The Business Case for Open Source Large Language Models: A Deep Dive into Llama-2

Unlocking Competitive Advantage and ROI with a Strategic Business Guide to Deploying Open-Source Language Models like Llama-2 for Cost-Effective Accuracy

Large Language Models (LLMs) like GPT-4 have become indispensable tools for various applications, from chatbots to content summarization; however, these models’ cost and proprietary nature can be a barrier for many businesses.

And here is where open-source alternatives like Llama-2 promise similar capabilities at a fraction of the cost. But how do they stack up in terms of performance and reliability?

This article will help to understand from a less technical but more business-oriented perspective a comprehensive experiment shared by Waleed Kadous from Anyscale, comparing Llama-2 with its more famous counterparts, GPT-3.5-turbo and GPT-4.

I will provide some business-oriented explanations and details about the results that are very enlightening, and they make a compelling case for considering open-source LLMs in your business strategy.

The Experiment: Setting the Stage

The experiment aimed to evaluate the factual accuracy of various LLMs in summarizing news reports. A 3-way verified, hand-labeled set of 373 news report statements was used.

Each LLM had to decide which of the two summaries presented was factually correct.

The models tested included different versions of Llama-2 (7b, 13b, and 70b), GPT-3.5-turbo, and GPT-4.

Key Findings:

1. Factual Accuracy: Llama-2–70b scored an 81.7% accuracy rate, almost as strong as GPT-4’s 85.5% and considerably better than GPT-3.5-turbo’s 67.0%.
2. Cost-Effectiveness: Despite a 19% longer tokenization process, Llama-2 is 30 times cheaper than GPT-4 for equivalent levels of factuality in summarization.
3. Instruction Following: GPT-4 and GPT-3.5 were better at following instructions than their open-source counterparts. However, Llama-2–70b was the best among the Llama models.
4. Ordering Bias: Smaller versions of Llama and GPT-3.5-turbo had severe ordering bias issues, making them less reliable for tasks requiring high factual accuracy.

Why Business Leaders Should Consider Llama-2

Quality and Reliability

The experiment showed that Llama-2–70b is almost on par with GPT-4 regarding factual accuracy. This significant achievement narrows the quality gap between open-source and proprietary LLMs. If you’re a business leader looking for reliable, high-quality language models, Llama-2–70b offers a viable alternative.

Cost-Effectiveness

The cost of running Llama-2 is substantially lower than that of GPT-4. Even when accounting for the 19% longer tokenization process, Llama-2 is 30 times cheaper. This cost advantage can be a game-changer for businesses, especially startups and SMEs.

Openness and Flexibility

Being open-source, Llama-2 offers transparency and flexibility that proprietary models can’t match. You can fine-tune the model to suit your specific needs better, and you’re not locked into a single vendor’s ecosystem.

Practical Tips for LLM Implementation

Implementing a Large Language Model like Llama-2 in your business operations is a technical and strategic decision.

Here’s my Implementation advice on going about it, considering the business implications at each step.

Conduct a Cost-Benefit Analysis

Before diving into any Artificial Intelligence implementation, in particular, related to the “new kid on the block — Generative AI,” it’s crucial to understand the financial implications. A cost-benefit analysis will help you evaluate the ROI (Return on Investment) you can expect from implementing Llama-2.

My Tip: Compare the costs of running Llama-2 versus proprietary models like GPT-4 for your specific use cases. Factor in not just the direct costs but also the potential savings from reduced cloud usage and the value of increased speed and efficiency.

Beware of Ordering Bias

Ordering bias (see link below) can significantly impact the reliability of the model’s output, which in turn can affect customer trust and brand reputation.

My Tip: Conduct A/B testing to identify any inherent biases in how the model responds to queries. If you find that the model has a strong ordering bias, you may need to adjust the prompts or consider using a different version of the model.

Use Fine-Tuned Variants for Specific Tasks

Different business applications may require the model to excel in customer service, content generation, or data analysis.

My Tip: If Llama-2 offers fine-tuned variants optimized for particular tasks, consider using them. This can improve performance and provide a more tailored customer or stakeholder experience.

Address Tokenization Efficiency

The tokenization process can have cost implications, especially at scale. Llama-2’s tokenization is 19% longer, which may affect the overall cost.

My Tip: Work with your technical team to optimize the tokenization process. This could involve batch processing or other techniques to offset the longer tokenization time without compromising output quality.

