What You Will Learn

• Explain DeepSeek's R1 model and its ecosystem
• Assess DeepSeek's cost-efficiency and real-world impact
• Run R1 models locally using Ollama and LM Studio
• Integrate R1 programmatically with Hugging Face Transformers
• Optimize hardware, quantization, and resource usage

Study Guide

Introduction to 'Video Course: DeepSeek-R1 Crash Course'

Welcome to the 'DeepSeek-R1 Crash Course', a comprehensive guide designed to introduce you to the world of DeepSeek, a pioneering company in the field of Large Language Models (LLMs). This course will take you from the basics of understanding what DeepSeek is and why it matters, to practical implementations and advanced usage of their models. Whether you're a tech enthusiast, a business professional, or someone curious about AI, this course is crafted to equip you with the knowledge to leverage DeepSeek models effectively.

Why is this course valuable?
DeepSeek's models are not just another set of LLMs; they represent a significant shift in cost efficiency and accessibility in the AI landscape. With speculation of a 95-97% reduction in costs compared to other leading models, DeepSeek democratizes access to powerful AI tools. This course will not only teach you how to use these models but also provide insights into their potential applications and advantages.

Section 1: Understanding DeepSeek

What is DeepSeek?
DeepSeek is a Chinese company renowned for developing open-weight Large Language Models (LLMs). Their suite of models includes R1, R1 Z, DeepSeek V3, Math Coder, Moe (Mixture of Experts), and DeepSeek V3 (Mixture of Models). The company's models are celebrated for their reasoning capabilities and significantly lower training and operational costs compared to industry giants like OpenAI.

Why DeepSeek Matters
The significance of DeepSeek lies in its ability to offer high-performance AI models at a fraction of the cost. This cost efficiency is crucial, as it allows broader access to advanced AI technologies, making them feasible for smaller businesses and individual developers. By reducing the financial barriers, DeepSeek has the potential to accelerate innovation and application of AI across various industries.

Section 2: DeepSeek's Model Portfolio

Overview of Models
DeepSeek offers a diverse range of models, each tailored for specific tasks and capabilities. The primary focus of this course is the R1 model, known for its text generation prowess. However, we will also touch upon V3, which is prominently featured on the DeepSeek website.

DeepSeek R1 and its Precursor, r10
DeepSeek R1 is an evolution of the r10 model, which was trained using large-scale reinforcement learning without supervised fine-tuning. While r10 showcased impressive reasoning capabilities, it suffered from poor readability and language mixing. R1 was developed to address these issues, achieving performance comparable to some of OpenAI's models. Notably, R1 is solely focused on text generation.

Section 3: The Significance of DeepSeek: Cost Reduction

Cost Efficiency
One of the most compelling aspects of DeepSeek is its cost efficiency. It's speculated that DeepSeek models offer a 95-97% reduction in cost compared to OpenAI models. This dramatic cost reduction is achieved through optimized training processes that reportedly cost around $5 million, a fraction of what other models require. This efficiency has significant implications for the AI industry, potentially influencing hardware manufacturers and the broader AI market.

Practical Implications
The cost savings offered by DeepSeek models mean that more organizations can afford to experiment with and deploy advanced AI solutions. For example, a small startup could leverage DeepSeek's models to develop a new AI-driven product without the prohibitive costs typically associated with LLMs. Similarly, educational institutions could integrate these models into their curriculum, providing students with hands-on experience in AI without breaking the bank.

Section 4: Hardware Considerations for Running DeepSeek Locally

Local vs. Cloud Execution
Running DeepSeek models locally is often more cost-effective than cloud-based solutions, as it avoids ongoing cloud service fees and provides direct control over hardware resources. However, the feasibility of local execution heavily depends on your hardware setup.

Recommended Hardware
To run DeepSeek models effectively, particularly the larger ones, you'll need robust hardware. The course demonstrates running models on an Intel Lunar Lake AI PC dev kit and a Precision 3680 Tower workstation with a GeForce RTX 4080 graphics card. While the RTX 4080 offers superior performance, the AI PC dev kit is a more budget-friendly option. It's possible to run models with 7-8 billion parameters on these setups, though optimization is crucial to prevent system crashes.

Section 5: Initial Exploration: DeepSeek.com AI Powered Assistant (V3)

Using the Online Assistant
DeepSeek offers an AI-powered assistant on their website, positioned as a competitor to ChatGPT and similar models. At the time of the course recording, this assistant was completely free and demonstrated capabilities such as handling complex prompts and transcribing text from images.

