# DeepSeek
# Instalação e configuração DeepSeek
# Using DeepSeek-R1 Locally
Link: [https://www.kdnuggets.com/using-deepseek-r1-locally](https://www.kdnuggets.com/using-deepseek-r1-locally)
Run powerful reasoning models locally, matching the performance of OpenAI's o1 capabilities, completely free, and avoid paying $200 a month for a pro subscription.
By **[Abid Ali Awan](https://www.kdnuggets.com/author/abidali-awan "Posts by Abid Ali Awan")**, KDnuggets Assistant Editor on January 27, 2025 in [Language Models](https://www.kdnuggets.com/tag/language-models)
Image by Author
Many professionals are buzzing about the new DeepSeek model, claiming it could be an "OpenAI killer," and the hype surrounding it seems justified. Recently, DeepSeek launched the DeepSeek-R1-Zero and DeepSeek-R1 models in various versions. These models deliver performance comparable to OpenAI's o1 on benchmarks like MMLU, Math-500, Codeforces, and more.
In this short tutorial, we will explore the DeepSeek-R1 model and walk through how to run its Distill version locally using Ollama, Docker, and Open WebUI. This means you will be able to use a reasoning model with a user interface similar to ChatGPT—completely free and without needing an internet connection.
## What is DeepSeek-R1?
DeepSeek has introduced first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. The DeepSeek-R1 was built upon its predecessor, DeepSeek-R1-Zero, which was trained exclusively with large-scale reinforcement learning (RL) without supervised fine-tuning (SFT). While DeepSeek-R1-Zero showcased advanced reasoning behaviors such as self-verification and generating long chain-of-thoughts (CoTs), it faced challenges like repetitive responses, poor readability, and language mixing. To address these limitations, DeepSeek-R1 incorporates cold-start data before RL, enhancing reasoning performance across math, code, and logic tasks. It achieves results similar to OpenAI-o1 and has led to developing smaller, high-performing distilled models, such as DeepSeek-R1-Distill-Qwen-32B, which achieves state-of-the-art results on reasoning benchmarks.
## Setting up Open WebUI
Before we install[ Open WebUI](https://docs.openwebui.com/), an open-source chat user interface similar to ChatGPT, we have to download and install Docker desktop by going to the official website:[ https://www.docker.com/](https://www.docker.com/).
After that, you can pull the Open WebUI image from the GitHub container repository by typing the following command in the terminal.
```
docker pull ghcr.io/open-webui/open-webui:main
```
After successfully pulling the Docker image, we need to run the Docker container using the Open WebUI image. We will map the volume for persistent data storage with the option `-v open-webui:/app/backend/data`. Additionally, we will map the port using `-p 9783:8080`, which exposes the WebUI on port 9783 of your local machine.
```
docker run -d -p 9783:8080 -v open-webui:/app/backend/data --name open-webui ghcr.io/open-webui/open-webui:main
```
Wait a few seconds, then access the web app by copying and pasting the URL [http://localhost:9783/](http://localhost:9783/) into your browser. It will prompt you to create an account, and after that, you will be redirected to the main chat menu. As you can see, there are no models available for selection. To resolve this, we will set up Ollama next.
## Setting up Ollama
Go to the official website,[ https://ollama.com/](https://ollama.com/), to download and install Ollama. Afterward, go to the “Models” menu and select the[ deepseek-r1](https://ollama.com/library/deepseek-r1) option. This page will contain a run command to download and run various versions of the DeepSeek R1 models.
## Using DeepSeek-R1 Locally
Refresh the Open WebUI page, and you will see the `deespseek-r1:8b` model. Select the model and start using it.
After typing the default prompt, it took the model 18 seconds to think before responding, which is great and similar to the o1 model.
You can see the thought process by clicking on the “Thought for 18 Second” drop-down menu.
The model response generation was fast, close to 54 tokens per second. This is the best performance you can achieve from the 8B parameter quantized model.
To test the full version of the DeepSeek-R1 model, please visit[ https://chat.deepseek.com/](https://chat.deepseek.com/) and select the `DeepThink (R1)` option.
## Conclusion
Open-source AI is the future, and even big tech giants recognize this. With companies from China entering the scene, we, as users and everyday people, have a great opportunity to take advantage of advanced AI models privately using local resources.
All you need to do is install Ollama and Docker, then pull the Docker image of the Open WebUI application using a simple Docker command. Trust me, it’s that straightforward! This setup requires limited computing resources, so even a laptop with 8GB of RAM and no GPU can run these models effectively.
So, what are you waiting for? Start building and integrating these tools into your workspace.
**[Abid Ali Awan](https://www.polywork.com/kingabzpro)** ([@1abidaliawan](https://www.linkedin.com/in/1abidaliawan)) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in technology management and a bachelor's degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.
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# Run DeepSeek-R1 locally with Ollama on Docker
Link: [https://www.linkedin.com/pulse/run-deepseek-r1-locally-ollama-docker-adrian-escutia-wtikc/](https://www.linkedin.com/pulse/run-deepseek-r1-locally-ollama-docker-adrian-escutia-wtikc/)
Simplifying Solutions in Airgap and Enterprise-Restricted Environments
Would you like to run local LLMs, here is how you can do it with Ollama and the new [DeepSeek](https://www.deepseek.com/)-R1 model that is breaking the boundaries of AI. 🚀
For those of us passionate about pushing the boundaries of AI, this is a game changer. 💡
Being able to run powerful language models locally, with the flexibility to fine-tune and experiment in a more personalized and secure environment, opens up so many possibilities.
Steps to run [DeepSeek AI](https://www.linkedin.com/company/deepseek-ai/) locally with [Ollama](https://www.linkedin.com/company/ollama/) on Docker:
```
# Install Ollama
docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
# Pull the DeepSeek-R1 model
docker exec -it ollama ollama run deepseek-r1:7b
# Start chatting with DeepSeek-R1 - Web UI
docker run -d -p 3000:8080 --add-host=host.docker.internal:host-gateway -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:main
```
Open [http://localhost:3000](http://localhost:3000/) in your browser, and you are done!
Let's make AI work for us, locally, efficiently, and creatively