Open large language models (LLMs) have emerged as a compelling and cost-effective alternative to proprietary models like OpenAI’s GPT model family. For anyone making products with AI, open models provide strong enough performance and better data privacy at a lower price point. They can also serve as viable replacements for tools and chatbots like ChatGPT.

The challenges of proprietary models

OpenAI’s ChatGPT chatbot, GPT suite of models (GPT-4o and GPT-4o-mini), and o1 family of models (o1-preview and o1-mini) have dominated the conversation around chatbots and LLMs in recent years. While these proprietary models deliver excellent performance, they come with two major limitations.

First, data privacy – OpenAI discloses very little about how its AI models operate. They haven’t published the model weights, training data, or even the number of parameters for any model since GPT-3. When using OpenAI’s services, users rely on a black-box model on external servers to process potentially sensitive data. With open models, not only can you select a model you understand better, but you also have control over where you deploy it.

Second, cost. Deploying LLMs is an incredibly resource intensive computing task. While proprietary models like the GPT family typically perform well in benchmarks, they aren’t necessarily optimized for cost effectiveness. Not every application requires maximum performance and having a wider range of models to select from allows you to choose the most effective one for the job.

Proprietary models may still be a good choice, particularly during prototyping stages. However, you should also weigh open alternatives before making a selection.

Key consideration in selecting a model

Selecting the right AI model involves assessing several key factors:

What modalities does it need to support? LLMs just handle text, though there are now multimodal models available that can also process images, audio, and video. If you just need a text model, remember, they operate on text fragments called tokens, rather than words or sentences. This determines how they are priced and how performance is measured.

What level of performance does it need to have and what size of model is most appropriate? Larger models typically achieve higher performance on benchmarks but are more costly to run. Depending on the model, the price can vary from around $0.06 per million tokens (approximately 750,000 words) to $5 per million tokens. The price-performance trade-off can really make or break your profit margins. Look at benchmarks to find a few models that could meet your needs then test them with a sample dataset to find the most appropriate model for your needs.

To Know More, Read Full Article @ https://ai-techpark.com/open-source-llms-reshaping-ai/

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