Instructions to use PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use PixelLadies45/gemma-3n-E2B-IQ4_NL-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 PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF:IQ4_NL
# Run inference directly in the terminal:
llama cli -hf PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF:IQ4_NL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF:IQ4_NL
# Run inference directly in the terminal:
llama cli -hf PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF:IQ4_NL

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 PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF:IQ4_NL
# Run inference directly in the terminal:
./llama-cli -hf PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF:IQ4_NL

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 PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF:IQ4_NL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF:IQ4_NL

Use Docker

docker model run hf.co/PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF:IQ4_NL

LM Studio
Jan

vLLM

How to use PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF:IQ4_NL

SGLang

How to use PixelLadies45/gemma-3n-E2B-IQ4_NL-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 "PixelLadies45/gemma-3n-E2B-IQ4_NL-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": "PixelLadies45/gemma-3n-E2B-IQ4_NL-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 "PixelLadies45/gemma-3n-E2B-IQ4_NL-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": "PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF with Ollama:
```
ollama run hf.co/PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF:IQ4_NL
```
Unsloth Desktop
Docker Model Runner
How to use PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF with Docker Model Runner:
```
docker model run hf.co/PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF:IQ4_NL
```

Lemonade

How to use PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF:IQ4_NL

Run and chat with the model

lemonade run user.gemma-3n-E2B-IQ4_NL-GGUF-IQ4_NL

List all available models

lemonade list

Atomic Chat

gemma-3n-E2B-IQ4_NL-GGUF / README.md

PixelLadies45

Update README.md

2d5654a verified about 1 year ago

preview code

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	---
	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
	tags:
	- automatic-speech-recognition
	- automatic-speech-translation
	- audio-text-to-text
	- video-text-to-text
	- llama-cpp
	- gguf-my-repo
	---

	# PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF
	THIS MODEL IS COMPLETELY UNTRAINED AND VERY UNSTABLE. I reccomend the IT models and quants instead. This model was converted to GGUF format from [`google/gemma-3n-E2B`](https://huggingface.co/google/gemma-3n-E2B) 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) 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 PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF --hf-file gemma-3n-e2b-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
	```

	### Server:
	```bash
	llama-server --hf-repo PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF --hf-file gemma-3n-e2b-iq4_nl-imat.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 PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF --hf-file gemma-3n-e2b-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
	```
	or
	```
	./llama-server --hf-repo PixelLadies45/gemma-3n-E2B-IQ4_NL-GGUF --hf-file gemma-3n-e2b-iq4_nl-imat.gguf -c 2048
	```