Instructions to use unsloth/gemma-3n-E4B-it-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unsloth/gemma-3n-E4B-it-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="unsloth/gemma-3n-E4B-it-GGUF") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("unsloth/gemma-3n-E4B-it-GGUF", dtype="auto") - llama-cpp-python
How to use unsloth/gemma-3n-E4B-it-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="unsloth/gemma-3n-E4B-it-GGUF", filename="gemma-3n-E4B-it-F16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use unsloth/gemma-3n-E4B-it-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf unsloth/gemma-3n-E4B-it-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama-cli -hf unsloth/gemma-3n-E4B-it-GGUF:UD-Q4_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf unsloth/gemma-3n-E4B-it-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama-cli -hf unsloth/gemma-3n-E4B-it-GGUF:UD-Q4_K_XL
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 unsloth/gemma-3n-E4B-it-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./llama-cli -hf unsloth/gemma-3n-E4B-it-GGUF:UD-Q4_K_XL
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 unsloth/gemma-3n-E4B-it-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf unsloth/gemma-3n-E4B-it-GGUF:UD-Q4_K_XL
Use Docker
docker model run hf.co/unsloth/gemma-3n-E4B-it-GGUF:UD-Q4_K_XL
- LM Studio
- Jan
- vLLM
How to use unsloth/gemma-3n-E4B-it-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "unsloth/gemma-3n-E4B-it-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "unsloth/gemma-3n-E4B-it-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/unsloth/gemma-3n-E4B-it-GGUF:UD-Q4_K_XL
- SGLang
How to use unsloth/gemma-3n-E4B-it-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "unsloth/gemma-3n-E4B-it-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "unsloth/gemma-3n-E4B-it-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "unsloth/gemma-3n-E4B-it-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "unsloth/gemma-3n-E4B-it-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Ollama
How to use unsloth/gemma-3n-E4B-it-GGUF with Ollama:
ollama run hf.co/unsloth/gemma-3n-E4B-it-GGUF:UD-Q4_K_XL
- Unsloth Studio new
How to use unsloth/gemma-3n-E4B-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 unsloth/gemma-3n-E4B-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 unsloth/gemma-3n-E4B-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 unsloth/gemma-3n-E4B-it-GGUF to start chatting
- Docker Model Runner
How to use unsloth/gemma-3n-E4B-it-GGUF with Docker Model Runner:
docker model run hf.co/unsloth/gemma-3n-E4B-it-GGUF:UD-Q4_K_XL
- Lemonade
How to use unsloth/gemma-3n-E4B-it-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull unsloth/gemma-3n-E4B-it-GGUF:UD-Q4_K_XL
Run and chat with the model
lemonade run user.gemma-3n-E4B-it-GGUF-UD-Q4_K_XL
List all available models
lemonade list
Multimodal support
I understand Gemini was built to be natively multimodal. Could you elaborate on the current capabilities, especially regarding real-time processing of combined audio and video inputs? Furthermore, what does the development roadmap look like for expanding these core multimodal features?
Currently this GGUF only supports text. We wrote it in the description. Hopefully llama.cpp will be able to support all forms soon
Ok, now I see that it is a llama.cpp restriction, not specifically this quant. Thanks!
llama.cpp now has support for multi-modal vision models. Your Gemma 3 (not 3n here) variants already support it link, so I wanted to check if any plans on supporting it here?
Lastly, what about also supporting ALL modalities? The original gemma 3n support text, audio, and vision (image and video) inputs.