Gemma-3-Gaia-PT-BR-4b-it-gguf:BF16
- LM Studio
- Jan
- Ollama
How to use marcelone/Gemma-3-Gaia-PT-BR-4b-it-gguf with Ollama:
ollama run hf.co/marcelone/Gemma-3-Gaia-PT-BR-4b-it-gguf:BF16
- Unsloth Desktop
- Docker Model Runner
How to use marcelone/Gemma-3-Gaia-PT-BR-4b-it-gguf with Docker Model Runner:
docker model run hf.co/marcelone/Gemma-3-Gaia-PT-BR-4b-it-gguf:BF16
- Lemonade
How to use marcelone/Gemma-3-Gaia-PT-BR-4b-it-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull marcelone/Gemma-3-Gaia-PT-BR-4b-it-gguf:BF16
Run and chat with the model
lemonade run user.Gemma-3-Gaia-PT-BR-4b-it-gguf-BF16
List all available models
lemonade list
- Atomic Chat
This model is based on the Gemma3 architecture (4 billion parameters) and has been quantized using a mixed-precision approach to balance memory efficiency and inference speed while preserving accuracy.
Quantization Types Used:
Q8_0-MiXED Full precision (float32): 205 tensors 8-bit quantization (q8_0): 47 tensors 3-bit quantization (q3_K): 48 tensors 4-bit quantization (q4_K): 47 tensors 5-bit quantization (q5_K): 49 tensors 6-bit quantization (q6_K): 48 tensors Model Size: Approximately 2.33 GiB Average Bits Per Weight (BPW): 5.16
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Model tree for marcelone/Gemma-3-Gaia-PT-BR-4b-it-gguf
Base model
google/gemma-3-4b-pt