Instructions to use Cenedril/Megga-4-E2B-it-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Cenedril/Megga-4-E2B-it-Q4_K_M-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="Cenedril/Megga-4-E2B-it-Q4_K_M-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Cenedril/Megga-4-E2B-it-Q4_K_M-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Cenedril/Megga-4-E2B-it-Q4_K_M-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 Cenedril/Megga-4-E2B-it-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Cenedril/Megga-4-E2B-it-Q4_K_M-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Cenedril/Megga-4-E2B-it-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Cenedril/Megga-4-E2B-it-Q4_K_M-GGUF:Q4_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 Cenedril/Megga-4-E2B-it-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Cenedril/Megga-4-E2B-it-Q4_K_M-GGUF:Q4_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 Cenedril/Megga-4-E2B-it-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Cenedril/Megga-4-E2B-it-Q4_K_M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Cenedril/Megga-4-E2B-it-Q4_K_M-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Cenedril/Megga-4-E2B-it-Q4_K_M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Cenedril/Megga-4-E2B-it-Q4_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": "Cenedril/Megga-4-E2B-it-Q4_K_M-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Cenedril/Megga-4-E2B-it-Q4_K_M-GGUF:Q4_K_M

SGLang

How to use Cenedril/Megga-4-E2B-it-Q4_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 "Cenedril/Megga-4-E2B-it-Q4_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": "Cenedril/Megga-4-E2B-it-Q4_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 "Cenedril/Megga-4-E2B-it-Q4_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": "Cenedril/Megga-4-E2B-it-Q4_K_M-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use Cenedril/Megga-4-E2B-it-Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/Cenedril/Megga-4-E2B-it-Q4_K_M-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use Cenedril/Megga-4-E2B-it-Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/Cenedril/Megga-4-E2B-it-Q4_K_M-GGUF:Q4_K_M
```

Lemonade

How to use Cenedril/Megga-4-E2B-it-Q4_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Cenedril/Megga-4-E2B-it-Q4_K_M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Megga-4-E2B-it-Q4_K_M-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Megga-4-E2B-it-Q4_K_M-GGUF

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---
library_name: transformers
license: apache-2.0
license_link: https://ai.google.dev/gemma/docs/gemma_4_license
pipeline_tag: image-text-to-text
base_model: google/gemma-4-E2B-it
tags:
- llama-cpp
- I-Matrix
---
 
# Cenedril/Megga-4-E2B-it-Q4_K_M-GGUF

![x2FVAmpk](https://cdn-uploads.huggingface.co/production/uploads/6593502ca2607099284523db/LKd73_E6ZkBpNIjMoQM3m.jpeg)


This model was converted to GGUF format from late May iteration [`google/gemma-4-E2B-it`](https://huggingface.co/google/gemma-4-E2B-it) using a custom agentic importance matrix.

## 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.