Ollama install deepseek Apr 30, 2025 · DeepSeek-R1 distilled models, like DeepSeek-R1-Distill-Qwen-7B and DeepSeek-R1-Distill-LLaMA-70B, are fine-tuned versions of open-source models like LLaMA and Qwen, trained on data generated by DeepSeek-R1. For more information and updates, visit: Ollama's DeepSeek V3 Page; DeepSeek Official Documentation; Ollama GitHub Repository Jan 29, 2025 · STEP 1: Install Ollama. This guide covers installation, configuration, model management, and practical usage examples. Launch the terminal and type the following command. Jan 31, 2025 · In this guide, we’ll walk you through how to install ollama and run deepseek, adding different models, and understanding what each model offers—including DeepSeek R1 and others. Feb 2, 2025 · Learn how to install DeepSeek-R1, a high performing reasoning LLM for data retrieval and management, on your Mac or PC using Ollama, a tool that simplifies AI model installation and management. It tops the leaderboard among open-source models and rivals the most advanced closed-source models globally. Install Ollama. DeepSeek R1 is available in multiple sizes, ranging from 1. DeepSeek R1 vs. 5 or later. If installed correctly, you should see the version number of Ollama displayed. Jan 29, 2025 · Running large language models like DeepSeek locally on your machine is a powerful way to explore AI capabilities without relying on cloud services. It supports DeepSeek-R1, among other models. Download based on your Operating System. Ollama offers a range of DeepSeek R1 models, spanning from 1. Check the Installation: Type ollama and hit Enter. 5 minutes even by non-experts with slightly advanced computer skills. Step 2: Install DeepSeek-R1 32B Model. Jan 30, 2025 · Once the download is complete, install the Ollama application like you would do for any other application. Mar 31, 2025 · To install DeepSeek locally to your PC, Mac, or other machine, you’ll need to use Ollama. A Complete Guide to Ollama: Installation, Models, and Usage Ollama is a powerful tool that simplifies the process of running and managing large language models locally. Whether you’re […] Jan 20, 2025 · DeepSeek V3 on Ollama brings state-of-the-art AI capabilities to local environments. Otherwise, you must use commands. This can be done in approx. Jan 13, 2025 · Note: this model requires Ollama 0. However, thanks to advanced quantization techniques from Unsloth , the model's size can be reduced to 162GB, an 80% reduction. Verify that DeepSeek-R1 is running (ollama run deepseek-r1). Llama 2. Step 2: Download and run DeepSeek-R1. STEP 2: Download Deepseek-R1. This is a free and open-source tool for running various large language models and AI bots locally. ollama run deepseek-r1. In this guide, we’ll walk you through installing DeepSeek using Ollama on Ubuntu 24. Learn how to use Ollama, a user-friendly platform, to download, manage, and run DeepSeek R1, a state-of-the-art language model, locally. Ensure that Ollama is installed correctly: Windows Users: Open the Command Prompt from the Start menu. Apr 12, 2025 · How to Set Up DeepSeek with Ollama 1. Once Ollama is installed, run the following command to download and prepare the model: DeepSeek R1 comes in multiple sizes. Whether you're a developer or an AI enthusiast, you’ll learn how to set Jun 9, 2025 · DeepSeek-R1-0528 is the latest update to DeepSeek's R1 reasoning model that requires 715GB of disk space, making it one of the largest open-source models available. 5 GB of free hard disk space, the free Ollama tool and a suitable Deepseek model. Jan 31, 2025 · If this is your first time using DeepSeek R1, Ollama will automatically download the 7B model before starting it. Follow the steps to install Ollama, pull DeepSeek R1, and interact with it via CLI, API, or Python. . In this guide, we’ll walk you through how to deploy DeepSeek R1 locally on your machine using Ollama and interact with it through the Open Web UI. 5B parameters to Jan 27, 2025 · Learn how to install and use Ollama, a platform for running AI models locally, to run DeepSeek-R1, a powerful data analysis tool. Feb 23, 2025 · For the installation you need a Win/Mac/Linux computer that is as up-to-date as possible, approx. Jan 30, 2025 · Learn how to run powerful DeepSeek AI models locally on your computer using Ollama. Feb 8, 2025 · Verify the installation: ollama --version. Hostinger users can easily install Ollama by selecting the corresponding template during onboarding or in hPanel’s Operating System menu. MacOS/Linux Users: Open the Terminal from Applications or use Spotlight search. 5B to 671B parameters, making it a versatile option for different hardware setups and use cases. 5. 04 and setting up a Web UI for an interactive and user-friendly experience. Thus, inheriting DeepSeek’s reasoning capabilities while being far more efficient to self-host. Note: to update the model from an older version, run ollama pull deepseek-r1 Distilled models DeepSeek team has demonstrated that the reasoning patterns of larger models can be distilled into smaller models, resulting in better performance compared to the reasoning patterns discovered through RL on small models. Whether you're a developer, researcher, or AI enthusiast, this setup provides a powerful platform for exploring advanced language models. First, we need to download and Install Ollama. Jan 29, 2025 · DeepSeek R1 is one of the most advanced AI models available today, offering powerful text generation and NLP capabilities. To begin, connect to your server via SSH using PuTTY or Terminal. Step 3: Verify Ollama Installation. Follow the step-by-step guide, prerequisites and benefits of running DeepSeek-R1 locally. Follow the step-by-step guide and optimize performance, troubleshoot issues, and explore further reading. DeepSeek-V3 achieves a significant breakthrough in inference speed over previous models. Let’s test the setup and download our model. A list of commands should appear, confirming that the installation was . After installing Ollama, we need to download Deepseek-R1 model. Open powershell on windows or terminal on mac and linux and type in the command. qoctobwdwmtqsdipbtjqjrbsatmmotirgfoiulgvgnywxtwdfz