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• LoRA adapters: apply LoRA GGUF at runtime; manage adapters in Settings. • Gemini API: Google‑compatible endpoints — /v1beta/models/:generateContent and :streamGenerateContent (v1 alias included). • MCP integration: resolved handshake failures and stabilized tool registration across sessions.
LLM AI server providing advanced local generation controls with API/WebUI.
Unlimited AI, zero cloud. Your phone is the server. Run powerful LLMs locally, privately, and freely — with a lightweight UI anyone can use. One Android device becomes your personal AI backend, accessible from PCs, tablets, and other devices on the same network. --- 1. Overview LLM AI Server with llama.cpp is a fully local LLM server for Android, enabling private, offline text and multimodal generation. It supports Hugging Face model search, GGUF downloads, local model loading, and optional MTP speculative decoding for faster inference. Supported model families include Gemma‑4 / Gemma‑4 Vision, Qwen / Qwen Vision, Mistral, LLaMA, Phi, Bonsai, and others. A built‑in API server is compatible with the Ollama, OpenAI, Anthropic (Claude) Messages, and Google Gemini APIs — providing chat, completion, generation, embedding, tokenize/token‑count, and model‑listing endpoints, so existing OpenAI, Ollama, Claude, and Gemini clients and SDKs can point straight at your phone. The bundled WebUI runs on the same port and supports model switching, parameter editing, log viewing, and PWA installation. MCP and Function Calling enable tool‑augmented workflows, and structured output is supported via GBNF and JSON Schema. --- 2. Intended Users and Supported Devices Designed for users who want a private, fully local LLM environment: - Offline‑first users - Developers integrating a local backend - Advanced users tuning sampling and performance - Researchers testing inference behavior - Privacy‑focused users Performance can be tuned via context size, threads, batch size, GPU offload, KV cache quantization, and optional MTP decoding. --- 3. Key Features - Hugging Face GGUF search & download - Qwen3-VL vision model (bundled default) with automatic projector (mmproj) detection - Automatic context (n_ctx) expansion for large images and long prompts - Automatic GPU→CPU fallback when GPU init fails - Smarter Hugging Face search: model / size / quantization guidance, device-suitability rating, and one-tap mmproj download - On-device log retrieval via GET /api/diagnostics - Local GGUF loading - LoRA adapter support: apply a LoRA GGUF to the base model at runtime; select, save, and manage adapters from Settings - MTP speculative decoding - Detailed parameter control (Mirostat, DRY, XTC, Min‑p, Typical‑p, penalties, dynamic temperature) - Integrated Ollama / OpenAI / Anthropic (Claude) Messages / Google Gemini‑compatible API server - Embedding API - Automatic prompt template selection - Enhanced WebUI with multi‑language support - PWA support - MCP and Function Calling - Profile backup & restore support Ollama-, OpenAI-, Anthropic-(Claude-)-, and Google-Gemini-compatible endpoints; full list on the tester page below. Full API docs, examples & an interactive tester: https://micklab.web.app/llama-tester-en.html --- 4. Getting Started You can begin using the app immediately in either of the following ways: A) Start API/WebUI → Open in browser 1. Tap Start API/WebUI on the main screen. 2. Tap Open in browser to launch the bundled WebUI. 3. You can chat and run the model directly from the WebUI. - Note: On first use, if the model is not downloaded yet, the WebUI will download it when you send the first message. Large models may take time to download. B) Direct Run 1. Open the Direct Run section on the main screen. 2. Enter a prompt and tap Send to run the model directly inside the app. - If the model is not downloaded yet, the app will show a notice before downloading. C) or Use Settings to prepare a model first 1. Open Settings. 2. Search Hugging Face for a GGUF model or enter a direct URL or import a local GGUF file. 3. Adjust parameters (context size, temperature, penalties, MTP, GPU offload, etc.). 4. Tap Save Config, then SAVE & CLOSE to load the model. 5. Optionally create a profile backup for later restoration.