DeepSeek-R1 has been creating quite a buzz in the AI community. Developed by a Chinese AI company DeepSeek, this model is being compared to OpenAI's top models. The excitement around DeepSeek-R1 is not just because of its capabilities but also because it is open-sourced, allowing anyone to download and run it locally. In this blog, I'll guide you through setting up DeepSeek-R1 on your machine using Ollama.
Why DeepSeek-R1?
DeepSeek-R1 stands out for several reasons. Not only is it cheaper than many other models, but it also excels in problem-solving, reasoning, and coding. Its built-in chain of thought reasoning enhances its efficiency, making it a strong contender against other models. Let's dive into how you can get this model running on your local system.
Getting Started with Ollama
Before we begin, let's discuss Ollama. Ollama is a free, open-source tool that allows users to run Natural Language Processing models locally. With Ollama, you can easily download and run the DeepSeek-R1 model.
Here's how you can get started:
Step 1: Install Ollama
First, you'll need to download and install Ollama. Visit the Ollama website and download the version that matches your operating system.
Follow the installation instructions provided on the site.
Step 2: Download DeepSeek-R1
Once Ollama is installed, open your terminal and type the following command to download the DeepSeek-R1 model:
ollama run deepseek-r1
This command tells Ollama to download the model. Depending on your internet speed, this might take some time. Grab a coffee while it completes!
Step 3: Verify Installation
After downloading, verify the installation by running:
ollama list
You should see deepseek-r1 in the list of available models. If you do, great job! You're ready to run the model.
Step 4: Run DeepSeek-R1
Now, let's start the model using the command:
ollama run deepseek-r1
And just like that, you're interacting with DeepSeek-R1 locally. It's that simple!
Below is a complete step-by-step video of using DeepSeek-R1 for different use cases.
My first impression about DeepSeek-R1 is just mind blowing:)
By following this guide, you've successfully set up DeepSeek-R1 on your local machine using Ollama. This setup offers a powerful solution for AI integration, providing privacy, speed, and control over your applications. Enjoy experimenting with DeepSeek-R1 and exploring the potential of local AI models. BTW, having a robust database for your AI/ML applications is a must. I recommend using an all-in-one data platform like SingleStore.
RAG Application using DeepSeek and SingleStore
If you like to extend your learning and build a simple RAG application, you can follow this tutorial. We will be using SingleStore as a vector database here to store our data. Singlestore is an all-in-one data platform to build AI/ML applications.
1. Prerequisites
- Python 3.8+
- SingleStore account (free trial available)
- DeepSeek API key
Required libraries:
pip install singlestoredb sentence-transformers deepseek-sdk python-dotenv
Set Up Environment
Create .env file:
SINGLESTORE_HOST=your-singlestore-host
SINGLESTORE_USER=your-username
SINGLESTORE_PASSWORD=your-password
DEEPSEEK_API_KEY=your-api-key
Get credentials from SingleStore Cloud & DeepSeek API.
Database Setup
Create a new database in SingleStore:
CREATE DATABASE rag_db;
The code will automatically create the documents table
Document Preparation
Replace the example documents list with your own data
- For large datasets, consider batch processing
Run the Application
python rag_app.py
Example usage
# Custom query example
query = "How does SingleStore handle vector search?"
print(rag.generate_response(query))
The complete code updated below,
import os
import numpy as np
from typing import List
import singlestoredb as s2
from getpass import getpass
from sentence_transformers import SentenceTransformer
from deepseek_sdk import DeepSeek
class RAGApplication:
def __init__(self):
self._load_env()
self._init_db()
self._init_models()
def _load_env(self):
self.db_config = {
'host': os.getenv('SINGLESTORE_HOST', 'localhost'),
'port': 3306,
'user': os.getenv('SINGLESTORE_USER', 'root'),
'password': os.getenv('SINGLESTORE_PASSWORD', ''),
'database': os.getenv('SINGLESTORE_DB', 'rag_db')
}
self.deepseek_key = os.getenv('DEEPSEEK_API_KEY') or getpass('DeepSeek API key: ')
def _init_db(self):
# Create connection pool
self.pool = s2.create_pool(**self.db_config)
self._create_table()
def _create_table(self):
create_table_sql = """
CREATE TABLE IF NOT EXISTS documents (
id INT PRIMARY KEY AUTO_INCREMENT,
text TEXT,
embedding BLOB,
VECTOR INDEX (embedding) USING FLAT
)
"""
with self.pool.connect() as conn:
conn.execute(create_table_sql)
def _init_models(self):
self.embedder = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
self.llm = DeepSeek(api_key=self.deepseek_key)
def store_documents(self, documents: List[str]):
embeddings = self.embedder.encode(documents)
insert_sql = """
INSERT INTO documents (text, embedding)
VALUES (%s, JSON_ARRAY_PACK(%s))
"""
with self.pool.connect() as conn:
with conn.cursor() as cursor:
cursor.executemany(insert_sql, [
(text, f'[{",".join(map(str, emb.tolist()))}]')
for text, emb in zip(documents, embeddings)
])
conn.commit()
def retrieve_context(self, query: str, top_k: int = 3) -> List[str]:
query_embedding = self.embedder.encode([query])[0].tolist()
search_sql = """
SELECT text
FROM documents
ORDER BY DOT_PRODUCT(embedding, JSON_ARRAY_PACK(%s)) DESC
LIMIT %s
"""
with self.pool.connect() as conn:
with conn.cursor() as cursor:
cursor.execute(search_sql, (
f'[{",".join(map(str, query_embedding))}]',
top_k
))
results = cursor.fetchall()
return [result[0] for result in results]
def generate_response(self, query: str) -> str:
context = self.retrieve_context(query)
prompt = f"Context:\n{'\n'.join(context)}\n\nQuestion: {query}\nAnswer:"
response = self.llm.chat(
messages=[{'role': 'user', 'content': prompt}],
temperature=0.7
)
return response.choices[0].message.content
if __name__ == "__main__":
rag = RAGApplication()
# Store sample documents
documents = [
"DeepSeek is a Chinese AI company focused on AGI research.",
"SingleStore is a distributed SQL database optimized for real-time analytics.",
"RAG combines retrieval from databases with LLM generation.",
"Vector embeddings represent text as numerical vectors for similarity search."
]
rag.store_documents(documents)
# Example query
query = "What is RAG?"
print(rag.generate_response(query))
Thank you for reading my article. You can also follow me through my Youtube channel. I create AI/ML/Data related videos on a weekly basis.
Top comments (12)
What is the minimum Requirements of Hardware to run this?
As you can see when you go to Llama website, you can run the different parameters of DeepSeek-R1. You can find the details of requirements here: ollama.com/library/deepseek-r1
You can run 1.5b, 7b, 8b, 14b, 32b, 70b, 671b and obviously the hardware requirements increase as you choose bigger parameter. I used 7b one in the above tutorial.
Good one, it helped me a lot.
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Thanks for the summary.
is there GUi for local version?
yeah there is one ! you can pair Ollama with Open WebUI – a graphical user interface (GUI) tool
just go to docs.openwebui.com
No idea, need to check.
MSTY.app is a great one and my favorite. LMStudio is nice as well.
My name is Eugénio Correia
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What happened at tinanimun Square
To be fair... As the other AIs what think of Donal trump