🤖 Local LLMs on Android (Offline, Private & Fast)

An Android application that brings a large language model (LLM) to your phone — fully offline, no internet needed. Powered by ONNX Runtime and a Hugging Face-compatible tokenizer, it provides fast, private, on-device question answering with streaming responses.

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✨ Features

• 📱 Fully on-device LLM inference with ONNX Runtime.
• 🔤 Hugging Face-compatible BPE tokenizer (tokenizer.json)
• 🧠 Qwen2.5 & Qwen3 prompt formatting with streaming generation
• 🧩 Custom ModelConfig for precision, prompt style, and KV cache
• 🧘‍♂️ Thinking Mode toggle (enabled in Qwen3) for step-by-step reasoning
• 🚀 Coroutine-based UI for smooth user experience.
• 🔐 Runs 100% offline — no network, no telemetry

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📸 Inference Preview

Figure: App interface showing prompt input and generated answers using the local LLM.

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📂 App Variants

This repo includes two modes of interaction:

Qwen_QA_style_app

• Single-turn QA with minimal prompt.
• Fastest response time.
• Best for quick facts or instructions.

Qwen_chat_style_app

• Multi-turn chat with short-term memory.
• Qwen-style prompt formatting with context compression.
• Best for reasoning, assistant-style dialogue, and follow-up questions.

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🧠 Model Info

This app supports both Qwen2.5-0.5B-Instruct and Qwen3-0.6B — optimized for instruction-following, QA, and reasoning tasks.

🔁 Option 1: Use Preconverted ONNX Model

Download the model.onnx and tokenizer.json from Hugging Face:

• 🔹 Qwen2.5
• 🔹 Qwen3

⚙️ Option 2: Convert Model Yourself

pip install optimum[onnxruntime]
# or
python -m pip install git+https://github.com/huggingface/optimum.git

Export the model:

optimum-cli export onnx --model Qwen/Qwen2.5-0.5B-Instruct qwen2.5-0.5B-onnx/

• You can also convert any fine-tuned variant by specifying the model path.
• Learn more about Optimum here.

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⚙️ Requirements

• Android Studio
• ONNX Runtime for Android (already included in this repo).
• A physical Android device for deployment and testing, ≥ 4 GB RAM for FP16 / Q4 models, ≥ 6 GB RAM for FP32 models.
• Real hardware preferred—emulators are acceptable for UI checks only.

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Choose which Qwen model to run

InMainActivity.kt you will find two pre-defined ModelConfig objects:

val modelconfigqwen25 = …   // Qwen 2.5-0.5B
val modelconfigqwen3  = …   // Qwen 3-0.6B

Right below them is a single line that tells the app which one to use:

val config = modelconfigqwen25      // ← change to modelconfigqwen3 for Qwen 3

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How to Build & Run

1. Open Android Studio and create a new project (Empty Activity).
2. Name your app local_llm.
3. Copy all the project files from Qwen_QA_style_app or Qwen_chat_style_app into the appropriate folders.
4. Place your model.onnx and tokenizer.json in:

   app/src/main/assets/
   

5. Connect your Android phone using wireless debugging or USB.
6. To install:
   • Press Run ▶️ in Android Studio, or
   • Go to Build → Generate Signed Bundle / APK to export the .apk file.
7. Once installed, look for the Pocket LLM icon  on your home screen.

Note: All Kotlin files are declared in the package com.example.local_llm, and the Gradle script sets applicationId "com.example.local_llm". If you name the app (or change the package) to anything other than local_llm, you must refactor:

• The directory structure in app/src/main/java/...,
• Every package com.example.local_llm line, and
• The applicationId in app/build.gradle.
• Otherwise, Android Studio will raise “package … does not exist” errors and the project will fail to compile.

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📦 Download Prebuilt APKs

• ➡️ pocket_llm_qwen2.5_0.5B_v1.1.0.apk

  • Full precision (FP32). Best for high-end devices. Improved inference performance.

• ➡️ pocket_llm_qwen2.5_0.5B_fp16_v1.1.0.apk

  • Half-precision (FP16). Great balance of speed and accuracy for most devices.

• ➡️ pocket_llm_qwen2.5_0.5B_q4fp16_v1.1.0.apk

  • Quantized Q4 + FP16. Fastest and lightest version of Qwen2.5.

• ➡️ pocket_llm_qwen3_0.6B_fp16_v1.1.0.apk

  • 🔥 New! Qwen3-0.6B with improved reasoning and Thinking Mode support.

• ➡️ pocket_llm_qwen3_0.6B_q4fp16_v1.1.0.apk

  • 🔥 New! Qwen3 quantized version (Q4 + FP16). Compact and fast with Thinking Mode.

Customize Your App Experience with These

• Define the assistant’s tone and role by setting defaultSystemPrompt (in your model config).
• Adjust TEMPERATURE to control response randomness — lower for accuracy, higher for creativity (OnnxModel.kt).
• Use REPETITION_PENALTY to avoid repetitive answers and improve fluency (OnnxModel.kt).
• Change MAX_TOKENS to limit or expand the length of generated replies (OnnxModel.kt).

📄 License Notice

Note: These ONNX models are based on Qwen, which is licensed under the Apache License 2.0.