Instructions to use matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF", dtype="auto")

llama-cpp-python

How to use matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF",
	filename="gemma-3n-e2b-it-q3_k_m.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF:Q3_K_M
# Run inference directly in the terminal:
llama-cli -hf matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF:Q3_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF:Q3_K_M
# Run inference directly in the terminal:
llama-cli -hf matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF:Q3_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 matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF:Q3_K_M
# Run inference directly in the terminal:
./llama-cli -hf matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF:Q3_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 matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF:Q3_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF:Q3_K_M

Use Docker

docker model run hf.co/matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF:Q3_K_M

LM Studio
Jan

vLLM

How to use matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF:Q3_K_M

SGLang

How to use matrixportalx/gemma-3n-E2B-it-Q3_K_M-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 "matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF with Ollama:
```
ollama run hf.co/matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF:Q3_K_M
```

Unsloth Studio

How to use matrixportalx/gemma-3n-E2B-it-Q3_K_M-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 matrixportalx/gemma-3n-E2B-it-Q3_K_M-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 matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF:Q3_K_M
```

Lemonade

How to use matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF:Q3_K_M

Run and chat with the model

lemonade run user.gemma-3n-E2B-it-Q3_K_M-GGUF-Q3_K_M

List all available models

lemonade list

gemma-3n-E2B-it-Q3_K_M-GGUF

File size: 2,247 Bytes

5e8fbe7

---
license: gemma
library_name: transformers
pipeline_tag: image-text-to-text
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
  agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
  Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: google/gemma-3n-E2B-it
tags:
- automatic-speech-recognition
- automatic-speech-translation
- audio-text-to-text
- video-text-to-text
- llama-cpp
- gguf-my-repo
---

# matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF
This model was converted to GGUF format from [`google/gemma-3n-E2B-it`](https://huggingface.co/google/gemma-3n-E2B-it) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/google/gemma-3n-E2B-it) for more details on the model.

## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF --hf-file gemma-3n-e2b-it-q3_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF --hf-file gemma-3n-e2b-it-q3_k_m.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF --hf-file gemma-3n-e2b-it-q3_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo matrixportalx/gemma-3n-E2B-it-Q3_K_M-GGUF --hf-file gemma-3n-e2b-it-q3_k_m.gguf -c 2048
```