Image-Text-to-Text
Transformers
GGUF
automatic-speech-recognition
automatic-speech-translation
audio-text-to-text
video-text-to-text
llama-cpp
gguf-my-repo
imatrix
Instructions to use mrmage/gemma-3n-E4B-IQ4_NL-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrmage/gemma-3n-E4B-IQ4_NL-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="mrmage/gemma-3n-E4B-IQ4_NL-GGUF")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mrmage/gemma-3n-E4B-IQ4_NL-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mrmage/gemma-3n-E4B-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 mrmage/gemma-3n-E4B-IQ4_NL-GGUF:IQ4_NL # Run inference directly in the terminal: llama cli -hf mrmage/gemma-3n-E4B-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 mrmage/gemma-3n-E4B-IQ4_NL-GGUF:IQ4_NL # Run inference directly in the terminal: llama cli -hf mrmage/gemma-3n-E4B-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 mrmage/gemma-3n-E4B-IQ4_NL-GGUF:IQ4_NL # Run inference directly in the terminal: ./llama-cli -hf mrmage/gemma-3n-E4B-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 mrmage/gemma-3n-E4B-IQ4_NL-GGUF:IQ4_NL # Run inference directly in the terminal: ./build/bin/llama-cli -hf mrmage/gemma-3n-E4B-IQ4_NL-GGUF:IQ4_NL
Use Docker
docker model run hf.co/mrmage/gemma-3n-E4B-IQ4_NL-GGUF:IQ4_NL
- LM Studio
- Jan
- vLLM
How to use mrmage/gemma-3n-E4B-IQ4_NL-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mrmage/gemma-3n-E4B-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": "mrmage/gemma-3n-E4B-IQ4_NL-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mrmage/gemma-3n-E4B-IQ4_NL-GGUF:IQ4_NL
- SGLang
How to use mrmage/gemma-3n-E4B-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 "mrmage/gemma-3n-E4B-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": "mrmage/gemma-3n-E4B-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 "mrmage/gemma-3n-E4B-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": "mrmage/gemma-3n-E4B-IQ4_NL-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use mrmage/gemma-3n-E4B-IQ4_NL-GGUF with Ollama:
ollama run hf.co/mrmage/gemma-3n-E4B-IQ4_NL-GGUF:IQ4_NL
- Unsloth Desktop
- Docker Model Runner
How to use mrmage/gemma-3n-E4B-IQ4_NL-GGUF with Docker Model Runner:
docker model run hf.co/mrmage/gemma-3n-E4B-IQ4_NL-GGUF:IQ4_NL
- Lemonade
How to use mrmage/gemma-3n-E4B-IQ4_NL-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mrmage/gemma-3n-E4B-IQ4_NL-GGUF:IQ4_NL
Run and chat with the model
lemonade run user.gemma-3n-E4B-IQ4_NL-GGUF-IQ4_NL
List all available models
lemonade list
- Atomic Chat
File size: 2,193 Bytes
e13e9fc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | ---
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
tags:
- automatic-speech-recognition
- automatic-speech-translation
- audio-text-to-text
- video-text-to-text
- llama-cpp
- gguf-my-repo
base_model: google/gemma-3n-E4B
---
# mrmage/gemma-3n-E4B-IQ4_NL-GGUF
This model was converted to GGUF format from [`google/gemma-3n-E4B`](https://huggingface.co/google/gemma-3n-E4B) 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-E4B) 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 mrmage/gemma-3n-E4B-IQ4_NL-GGUF --hf-file gemma-3n-e4b-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo mrmage/gemma-3n-E4B-IQ4_NL-GGUF --hf-file gemma-3n-e4b-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 mrmage/gemma-3n-E4B-IQ4_NL-GGUF --hf-file gemma-3n-e4b-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo mrmage/gemma-3n-E4B-IQ4_NL-GGUF --hf-file gemma-3n-e4b-iq4_nl-imat.gguf -c 2048
```
|