LLM

2 posts

Deploy Ollama DeepSeek + RAGFlow Locally

・ Technology #DeepSeek #RAGFlow #Ollama #LLM #Tutorial

Here's how to deploy Ollama DeepSeek and RAGFlow locally to build your own RAG system.

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.

DeepSeek-R1's Innovation

・ Research #DeepSeek #LLM

Learn about the innovative features of DeepSeek-R1.

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.