Practical Example
The course tested the assistant with a Japanese language learning prompt, showcasing its ability to manage roles, instructions, and multiple input documents. While the assistant performed well, it initially struggled to adhere strictly to prompt instructions, a common challenge in AI models that highlights the need for ongoing refinement and understanding of model behavior.

Section 6: Local Download and Usage with O llama (R1)

Downloading and Running Models Locally
O llama is a tool that facilitates the local running of LLMs via the terminal. DeepSeek R1 models, ranging from 1.5 billion to 671 billion parameters, can be downloaded through O llama. However, the largest models require substantial hardware resources, such as multiple high-end GPUs or networked systems.

Practical Application
The course successfully demonstrates downloading and running 7 billion and 1.5 billion parameter R1 models on the Intel Lunar Lake AI PC dev kit. Fine-tuning smaller models for specific tasks is suggested as a strategy to enhance performance, making it possible to tailor the models to particular use cases without requiring excessive computational power.

Section 7: Local Usage with LM Studio (R1 Distilled)

LM Studio as a User-Friendly Tool
LM Studio provides a more accessible interface for running LLMs, resembling a chat assistant. It doesn't require O llama for model downloads, making it an all-inclusive option for users who prefer a graphical interface.

Example of Use
The course demonstrates running the "deep seek R1 distilled llama 8B" model within LM Studio. While initial attempts to run the model on the Intel AI PC dev kit led to system restarts, adjusting settings improved performance. Running the model on a workstation with the RTX 4080 yielded significantly better results, underscoring the importance of dedicated GPU resources for optimal performance.

Section 8: Programmatic Usage with Hugging Face Transformers (R1 Distilled)

Using Hugging Face Transformers
The course explores downloading and using DeepSeek R1 models programmatically via the Hugging Face Transformers library. This approach is ideal for developers who want to integrate DeepSeek models into their applications or workflows.

Implementation Example
Setting up a Python environment and installing necessary libraries, such as Transformers, PyTorch, and TensorFlow, is covered in detail. The course successfully runs a text generation pipeline after resolving dependency issues, demonstrating the practical steps needed to harness the power of DeepSeek models programmatically.

Conclusion

By completing the 'DeepSeek-R1 Crash Course', you now possess a comprehensive understanding of DeepSeek and its R1 model. From the basics of DeepSeek's significance and model portfolio to practical implementations using various tools, you've explored every facet of this innovative technology. The course has equipped you with the knowledge to run DeepSeek models locally, understand hardware considerations, and interact with these models programmatically. As you apply these skills, remember the importance of thoughtful application and ongoing learning to fully leverage the potential of DeepSeek's cost-efficient AI models. Embrace the possibilities, and let your creativity guide you in exploring new AI-driven solutions.

Podcast

There'll soon be a podcast available for this course.

Frequently Asked Questions

Welcome to the FAQ section for the 'DeepSeek-R1 Crash Course'. This resource is designed to provide you with clear, concise, and comprehensive answers to common questions about DeepSeek and its R1 model. Whether you're a beginner or an advanced user, you'll find valuable insights into the workings, applications, and challenges of these cutting-edge language models. Let's dive into the world of DeepSeek!

1. What is DeepSeek?

DeepSeek is a Chinese company that develops open-weight Large Language Models (LLMs). They offer a variety of models, including R1, R1 Z, DeepSeek V3, Math Coder, Moe (Mixture of Experts), and DeepSeek V3 (Mixture of Models). Their models are known for their reasoning capabilities and a significantly lower estimated cost of training and running compared to models from companies like OpenAI.

2. What is the significance of DeepSeek R1?

DeepSeek R1 is a text generation model that has demonstrated performance comparable to OpenAI's models in certain benchmarks. The primary significance of R1 lies in its speculated 95-97% reduction in cost compared to models like those from OpenAI. This cost efficiency has the potential to democratise access to powerful LLMs, as the high expenses associated with training and running such models have historically been a barrier. R1 was developed to address issues like poor readability and language mixing found in its predecessor, r10.

3. Can DeepSeek models be run locally?

Yes, various DeepSeek models, particularly the smaller parameter versions (e.g., 1.5 billion, 7 billion, 8 billion), can be downloaded and run on local hardware. The feasibility and performance depend heavily on the specifications of your computer, including the CPU, RAM, and especially the GPU (or integrated graphics on newer AI PCs). Tools like Ollama and LM Studio facilitate the download and running of these models locally.

