Llama 3 vs Mistral: RAG Application Performance Comparison

Introduction to Llama 3 and RAG Applications

The artificial intelligence landscape is constantly evolving, with new models and technologies emerging at a rapid pace. Recently, Meta released Llama 3, a new language model that has garnered significant attention in the AI community. According to initial benchmarks, the Llama 3 8 billion parameter model is said to outperform both Mistral and Gamma 7 billion models. However, benchmarks don't always tell the full story, especially when it comes to real-world applications.

In this article, we'll dive deep into a practical comparison between Llama 3 and Mistral models, specifically in the context of a Retrieval-Augmented Generation (RAG) application. We'll explore how these models perform when tasked with answering specific questions based on provided context, and compare their outputs to determine which model might be more suitable for RAG implementations.

Setting Up the RAG Application

Before we delve into the comparison, let's briefly outline the setup of our RAG application:

1. Required Libraries: We start by installing and importing necessary libraries, including LangChain for PDF processing.

2. Document Loading: The application loads documents that will serve as the knowledge base for our questions.

3. Vector Database: We initialize a vector database to store and efficiently retrieve relevant information.

4. Prompt Template: A template is defined to guide the model in providing precise responses based on the given context.

5. Hardware: The tests are run on an A100 GPU, though it's noted that quantized versions of the models could run on systems with less powerful GPUs, such as those with 16GB of VRAM.

Comparing Mistral and Llama 3

To compare the performance of Mistral and Llama 3, we'll use a specific question about video game awards. The question we'll pose to both models is:

"How many awards did Elden Ring win and did it win the game of the year award?"

Let's examine how each model performs in answering this question.

Mistral's Performance

When we posed the question to the Mistral model, the results were somewhat disappointing. Despite having access to the correct information in the context, Mistral's response was limited and not particularly helpful:

• The model provided a couple of links to Metacritic.
• It failed to directly answer either part of the question.
• The response did not mention the number of awards or specifically address the game of the year award.

This outcome suggests that Mistral, at least in this instance, struggled to extract and synthesize the relevant information from the provided context.

Llama 3's Performance

After resetting the runtime and loading the Llama 3 8 billion parameter model, we posed the same question. Llama 3's performance showed a noticeable improvement:

• The model correctly identified that Elden Ring won the Game of the Year award.
• It mentioned that the game won awards at The Game Awards 2023.
• However, like Mistral, it did not provide the specific number of awards (324) that was available in the context.

While Llama 3 didn't provide a complete answer, it did perform better than Mistral by correctly addressing part of the question and providing more relevant information.

Comparing with GPT-4

To provide a benchmark for high-end performance, we also tested the same question with GPT-4. The results were significantly better:

• GPT-4 correctly stated that Elden Ring won 324 Game of the Year awards.
• It confirmed that the game did win the Game of the Year award at the 23rd Game Developers Choice Awards.
• The answer was comprehensive, addressing both parts of the question accurately.

This comparison highlights that while Llama 3 shows improvement over Mistral, there's still a considerable gap between these 8 billion parameter models and more advanced models like GPT-4.

Analysis of the Results

Based on this experiment, we can draw several conclusions:

1. Llama 3 Outperforms Mistral: In this RAG application scenario, Llama 3 demonstrated superior performance compared to Mistral. It provided more relevant information and partially answered the question correctly.

2. Room for Improvement: Both Llama 3 and Mistral fell short of providing a complete and accurate answer, indicating that there's still significant room for improvement in these 8 billion parameter models.

3. Context Utilization: Neither Llama 3 nor Mistral fully utilized the context provided, missing key details like the specific number of awards. This suggests that improvements in context comprehension and information extraction are needed.

4. GPT-4's Superiority: The test with GPT-4 shows that more advanced models still hold a significant advantage in tasks requiring detailed information extraction and synthesis.

5. Potential for RAG Applications: While not perfect, Llama 3's performance suggests it could be a better choice for RAG applications compared to Mistral, especially when considering the 8 billion parameter versions.

Implications for AI Development and Applications

The results of this comparison have several implications for AI development and applications:

1. Rapid Progress in Open-Source Models

The improvement shown by Llama 3 over Mistral demonstrates the rapid progress being made in open-source language models. This progress is narrowing the gap between freely available models and proprietary ones, potentially democratizing access to advanced AI capabilities.

2. Importance of Real-World Testing

While benchmarks are useful, this experiment highlights the importance of testing models in real-world scenarios. Performance in practical applications can differ from benchmark results, emphasizing the need for comprehensive evaluation methods.

3. Refinement of RAG Techniques

The experiment shows that even advanced models can struggle with fully utilizing provided context. This suggests a need for further refinement of RAG techniques, possibly including improved methods for context integration and information extraction.

4. Tailored Model Selection

The performance difference between models underscores the importance of selecting the right model for specific applications. Developers should consider factors like task complexity, required accuracy, and computational resources when choosing a model.

5. Continuous Improvement in AI Capabilities

The gap between 8 billion parameter models and GPT-4 indicates that there's still significant potential for improvement in AI capabilities. This gap may drive further research and development in model architecture, training techniques, and scaling methods.

Practical Applications and Future Directions

The insights gained from this comparison can guide developers and researchers in several ways:

Enhancing RAG Systems

Developers working on RAG systems might consider using Llama 3 as a foundation, given its better performance. However, they should also focus on improving context utilization and information extraction capabilities.

Hybrid Approaches

Given the strengths and limitations of different models, exploring hybrid approaches that combine multiple models or techniques could yield better results in complex applications.

Fine-Tuning for Specific Tasks

While general-purpose models like Llama 3 show promise, fine-tuning these models for specific tasks or domains could potentially bridge the performance gap with more advanced models like GPT-4.

Scalability and Efficiency

As models continue to improve, research into making larger models more efficient and scalable will be crucial. This includes work on model compression, quantization, and optimized inference techniques.

Ethical Considerations

As these models become more capable, it's important to consider the ethical implications of their use, including issues of bias, misinformation, and responsible AI deployment.

Conclusion

Our comparison of Llama 3 and Mistral in a RAG application scenario has provided valuable insights into the current state of open-source language models. While Llama 3 demonstrated superior performance compared to Mistral, both models still have room for improvement, especially when compared to more advanced models like GPT-4.

This experiment underscores the rapid progress being made in AI development, particularly in the realm of open-source models. It also highlights the importance of real-world testing and the need for continued research and development in areas such as context utilization, information extraction, and model efficiency.

As we move forward, the AI community will likely focus on bridging the performance gap between these models and more advanced ones, while also addressing challenges related to scalability, efficiency, and ethical deployment. The future of AI looks promising, with models like Llama 3 paving the way for more accessible and capable artificial intelligence systems.

For developers and researchers working on RAG applications or similar AI-driven systems, this comparison serves as a valuable reference point. It suggests that Llama 3 could be a strong candidate for current projects, while also indicating areas where further improvements and innovations are needed.

As the field of AI continues to evolve at a rapid pace, staying informed about the latest developments and conducting practical evaluations will be crucial for anyone working in this exciting and transformative field. The journey from Mistral to Llama 3 represents just one step in the ongoing evolution of AI technology, with many more exciting developments surely on the horizon.

Article created from: https://youtu.be/sbKz-f05QZY?si=io5x9iELIYL8IQLJ