Image-Text-to-Text
ExecuTorch
Safetensors
GGUF
English
Korean
gemma4
android
qnn
speculative-decoding
dflash2
quantized
imatrix
conversational
Instructions to use RoundRobin237/Gemma4-E2B-Dflash2-Android with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use RoundRobin237/Gemma4-E2B-Dflash2-Android 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 RoundRobin237/Gemma4-E2B-Dflash2-Android:Q4_K_M # Run inference directly in the terminal: llama cli -hf RoundRobin237/Gemma4-E2B-Dflash2-Android:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf RoundRobin237/Gemma4-E2B-Dflash2-Android:Q4_K_M # Run inference directly in the terminal: llama cli -hf RoundRobin237/Gemma4-E2B-Dflash2-Android: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 RoundRobin237/Gemma4-E2B-Dflash2-Android:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf RoundRobin237/Gemma4-E2B-Dflash2-Android: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 RoundRobin237/Gemma4-E2B-Dflash2-Android:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf RoundRobin237/Gemma4-E2B-Dflash2-Android:Q4_K_M
Use Docker
docker model run hf.co/RoundRobin237/Gemma4-E2B-Dflash2-Android:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use RoundRobin237/Gemma4-E2B-Dflash2-Android with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RoundRobin237/Gemma4-E2B-Dflash2-Android" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RoundRobin237/Gemma4-E2B-Dflash2-Android", "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/RoundRobin237/Gemma4-E2B-Dflash2-Android:Q4_K_M
- Ollama
How to use RoundRobin237/Gemma4-E2B-Dflash2-Android with Ollama:
ollama run hf.co/RoundRobin237/Gemma4-E2B-Dflash2-Android:Q4_K_M
- Unsloth Desktop
- Pi
How to use RoundRobin237/Gemma4-E2B-Dflash2-Android with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf RoundRobin237/Gemma4-E2B-Dflash2-Android:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "RoundRobin237/Gemma4-E2B-Dflash2-Android:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use RoundRobin237/Gemma4-E2B-Dflash2-Android with Docker Model Runner:
docker model run hf.co/RoundRobin237/Gemma4-E2B-Dflash2-Android:Q4_K_M
- Lemonade
How to use RoundRobin237/Gemma4-E2B-Dflash2-Android with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RoundRobin237/Gemma4-E2B-Dflash2-Android:Q4_K_M
Run and chat with the model
lemonade run user.Gemma4-E2B-Dflash2-Android-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use RoundRobin237/Gemma4-E2B-Dflash2-Android with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf RoundRobin237/Gemma4-E2B-Dflash2-Android:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default RoundRobin237/Gemma4-E2B-Dflash2-Android:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use RoundRobin237/Gemma4-E2B-Dflash2-Android with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf RoundRobin237/Gemma4-E2B-Dflash2-Android:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "RoundRobin237/Gemma4-E2B-Dflash2-Android:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Download device.json from RoundRobin237/Gemma4-E2B-Dflash2-Android: direct link, hf CLI and curl.
- Browser
- Download file 1.17 kB
-
https://huggingface.co/RoundRobin237/Gemma4-E2B-Dflash2-Android/resolve/main/device.json
- Command line
-
hf download hf://RoundRobin237/Gemma4-E2B-Dflash2-Android/device.json
-
curl -L -o device.json https://huggingface.co/RoundRobin237/Gemma4-E2B-Dflash2-Android/resolve/main/device.json
1.17 kB
| { | |
| "device": "Samsung Galaxy S22 Ultra", | |
| "model": "SM-S908N", | |
| "soc": "Qualcomm Snapdragon 8 Gen 1 / SM8450", | |
| "ram_marketed_gb": 12, | |
| "android_version": "16", | |
| "android_api": 36, | |
| "page_size_bytes": 4096, | |
| "cpu": [ | |
| { | |
| "cores": [ | |
| 0, | |
| 1, | |
| 2, | |
| 3 | |
| ], | |
| "part": "0xd46", | |
| "architecture": "Arm Cortex-A510", | |
| "observed_cpuinfo_max_freq_khz": 1785600 | |
| }, | |
| { | |
| "cores": [ | |
| 4, | |
| 5, | |
| 6 | |
| ], | |
| "part": "0xd47", | |
| "architecture": "Arm Cortex-A710", | |
| "observed_cpuinfo_max_freq_khz": 2496000 | |
| }, | |
| { | |
| "cores": [ | |
| 7 | |
| ], | |
| "part": "0xd48", | |
| "architecture": "Arm Cortex-X2", | |
| "observed_cpuinfo_max_freq_khz": 2995200 | |
| } | |
| ], | |
| "gpu": "Qualcomm Adreno 730", | |
| "gpu_used_in_selected_baseline": false, | |
| "npu": "Qualcomm Hexagon HTP v69", | |
| "cpu_information_source": "Read-only /proc/cpuinfo and cpuinfo_max_freq checks on the same device; max-frequency values are not measured benchmark clocks", | |
| "identity_and_clock_recheck_date": "2026-10-15", | |
| "device_serial_published": false | |
| } | |