4. What hardware is recommended for running DeepSeek models locally?

While smaller models can run on standard PCs with sufficient RAM (e.g., 32GB for a 7 billion parameter model), for better performance and larger models, newer "AI PCs" with integrated Graphics Processing Units (iGPUs) and Neural Processing Units (NPUs), or dedicated NVIDIA GeForce RTX series graphics cards are beneficial. For running very large models (hundreds of billions of parameters), multiple networked computers with significant unified memory, such as stacked Mac Minis or distributed systems using frameworks like Ray, may be required, though performance might still be slower than cloud-based solutions.

5. How can I interact with DeepSeek models?

There are several ways to interact with DeepSeek models:
DeepSeek.com: The DeepSeek website offers an AI-powered assistant based on DeepSeek V3, which can be used similarly to ChatGPT and supports text and image input.
Ollama: This tool allows you to download and run various LLMs, including DeepSeek R1, via the command line.
LM Studio: A user-friendly application that provides a chat-like interface for interacting with LLMs downloaded locally. It supports GGML/GGUF format models, which are compatible with projects like llama.cpp and often optimised for CPU and GPU use.
Hugging Face Transformers: The Transformers library in Python can be used to programmatically download and run DeepSeek models for local inference, provided the necessary dependencies like PyTorch or TensorFlow are installed and your hardware resources are sufficient.

6. What are distilled models and why are they mentioned for DeepSeek R1?

Distilled models are smaller, more efficient versions of larger models that have undergone a process called knowledge distillation. This involves transferring the knowledge and capabilities of a larger, more complex model to a smaller one, allowing the smaller model to achieve comparable performance on many tasks while requiring less computational resources. DeepSeek R1 distilled versions, such as the Llama 8 billion parameter distilled model, are more feasible to run on consumer-grade hardware.

7. What challenges might I encounter when running DeepSeek models locally?

Several challenges can arise:
Hardware limitations: Large models require significant RAM and GPU resources. Running models that exceed your hardware capabilities can lead to slow performance, computer crashes, or the inability to run the model at all.
Optimisation: Models may not be fully optimised for all hardware configurations. Formats like GGML/GGUF are often preferred for CPU inference, while other formats might be more GPU-intensive.
Software dependencies: Running models programmatically often requires installing specific libraries and frameworks (e.g., PyTorch, TensorFlow, Transformers), and compatibility issues can occur.
Resource management: It's crucial to monitor your computer's resource usage (CPU, GPU, RAM) when running LLMs locally to avoid overloading the system.

8. Where can I find DeepSeek models for local use?

DeepSeek models are primarily available through:
Hugging Face Hub: Many DeepSeek models, including R1 and its distilled versions, are hosted on the Hugging Face Model Hub. You can download them using tools like Ollama, LM Studio, or the Transformers library.
Ollama's library: Ollama has a built-in way to pull various LLMs, including DeepSeek R1, directly from repositories (likely Hugging Face).
LM Studio's model catalogue: LM Studio provides a user interface to browse and download compatible LLMs, including distilled DeepSeek R1 variants.

9. What is DeepSeek AI?

DeepSeek AI refers to the suite of open-weight large language models developed by the DeepSeek company. These models are designed to be accessible and cost-efficient compared to other industry-leading models, making them attractive for various applications in business and research.

10. What are open-weight models?

Open-weight models are AI models whose weights, or the parameters learned during training, are publicly available. This openness allows developers and researchers to access, modify, and build upon these models, fostering innovation and collaboration in the AI community.

11. What does parameter size mean in LLMs?

Parameter size refers to the number of learnable parameters in a model, which generally indicates its complexity and potential capabilities. Larger models typically require more computational resources but can perform more complex tasks. For example, a model with 7 billion parameters will need more memory and processing power than one with 1.5 billion parameters.

12. How do DeepSeek models achieve cost efficiency?

Cost efficiency in DeepSeek models is achieved through optimised training processes and architecture designs that reduce computational requirements. This results in a speculated 95-97% reduction in costs compared to models like OpenAI's, making them more accessible for widespread use without sacrificing performance.

13. How does hardware affect the performance of DeepSeek models?

Hardware dependence is crucial for running DeepSeek models effectively. The performance of these models on local machines relies heavily on the available CPU, GPU (integrated or dedicated), and RAM. More powerful hardware can handle larger models and provide faster inference times, while less capable systems may struggle with even smaller models.

