6850亿参数混合专家(MoE)架构开源大模型！Deepseek V3全方位客观评测文档处理、逻辑推理、算法编程等多维度的真实能力水平！是卓越还是拉胯？真能超越Claude还是言过其实？#claude

Introduction

This tutorial provides a comprehensive overview of the capabilities and limitations of the Deepseek V3 model, a large-scale open-source model with 685 billion parameters. It focuses on its performance in various tasks, including document processing, logical reasoning, and algorithm programming. Understanding these aspects can help users gauge its utility for specific applications and how it compares to other models like Claude.

Step 1: Understand the Model Specifications

• Deepseek V3 is built on a mixture of experts (MoE) architecture.
• The model consists of 256 expert models.
• Training data is current up to July 2024, making it relatively up-to-date.

Step 2: Test Knowledge Base Cutoff Date

• Verify the knowledge base's cutoff date to ensure the model's responses are relevant and accurate.
• This can be done by asking the model questions about events or information post-cutoff.

Step 3: Evaluate PDF Processing Capabilities

• Test the model's ability to process standard and large PDF files.
• For example, assess how it handles a 605-page PDF.
• Check for accuracy in extracting and interpreting the content.

Step 4: Assess Prompt Following Ability

• Conduct tests to see how well the model adheres to prompt instructions.
• Use specific prompts based on thinking chains to evaluate its performance.
• Document instances of strong adherence versus any weaknesses noticed.

Step 5: Conduct Logical Reasoning Tests

• Create logical reasoning questions to test the model.
• Analyze its responses for completeness and correctness.
• Note that the model may sometimes provide incorrect or incomplete answers.

Step 6: Test Programming and Algorithm Capabilities

• Start with a basic algorithm problem, such as generating prime numbers.
• Evaluate the provided code for optimization and execution efficiency.
• Be prepared to identify areas where the model's output may lack efficiency.

Example Code for Prime Number Generation

def generate_primes(n):
    primes = []
    for num in range(2, n + 1):
        is_prime = True
        for i in range(2, int(num**0.5) + 1):
            if num % i == 0:
                is_prime = False
                break
        if is_prime:
            primes.append(num)
    return primes

Step 7: Explore Other Algorithm Challenges

• Challenge the model with various algorithmic problems.
• Compare the correctness and efficiency of the solutions it provides.
• Document any inconsistencies in performance across different tasks.

Step 8: Implement a Complex Programming Case

• Attempt to create a slightly complex program, such as a Snake game.
• After initial coding, conduct multiple rounds of modifications.
• Identify any logical issues that prevent the program from running successfully.

Step 9: Compare with Claude Model

• Compare the results of the Snake game implementation between Deepseek V3 and Claude.
• Note that Claude may provide a more complete and logically sound code in a single attempt.
• Understand the implications of these differences for practical applications.

Conclusion

Deepseek V3 demonstrates strong capabilities in certain areas, particularly in prompt following and basic algorithm tasks. However, it also reveals significant limitations in logical reasoning and complex programming scenarios. Users should weigh these strengths and weaknesses when considering using this model for specific applications. Future steps could include further testing with different models or exploring enhancements in areas where Deepseek V3 currently falls short.