4 ways to run LLMs like DeepSeek-R1 locally on your computer:
Running LLMs locally is like having a superpower:
- Cost savings
- Privacy: Your data stays on your computer
- Plus, it's incredibly fun
Let us explore some of the best methods to achieve this.
1️⃣ *Ollama*
* Running a model through Ollama is as simple as executing a command: ollama run deepseek-r1
* You can also install Ollama with a single command: curl -fsSL https:// ollama. com/install .sh | sh
2️⃣ *LMStudio*
* Install LMStudio can be installed as an app on your computer.
* It offers a ChatGPT-like interface, allowing you to load and eject models as if you were handling tapes in a tape recorder.
3️⃣ *vLLM*
* vLLM is a fast and easy-to-use library for LLM inference and serving.
* It has State-of-the-art serving throughput ⚡️
* A few lines of code and you can locally run DeepSeek as an OpenAI compatible server with reasoning enabled.
4️⃣ *LlamaCPP (the OG)*
* LlamaCPP enables LLM inference with minimal setup and state-of-the-art performance.
Running LLMs locally is like having a superpower:
- Cost savings
- Privacy: Your data stays on your computer
- Plus, it's incredibly fun
Let us explore some of the best methods to achieve this.
1️⃣ *Ollama*
* Running a model through Ollama is as simple as executing a command: ollama run deepseek-r1
* You can also install Ollama with a single command: curl -fsSL https:// ollama. com/install .sh | sh
2️⃣ *LMStudio*
* Install LMStudio can be installed as an app on your computer.
* It offers a ChatGPT-like interface, allowing you to load and eject models as if you were handling tapes in a tape recorder.
3️⃣ *vLLM*
* vLLM is a fast and easy-to-use library for LLM inference and serving.
* It has State-of-the-art serving throughput ⚡️
* A few lines of code and you can locally run DeepSeek as an OpenAI compatible server with reasoning enabled.
4️⃣ *LlamaCPP (the OG)*
* LlamaCPP enables LLM inference with minimal setup and state-of-the-art performance.
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𝐋𝐢𝐧𝐤👇:-
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No more flipping through tabs & tutorials—just pin this cheat sheet and analyze data like a pro!✅️
Don't waste your lot of time when learning data analysis.
Here's how you may start your Data analysis journey
1️⃣ - Avoid learning a programming language (e.g., SQL, R, or Python) for as long as possible.
This advice might seem strange coming from a former software engineer, so let me explain.
The vast majority of data analyses conducted each day worldwide are performed in the "solo analyst" scenario.
In this scenario, nobody cares about how the analysis was completed.
Only the results matter.
Also, the analysis methods (e.g., code) are rarely shared in this scenario.
Like for next steps
#dataanalysis
Here's how you may start your Data analysis journey
1️⃣ - Avoid learning a programming language (e.g., SQL, R, or Python) for as long as possible.
This advice might seem strange coming from a former software engineer, so let me explain.
The vast majority of data analyses conducted each day worldwide are performed in the "solo analyst" scenario.
In this scenario, nobody cares about how the analysis was completed.
Only the results matter.
Also, the analysis methods (e.g., code) are rarely shared in this scenario.
Like for next steps
#dataanalysis
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This 100% FREE resource from Kaggle will help you build job-ready skills—no fluff, no fees, just pure learning!
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SQL is one of the core languages used in data science, powering everything from quick data retrieval to complex deep dive analysis. Whether you're a seasoned data scientist or just starting out, mastering SQL can boost your ability to analyze data, create robust pipelines, and deliver actionable insights.
Let’s dive into a comprehensive guide on SQL for Data Science!
I have broken it down into three key sections to help you:
𝟭. 𝗦𝗤𝗟 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀:
Get a handle on the essentials -> SELECT statements, filtering, aggregations, joins, window functions, and more.
𝟮. 𝗦𝗤𝗟 𝗶𝗻 𝗗𝗮𝘆-𝘁𝗼-𝗗𝗮𝘆 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲:
See how SQL fits into the daily data science workflow. From quick data queries and deep-dive analysis to building pipelines and dashboards, SQL is really useful for data scientists, especially for product data scientists.
𝟯. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗦𝗤𝗟 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀:
Learn what interviewers look for in terms of technical skills, design and engineering expertise, communication abilities, and the importance of speed and accuracy.
Let’s dive into a comprehensive guide on SQL for Data Science!
I have broken it down into three key sections to help you:
𝟭. 𝗦𝗤𝗟 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀:
Get a handle on the essentials -> SELECT statements, filtering, aggregations, joins, window functions, and more.
𝟮. 𝗦𝗤𝗟 𝗶𝗻 𝗗𝗮𝘆-𝘁𝗼-𝗗𝗮𝘆 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲:
See how SQL fits into the daily data science workflow. From quick data queries and deep-dive analysis to building pipelines and dashboards, SQL is really useful for data scientists, especially for product data scientists.
𝟯. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗦𝗤𝗟 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀:
Learn what interviewers look for in terms of technical skills, design and engineering expertise, communication abilities, and the importance of speed and accuracy.
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Whether you’re a complete beginner or looking to level up, these courses cover Excel, Power BI, Data Science, and Real-World Analytics Projects to make you job-ready.
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Whether you’re a complete beginner or looking to level up, these courses cover Excel, Power BI, Data Science, and Real-World Analytics Projects to make you job-ready.
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Three different learning styles in machine learning algorithms:
1. Supervised Learning
Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.
A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.
Example problems are classification and regression.
Example algorithms include: Logistic Regression and the Back Propagation Neural Network.
2. Unsupervised Learning
Input data is not labeled and does not have a known result.
A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.
Example problems are clustering, dimensionality reduction and association rule learning.
Example algorithms include: the Apriori algorithm and K-Means.
3. Semi-Supervised Learning
Input data is a mixture of labeled and unlabelled examples.
There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.
Example problems are classification and regression.
Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.
1. Supervised Learning
Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.
A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.
Example problems are classification and regression.
Example algorithms include: Logistic Regression and the Back Propagation Neural Network.
2. Unsupervised Learning
Input data is not labeled and does not have a known result.
A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.
Example problems are clustering, dimensionality reduction and association rule learning.
Example algorithms include: the Apriori algorithm and K-Means.
3. Semi-Supervised Learning
Input data is a mixture of labeled and unlabelled examples.
There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.
Example problems are classification and regression.
Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.
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⏳ Don’t miss out—bookmark this for later!
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