On‑Device Deployment of Large Language Models Using Sohu’s Hybrid AI Engine and GPT‑2

Introduces the trend of integrating large language models (LLMs) into mobile devices, highlighting challenges such as computational load, privacy concerns, and memory constraints.

Discusses recent moves by Huawei, Xiaomi, OPPO, and Google (Gemini Nano) to enable on‑device AI.

Describes Sohu’s Hybrid AI Engine, which integrates a distilled GPT‑2 model into an offline SDK for mobile clients.

Explains why GPT‑2 was chosen: smaller size, suitability for TensorFlow Lite conversion, and acceptable performance with low memory impact.

Details the use of Keras to customize and pretrain the GPT‑2 model, including environment setup (Linux/Colab, Python 3, tensorflow‑text 2.12) and installation commands.

Shows how to load the pretrained GPT‑2 with Keras, convert it to TensorFlow Lite, and run inference using the TensorFlow Lite interpreter.

Provides a walkthrough of the core SDK code: initializing the GPT service, generating text, and releasing resources, with the key calls wrapped in mGPT = AIHelperFactory.getInstance(context).getGPT(); , mGPT.generate(prompt, text -> promptView.setText(text)); , and mGPT.release(); .

Explains the Tokenizer, Preprocessor, and Backbone components of the Keras NLP pipeline and their role in preparing tensors for the model.

Describes the attention mechanism (Query, Key, Value) with an illustrative analogy.

Concludes that on‑device LLM deployment via Hybrid AI Engine delivers fast, private, and cost‑effective AI experiences on mobile platforms.