๐ Journey into the AI Frontier: DeepSeek-R1 vs Llama 2 on Windows with Ollama
As an AI enthusiast, I recently embarked on an exciting exploration of two powerful language models: DeepSeek-R1 and Llama 2. Running these models locally on Windows using Ollama revealed fascinating insights into their capabilities, particularly in mathematical reasoning. Let me share my hands-on experience! ๐ค
๐ ๏ธ Setup and Installation
๐น Download Ollama
Ollama is available for macOS, Linux, and Windows. Follow these steps to install it:
1๏ธโฃ Go to the official Ollama download page
๐ Download Ollama
2๏ธโฃ Select your operating system (macOS, Linux, Windows)
3๏ธโฃ Click on the Download button
4๏ธโฃ Install it following the system-specific instructions
Getting started with Ollama on Windows was surprisingly straightforward. After installing WSL2 (Windows Subsystem for Linux), I pulled both models using simple commands:
ollama pull deepseek
ollama pull llama2
๐งฎ Mathematical Reasoning Face-off
To evaluate their mathematical prowess, I presented both models with identical problems. The results were eye-opening!
Problem 1: Quadratic Equation
Question: Solve for x: 2xยฒ + 5x - 12 = 0
DeepSeek-R1's Response:
Let me solve this step by step:
1) Using the quadratic formula: x = [-b ยฑ โ(bยฒ - 4ac)] / 2a
where a = 2, b = 5, and c = -12
2) Plugging in the values:
x = [-5 ยฑ โ(25 - 4(2)(-12))] / 2(2)
x = [-5 ยฑ โ(25 + 96)] / 4
x = [-5 ยฑ โ121] / 4
x = [-5 ยฑ 11] / 4
3) Therefore:
x = (-5 + 11) / 4 = 6/4 = 1.5
or
x = (-5 - 11) / 4 = -16/4 = -4
Answer: x = 1.5 or x = -4
Llama 2's Response:
Using quadratic formula:
x = [-5 ยฑ โ(25 - 4(2)(-12))] / 4
x = [-5 ยฑ โ121] / 4
x = -4 or 1.5
๐ Key Observations
-
Thinking Process ๐ง
- DeepSeek-R1 consistently showed its work with clear, step-by-step explanations
- Llama 2 often jumped to conclusions, though usually correct ones
-
Presentation ๐
- DeepSeek-R1 formatted solutions with numbered steps and proper mathematical notation
- Llama 2 tended to be more concise but sometimes at the expense of clarity
-
Accuracy โ
- Both models provided correct answers
- DeepSeek-R1's detailed approach made it easier to verify results
- Llama 2's brevity, while efficient, could leave some users wanting more explanation
๐ก Real-world Applications
This comparison revealed that DeepSeek-R1 might be particularly valuable for:
- Educational contexts where step-by-step explanations are crucial
- Complex problem-solving scenarios requiring detailed reasoning
- Learning environments where process transparency is as important as the final answer
Meanwhile, Llama 2's concise approach could be preferable for:
- Quick calculations and verifications
- Situations where computational speed is prioritized
- Users who are already familiar with the underlying concepts
๐ฏ Conclusion
While both models demonstrated strong mathematical capabilities, DeepSeek-R1 stood out for its methodical approach and clear explanation of reasoning. Its step-by-step problem-solving strategy makes it particularly suitable for educational purposes and complex problem-solving scenarios.
The experience of running these models locally through Ollama on Windows was smooth and efficient, opening up exciting possibilities for AI-assisted learning and problem-solving right on our personal computers.
Whether you're an educator, student, or AI enthusiast, these tools represent a significant step forward in making advanced AI capabilities accessible for local use. The future of AI-assisted learning looks bright indeed! โจ
Following are the abilities to think, solve complex problems
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