| --- |
| license: apache-2.0 |
| language: |
| - en |
| pipeline_tag: image-text-to-text |
| tags: |
| - gemma4 |
| - gguf |
| - llama-cpp |
| - offline |
| - multimodal |
| - vision |
| base_model: |
| - google/gemma-4-e2b-it |
| --- |
| |
| # Gemma4 LLM |
|
|
| **Run Google's Gemma 4 locally with vision.** A fully offline AI chat app powered by Gemma 4 E2B — no cloud, no API keys, no accounts. |
|
|
| ## Downloads |
|
|
| | Platform | File | Size | |
| |----------|------|------| |
| | **Windows** | [Gemma4-LLM.zip](https://huggingface.co/alphastack1/gemma4-llm/resolve/main/Gemma4-LLM.zip) | 5.05 GB | |
| | **Android** | [gemma4-llm.apk](https://huggingface.co/alphastack1/gemma4-llm/resolve/main/gemma4-llm.apk) | 4.1 GB | |
|
|
| Both bundles include the model, inference engine, and chat UI. Nothing else to install. |
|
|
| ## About |
|
|
| Gemma4 LLM bundles everything needed to run Google's **Gemma 4 E2B** model on your device — the model weights, inference engine, and chat UI are all included. |
|
|
| Gemma 4 E2B is **natively multimodal** — it understands both text and images out of the box. This isn't a bolt-on vision module; image understanding is built into the model architecture. |
|
|
| - **Natively multimodal**: Text and image understanding built into the model |
| - **Fully offline**: No internet connection required after install |
| - **GPU accelerated**: Uses CUDA on Windows, CPU on Android (arm64) |
| - **Stock llama.cpp**: Built on official [ggml-org/llama.cpp](https://github.com/ggml-org/llama.cpp) release b8683 |
|
|
| ## Model Details |
|
|
| | | | |
| |---|---| |
| | **Base model** | [google/gemma-4-e2b-it](https://huggingface.co/google/gemma-4-e2b-it) | |
| | **Architecture** | Mixture-of-Experts (5.1B total, 2.3B active) | |
| | **Quantization** | Q4_K_M (GGUF) via [unsloth](https://huggingface.co/unsloth/gemma-4-E2B-it-GGUF) | |
| | **Model file** | gemma-4-E2B-it-Q4_K_M.gguf (2.96 GB) | |
| | **Image encoder** | mmproj-F16.gguf (940 MB) — part of Gemma 4's native vision architecture | |
| | **License** | Apache 2.0 | |
|
|
| ## Windows |
|
|
| 1. Download and extract `Gemma4-LLM.zip` |
| 2. Double-click `Gemma4-LLM.exe` — a native window opens with the chat UI |
| 3. The model loads automatically on startup |
|
|
| The zip contains the EXE and a `models/` folder side by side. Bundles CUDA runtime for GPU acceleration — falls back to CPU if no GPU is available. |
|
|
| ## Android |
|
|
| 1. Download `gemma4-llm.apk` |
| 2. Enable "Install from unknown sources" in Settings |
| 3. Install and open — first launch extracts the model (~3 min) |
|
|
| Requirements: |
| - Android 9+ with arm64 (64-bit) processor |
| - 8+ GB RAM recommended |
| - Runs as a foreground service with status notification |
| - Model is bundled inside the APK (split into chunks, reassembled on first launch) |
|
|
| ## Technical Details |
|
|
| - **Inference**: llama.cpp server running locally on port 8080 |
| - **Context length**: 4096 tokens (mobile), 8192 tokens (desktop) |
| - **KV cache**: q8_0 quantized for reduced memory usage |
| - **Android**: 4 threads on big cores, WebView UI |
| - **Windows**: PyWebView (Edge/Chromium), Flask backend |
| |
| ## Credits |
| |
| - [Google](https://ai.google.dev/gemma) for the Gemma 4 model family |
| - [ggml-org/llama.cpp](https://github.com/ggml-org/llama.cpp) for the inference engine |
| - [unsloth](https://huggingface.co/unsloth) for GGUF quantizations |
| |