14. What is Olama and how is it used?

Olama is a tool that facilitates the download and running of LLMs like DeepSeek R1 via the terminal. It simplifies the process of interacting with these models on local machines, providing a streamlined way to manage and execute AI tasks without needing a deep understanding of the underlying code.

15. What is the Hugging Face Transformers library?

The Hugging Face Transformers library is a popular Python tool for working with pre-trained language models. It allows users to easily download, fine-tune, and deploy models like DeepSeek for various natural language processing tasks, leveraging a vast ecosystem of tools and community support.

16. What is LM Studio?

LM Studio is a desktop application that provides a user-friendly interface for downloading and running LLMs on local hardware. It offers an experience similar to interacting with online AI assistants, making it accessible for users who prefer a graphical interface over command-line tools.

17. What is quantization in the context of DeepSeek models?

Quantization involves techniques used to reduce the memory footprint and computational cost of a model by lowering the precision of the model's weights. This can significantly enhance the efficiency of running large models like DeepSeek on consumer-grade hardware without greatly affecting performance.

18. What does TOPS mean in relation to AI workloads?

TOPS (Tera Operations Per Second) is a measure of a processor's computational power, particularly relevant for AI workloads. It indicates how many trillion operations a processor can perform per second, which is crucial for evaluating the performance of CPUs, GPUs, and NPUs when running complex models.

19. What is an HF Token?

An HF Token is a personal access token from Hugging Face used to authenticate and download models from their platform. It ensures secure access to the vast library of models and datasets available on Hugging Face, facilitating seamless integration with local and cloud-based AI projects.

20. What is agentic behaviour in AI models?

Agentic behaviour refers to the ability of an AI model to exhibit autonomous or goal-directed actions and decision-making. This often involves planning and interaction with an environment or user, enabling more dynamic and adaptive AI applications.

21. What is a benchmark in AI?

A benchmark is a standardised test or set of tests used to evaluate and compare the performance of different AI models or systems across specific tasks or capabilities. Benchmarks help identify strengths and weaknesses, guiding improvements and innovations in AI development.

22. What is distributed compute?

Distributed compute is a computing paradigm where tasks are divided and processed across multiple interconnected computers or processors. This approach is often used to handle computationally intensive workloads like training and running large AI models, enhancing efficiency and scalability.

23. What is fine-tuning in machine learning?

Fine-tuning is the process of taking a pre-trained language model and further training it on a smaller, task-specific dataset to improve its performance on that particular task. This technique allows models to adapt to specific applications while leveraging the knowledge gained during initial training.

24. What is inference in the context of AI models?

Inference is the process of using a trained machine learning model to make predictions or generate outputs on new, unseen data. It is a critical stage in deploying AI models, where the model applies its learned patterns to real-world problems.

25. What is Mixture of Experts (MoE) in neural networks?

Mixture of Experts (MoE) is a type of neural network architecture that combines multiple sub-networks (the "experts"). This allows different parts of the model to specialise in different types of input data, often leading to improved capacity and efficiency by leveraging the strengths of each expert.

26. What is a Neural Processing Unit (NPU)?

A Neural Processing Unit (NPU) is a specialised type of processor designed to accelerate machine learning tasks, particularly neural network computations. NPUs are often found in modern CPUs and mobile chips, enhancing the performance of AI applications on these devices.

27. What is reinforcement learning?

Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by receiving rewards or penalties for its actions in an environment. The agent aims to maximise its cumulative reward, developing strategies that balance exploration and exploitation.

28. What is supervised fine-tuning (SFT)?

Supervised Fine-Tuning (SFT) is a method of fine-tuning a pre-trained language model using a dataset of input-output pairs. The model learns to generate desired outputs based on given inputs under supervision, improving its accuracy and applicability for specific tasks.

29. What are transformers in AI?

Transformers are a neural network architecture that has become highly successful in natural language processing. They are known for their ability to model long-range dependencies in sequential data using self-attention mechanisms, enabling advanced capabilities in tasks like translation, summarisation, and question answering.

30. What are some practical applications of DeepSeek models?

Practical applications of DeepSeek models include customer service automation, content creation, language translation, and data analysis. Their cost efficiency and reasoning capabilities make them suitable for businesses looking to enhance productivity and innovation through AI.