Plan for Scalability

As your business grows, you’ll need a model that can scale with you. The open-source nature of Llama-2 provides flexibility but also requires a well-thought-out scalability plan.

My Tip: Evaluate the infrastructure requirements for running Llama-2 at scale. Consider cloud solutions that offer easy scalability options and calculate the associated costs.

Legal and Compliance Considerations

Using an open-source model like Llama-2 may have different legal implications than a proprietary one, especially concerning data privacy and intellectual property.

My Tip: Consult with your legal team to ensure that using Llama-2 complies with industry regulations and intellectual property laws. Make sure to read and understand the licensing terms.

By taking a business-oriented approach to implementing Llama-2, you can ensure that the model meets your technical requirements and aligns with your broader business objectives.

OpenSource and the Enterprise Environment

In a groundbreaking move, Microsoft’s CEO, Satya Nadella, revealed at the Inspire conference that a partnership is in place to offer Llama 2 on Microsoft’s Azure cloud platform.

“Both Meta and Microsoft are aligned in our mission to make AI accessible to everyone, and we’re thrilled that Meta is adopting an open stance with Llama 2,” Nadella stated.

To emphasize the significance of this collaboration, Meta published a blog post spotlighting their alliance with Microsoft as a cornerstone of Llama 2’s launch.

So, why does this matter for the business world?

It’s a game-changer. The longstanding debate between proprietary enterprise models and open-source alternatives is becoming increasingly irrelevant. Open-source technology is now synergizing with enterprise solutions in an unprecedented manner, closing the gap that experts like Ilya Sutskever once highlighted and doing so at a pace that has taken the industry by surprise.

In this context, this partnership exemplifies the growing acceptance and integration of open-source Large Language Models like Llama-2 in enterprise settings.

It is compelling for business leaders to seriously consider open-source options when implementing generative AI systems, as they are now backed by major players and available on trusted cloud services.

Conclusion.

The experiment conducted by Waleed Kadous from Anyscale clarifies that Llama-2–70b is a strong contender in LLMs.

As we can see, it offers near-human levels of factual accuracy in summarization tasks at a fraction of the cost of its proprietary counterparts.

For business leaders, the message is clear: Open-source models like Llama-2 are cost-effective and high-quality alternatives that deserve serious consideration.

So, if you’re considering integrating a Large Language Model into your business operations, don’t just look at the big names like OpenAI, to name one… Give open-source models like Llama-2 a chance.

They often offer the best balance of quality, cost-effectiveness, and openness for your needs.

Related Links

The Original Research

• Llama 2 is about as factually accurate as GPT-4 for summaries and is 30X cheaper. The article that inspired me to analyze and provide a business analysis of the case: https://www.anyscale.com/blog/llama-2-is-about-as-factually-accurate-as-gpt-4-for-summaries-and-is-30x-cheaper

Factual Accuracy in Language Models

• Evaluating the Factual Consistency of Large Language Models Through Summarization: A scholarly article that delves into the importance of factual accuracy in language models: Evaluating the Factual Consistency of Large Language Models Through Summarization.

Open Source vs. Proprietary Models

• The Future of LLMs: Proprietary versus Open-Source: An article that explains the pros and cons of open-source and proprietary LLMs, which can help readers understand the significance of using an open-source model like Llama-2: The Future of LLMs: Proprietary versus Open-Source.

Cost-Effectiveness

• The $360K question about Large Language Models Economics: An article that breaks down the costs associated with running different LLMs, supporting the cost-effectiveness argument for Llama-2: The $360K question about Large Language Models Economics.

Open Source Language Models

• A Gentle Introduction to Open Source Language Models: A beginner-friendly guide to open-source language models, which can be helpful for readers unfamiliar with the concept: A Gentle Introduction to Open Source Language Models.

Tokenization in NLP

• Understanding Tokenization in NLP: An article that explains what tokenization is and why it’s crucial in Natural Language Processing (NLP), which can help readers understand the 19% longer tokenization process in Llama-2: Understanding Tokenization in NLP

Llama-2 for enterprise

• Microsoft plans to integrate Llama-2 in Azure: Satya Nadela revealed at the Inspire conference that a partnership is in place to offer Llama-2 on Microsoft’s Azure cloud platform. Meta and Microsoft Introduce the Next Generation of Llama.

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These articles cover a wide range of topics related to Generative AI, from introductions and use cases to exploring its potential and understanding its underlying layers. Happy reading!

Note: This article was published originally on my blog one week ago. You can read my articles first by subscribing for free here.