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 quantization.json from RoundRobin237/Gemma4-E2B-Dflash2-Android: direct link, hf CLI and curl.
- Browser
- Download file 2.03 kB
-
https://huggingface.co/RoundRobin237/Gemma4-E2B-Dflash2-Android/resolve/main/quantization.json
- Command line
-
hf download hf://RoundRobin237/Gemma4-E2B-Dflash2-Android/quantization.json
-
curl -L -o quantization.json https://huggingface.co/RoundRobin237/Gemma4-E2B-Dflash2-Android/resolve/main/quantization.json
2.03 kB
| { | |
| "base_model": "google/gemma-4-E2B-it", | |
| "base_revision": "3e22461f65e89153144f8adb70e3b8c2cc9845a7", | |
| "engine_git": "https://github.com/pytorch/executorch", | |
| "engine_revision": "ca56e1ffdba1c902d9202021d7297982d3501439", | |
| "qnn_sdk": "2.37.0.250724", | |
| "target_soc": "SM8450", | |
| "hexagon_arch": "v69", | |
| "context": 2048, | |
| "quantization": "mixed W4 block32 FFN gate/up/down; all other decoder projections W8 per-channel; final 8 cross-decoder FFN down projections restored to W8; A16; KV16; A16 x W8 attention boundary; Q4 group32 portable embeddings", | |
| "weight_bits": 4, | |
| "weight_granularity": "block", | |
| "block_size": 32, | |
| "w4_ffn_only": true, | |
| "w4_ffn_down": true, | |
| "w8_ffn_down_tail": 8, | |
| "compiled_shards": 11, | |
| "qnn_delegate_count": 12, | |
| "largest_delegate_bytes": 504553728, | |
| "source_calibration_context": 2048, | |
| "position_table_audit": true, | |
| "model_name": "Gemma4-E2B-Dflash2-Android", | |
| "original_baseline_pte_sha256": "38faf5d2337d9fb2b75b2ddca0fb3c63d916b2795c55bff6a1b6b92c027e8c7b", | |
| "original_baseline_pte_bytes": 3361616512, | |
| "original_baseline_pte_name": "gemma4-e2b-v69-mixed4b32-ffn-all-tail8w8-kv16-quality-2k-shard11.pte", | |
| "dflash2_target": { | |
| "name": "gemma4-e2b-dflash2-verify-2k.pte", | |
| "bytes": 3361310336, | |
| "sha256": "c05fe24cb87fe20f3c2fdf3bb942755486f4c34785ad4d28a5b5546a7378983b", | |
| "features": [ | |
| 3, | |
| 13, | |
| 23, | |
| 33 | |
| ], | |
| "verification_width": 4, | |
| "target_weights_changed_for_feature_export": false, | |
| "context_loading_order": "Head context loaded first; execution order unchanged" | |
| }, | |
| "vision_target": { | |
| "name": "gemma4-e2b-dflash2-vision-verify-2k.pte", | |
| "sha256": "6475a64ba4c82a85dd635bde40cc51ece8effe47366565a1c6e58d48862cdbba", | |
| "source_sha256": "c05fe24cb87fe20f3c2fdf3bb942755486f4c34785ad4d28a5b5546a7378983b", | |
| "weights_and_QNN_contexts_unchanged": true, | |
| "modification": "Portable embedding operator descriptor alias only", | |
| "device_validated": true | |
| } | |
| } | |