Choosing the Right Large Language Model for Your Needs

Large language models (LLMs) like GPT-3 and PaLM have ushered in a new era of AI-generated text. These models are capable of producing surprisingly human-like writing and open up exciting opportunities for businesses. However, with numerous options available, determining which model is the best fit for your specific use case can be challenging. Research has shown that the performance of these models can vary significantly based on the task at hand, emphasizing the importance of matching the model's capabilities to your specific needs ^[3].

Having a basic understanding of how LLMs and deep learning function will greatly assist you in the selection process. Essentially, LLMs are trained on massive datasets of text to generate intelligent responses by predicting the next word in a sentence. The architecture of these models, such as transformers, and their training methods, including self-supervised learning, are crucial to their performance. Familiarity with these concepts allows for a more effective assessment of different models ^[4].

In this guide, we'll dive into the key factors you should consider when selecting an LLM, while also touching on the core deep learning concepts that underpin them. Understanding your needs and the capabilities of these models will empower you to choose the right LLM for your goals.

Defining Your Needs and Use Case

The first step is to clearly define what you want to achieve with an LLM. Consider these questions:

• What are the primary applications? Are you looking for creative content, conversational AI, or perhaps code generation?
• Do you prefer longer, highly coherent text generation, or are concise responses more your style?
• How critical is accuracy for factual answers, especially in domains requiring high precision?
• Does the model need to be tailored to a specialized domain, as fine-tuning can significantly enhance performance in niche areas?

Having clear objectives will guide you in deciding on the size, architecture, and capabilities you need in a model.

Evaluating Model Architecture:

LLMs come with different architectures like GPT, BERT, and BART. Understanding how transformer models process language will help you choose the best structure for your needs. For instance:

• GPT models excel in textual generation, creativity, and open-ended tasks, making them ideal for applications requiring innovative content.
• BERT models are generally better suited for question answering and search tasks due to their bidirectional context understanding.
• BART combines auto-encoding and auto-regressive capabilities, making it particularly effective for summarization and translation tasks.

Assessing Model Size:

As the model size increases into the billions of parameters, so does its ability to generate coherent text. However, the computing power required also scales dramatically. More compact models, around 6 billion parameters, might have some limitations in quality but are often more feasible for various applications. Finding the right balance between text quality, model size, and budget is essential. With careful consideration, you can select a generative model that aligns with your objectives.

We now have a wealth of open-sourced models available, many of which boast over 100 billion parameters. However, more parameters don't always equate to better results. The outcome often hinges on the specific problem domain. Most models are trained on general data sources, necessitating fine-tuning for specific contexts ^[1]. This raises the question: “What's the advantage of fine-tuning a 70B model versus a 7B model?” Generally, the smaller model should already have a fundamental grasp of the English language. Once it's proficient in language, the focus shifts to inputs and outputs.

If we think of AI models as functions, the inputs are your domain data, while the task and output represent the expected results. Different inputs are fed at various stages, with domain data introduced during fine-tuning and the task specified during inference. The output then comes from these two inputs. Therefore, focusing on your inputs rather than solely on model parameters is crucial. Remember, larger models require more complex hardware, which can drive up costs.

Leveraging Fine-Tuning:

Most LLMs greatly benefit from fine-tuning with domain-specific data relevant to your use case. Look for models and platforms that support transfer learning and customization to tailor the model to your needs. Research indicates that models fine-tuned on domain-specific data can achieve significantly improved performance ^[5].

APIs vs Self-Hosted Models:

Deciding between APIs and self-hosted models is a significant choice. Three main factors typically influence this decision: compliance and legal requirements like HIPAA, data security, and cost.

While APIs offer a pay-as-you-go model, running similar solutions on a self-hosted infrastructure can be challenging. For instance, if it were a microservice with serverless deployment and billing, it would have no upfront costs. However, regarding LLMs, setting up a machine and running inference on demand with self-hosted options can be cumbersome and inefficient. In simple terms, serverless for LLMs is only feasible when using smaller models that can run on containers (and yes, it is indeed possible to run models on containers). Choosing the right model, whether it's a specific one or a larger one, blends art and science.

Assessing Vendor Reputation & Responsibility:

When working with a third-party LLM vendor, it's crucial to ensure they have a strong reputation and a history of developing quality models responsibly. Investigate their capabilities, support channels, and their commitment to AI ethics.

Responsibility and Ethics:

With an open-source stack, you can define content filters, ethical guidelines, and data practices. Conversely, proprietary models often reflect the values of their creators, necessitating careful consideration of the implications of their use.

Start Your LLM Journey Today:

With clear objectives, an understanding of model capabilities, and a thoughtful selection process, you'll be poised to find the ideal large language model to elevate your AI initiatives. To pinpoint a suitable model, it's essential to grasp the problem you're aiming to solve (a well-understood problem is already halfway solved). Once the model is selected, having clean, contextual domain data that meets quality standards addresses the next 30%. The remaining 50% comes from practices like prompt engineering and tuning the model with hyperparameters. In our experience, we've achieved similar results for large models like OpenAI's GPT-3.5 using smaller models like Llama2-7B through calibration and hyperparameter tuning. We even managed to run these models (fine-tuning + inference) on consumer-grade machines using techniques like LoRa and deploying them on smaller devices with quantization, which significantly reduced the model's size. Remember, the best model isn't always the largest model. So, don't get too caught up in the name of LLMs... :) See you in the next blog!