# Knowledge Processing

Developed by the InternLM team, the InternLM3-8B-Instruct is a large language model featuring exceptional reasoning capabilities and proficiency in knowledge-intensive tasks. Despite being trained with only 40 trillion high-quality tokens, it achieves over 75% lower training costs than similar models, while outperforming models such as Llama3.1-8B and Qwen2.5-7B on multiple benchmark tests. It supports deep reasoning modes that tackle complex inference tasks, while also offering smooth user interaction capabilities. The model is open-sourced under the Apache-2.0 license, making it suitable for various applications needing efficient reasoning and knowledge processing.

KPU (Knowledge Processing Unit) is a proprietary and rich framework that leverages the powerful capabilities of large language models and separates reasoning and data processing into an open system capable of tackling complex tasks. It comprises three main components: a reasoning engine, an execution engine, and a virtual context window. The reasoning engine is responsible for designing a step-by-step plan to solve user-defined tasks, utilizing pluggable large language models (currently widely tested with GPT-4 Turbo). The execution engine receives commands from the reasoning engine and executes them, sending the results back to the reasoning engine for replanning. The virtual context window manages the data and information input-output between the reasoning engine and the execution engine. This separation of reasoning and execution allows large language models to focus solely on reasoning, mitigating drawbacks such as hallucinations, data handling issues, or retrieving the latest information. KPU aims to enhance task quality and performance, addressing challenges related to large data volumes, multi-modal content, open-ended problem solving, and interactivity.