Unlock the Power of OpenAI with RAG

Unlock the Power of OpenAI with RAG

Table of Contents:

1. Introduction
2. The Debate: Open AI Models vs. Open Source Models
3. The Benefits of Smaller Specialized Models 3.1. Cost-effectiveness and Efficiency 3.2. Practical Adaptability 3.3. Integration into Existing Workflows 3.4. Human in the Loop Review
4. Evaluating the Accuracy of Open Source Models 4.1. The Need for Clear Benchmark Tests 4.2. The Limited Availability of Fine-Tuned LLMs
5. Introducing the Benchmark Test and Fine-Tuned LLMs 5.1. Creating the Benchmark Test Dataset 5.2. Building the Best Models: 1 Billion to 3 Billion Parameters
6. Results: Accuracy and Effectiveness of Smaller Models 6.1. Extractive Q&A Accuracy 6.2. Challenges in Math and Logic
7. The Performance of Larger Models: 7 Billion Parameters
8. Key Takeaways and Future Improvements
9. Learn More About LL Mware

The Benefits of Open Source Models for Retrieval Augmented Generation

Introduction

The question of whether open AI models or open source models are more effective for retrieval augmented generation (RAG) is a topic of much debate in the field of artificial intelligence. This video aims to shift the perspective on open source models and highlight their unique advantages. Rather than viewing open source as outdated, it is portrayed as a practical and efficient solution, akin to an SUV in the world of AI.

The Debate: Open AI Models vs. Open Source Models

The debate surrounding open AI models and open source models is often framed in terms of a sleek sports car versus a humble rickshaw. Open AI models are seen as cutting-edge technology, while open source models are considered the domain of hobbyists. However, this video seeks to challenge this viewpoint and present open source as a different Type of solution—a reliable and adaptable SUV.

The Benefits of Smaller Specialized Models

One of the key arguments in favor of open source models, particularly smaller specialized models in the range of 1 billion to 7 billion parameters, is their substantial benefits. These models offer significant cost savings, both in terms of inference and training, often one to three orders of magnitude cheaper than large proprietary models. They are also faster and easier to fine-tune, adapt to specific domains or workflows, and integrate into existing enterprise processes.

Evaluating the Accuracy of Open Source Models

The accuracy of open source models is a crucial aspect to consider when evaluating their effectiveness. However, the video highlights the lack of clear benchmark tests focused on Context-specific retrieval augmented generation and the limited availability of fine-tuned models within the 1 billion to 7 billion parameter range.

Introducing the Benchmark Test and Fine-Tuned LLMs

To address the gap in benchmark tests and fine-tuned models, the Creators of this video have developed a benchmark test dataset. Comprising 200 questions from various domains such as finance, legal, and technical topics, the dataset enables the evaluation of accuracy in retrieval augmented generation. Additionally, they have built the best models in the 1 billion to 3 billion parameter range, which are accessible on the LL Mware Hugging Face page.

Results: Accuracy and Effectiveness of Smaller Models

The results of applying the benchmark test to the smaller models demonstrate their efficacy in extractive Q&A tasks. With accuracy levels ranging from 80% to over 90%, these models prove to be reliable in providing accurate information. However, math and logic tasks pose challenges for smaller models, exhibiting lower accuracy levels. The performance of larger models, with 7 billion parameters, also shows an increase in accuracy but still falls short in math and logic tasks.

Key Takeaways and Future Improvements

In conclusion, this video emphasizes the suitability of open source models in retrieval augmented generation tasks, particularly in the 1 billion to 7 billion parameter range. With accuracy levels surpassing 80% and even reaching 95% in some cases, these models offer a practical and cost-effective solution for integrating AI into existing enterprise workflows. While improvements and advancements are expected, the Current state-of-the-art open source models provide a strong foundation for implementing efficient AI systems.

Learn More About LL Mware

For further information on this project, including ongoing research and updates, discover LL Mware on Hugging Face, GitHub, YouTube, and Medium platforms.

Highlights:

• Open source models offer unique advantages in retrieval augmented generation.
• Smaller specialized models (1 billion to 7 billion parameters) are cost-effective and adaptable.
• Clear benchmark tests and fine-tuned models are essential for evaluating accuracy.
• The benchmark test dataset and LL Mware models enhance accuracy assessment.
• Smaller models perform well in extractive Q&A but face challenges in math and logic tasks.
• Larger models (7 billion parameters) Show improved accuracy but still struggle in some areas.
• Open source models will Continue to improve, offering practical AI solutions.

FAQ:

Q: What are the benefits of open source models for retrieval augmented generation (RAG)? A: Open source models, especially smaller specialized models, provide cost-effectiveness, adaptability, and practical integration into existing workflows.

Q: How can the accuracy of open source models be evaluated? A: The video highlights the need for clear benchmark tests and fine-tuned models specifically designed for retrieval augmented generation tasks.

Q: Are smaller open source models accurate in extractive Q&A tasks? A: Yes, smaller models in the 1 billion to 7 billion parameter range demonstrate high accuracy levels, typically exceeding 80% and even reaching 95% in some cases.

Q: What challenges do smaller models face in math and logic tasks? A: Smaller models struggle in math and logic tasks, showing lower accuracy levels compared to extractive Q&A tasks. However, larger models perform better in this regard.

Q: Will open source models continue to improve in the future? A: Yes, the creators of this video anticipate further advancements in open source models for retrieval augmented generation, making them even more effective and efficient.