LLM

Alibaba just unveiled Qwen 3, its latest open-source large language model (LLM), and its flagship Qwen3-235B-A22B now rivals DeepSeek R1, Grok-3, and Gemini-2.5-Pro in benchmarks. With hybrid inference and enhanced Agent capabilities, Qwen 3 signals a shift — open source is China’s key to global AI dominance. The New Open-Source Power Duo: Qwen + DeepSeek For years, the open-source LLM ecosystem was ruled by Meta’s Llama and Mistral. But today, DeepSeek and Qwen are taking over.

Deploying Ollama DeepSeek and RAGFlow locally allows you to run powerful natural language processing (NLP) models in your own environment, enabling more efficient data processing and knowledge retrieval. Let’s get started. 1. Environment Preparation First, ensure your local machine meets the following requirements: Operating System: Linux or macOS (Windows is also supported via WSL) Python Version: 3.8 or higher GPU Support (optional): CUDA and cuDNN (for accelerating deep learning models) 2.

Innovation 1: Chain of Thought Self-Evaluation DeepSeek-R1 introduces a technique called “Chain of Thought (CoT),” which allows the model to explain its reasoning step-by-step. For example, when solving a math problem, it breaks down its thought process into clear steps. If an error occurs, it can be traced back to a specific step, enabling targeted improvements. This self-reflection mechanism not only enhances the model’s logical consistency but also significantly improves accuracy in complex tasks.