LLMs: Is bigger always better? Small LLMs are punching above their weight.?

Large Language Models (LLMs) have taken the world by storm, with names like GPT-4 by OpenAI , LLaMA2 by Meta , Jurassic-1 Jumbo by AI21 Labs , or Gemini (previously know as Bard) by Google DeepMind , dominating headlines. But are these behemoths always the best choice? In enterprise environment there are more factors to consider than versatility. There are smaller, more focused language models like Granite 13B by IBM , Mistral 7B by Mistral AI , or Flan-T5 3B by Hugging Face .?

LLMs: Powerhouses with potential pitfalls

• Strengths: LLMs boast impressive versatility, addressing diverse tasks from creative writing to code generation. They excel at complex reasoning and learning from massive datasets.
• Weaknesses: Their complexity comes at a cost. LLMs require significant computational resources and are often black boxes, making it difficult to understand their reasoning or identify biases. Additionally, their training on vast datasets can raise ethical concerns.

Usually they provide better results, but costs of use are surprisingly high for those who move from pilots and tests to production inference.

Smaller Models: Faster, cheaper, more explainable

• Strengths: Smaller models offer several advantages. They are generally more lightweight and require less computational power, making them easier to deploy and potentially more cost-effective. Additionally, their smaller size often makes them more transparent and explainable, easier to manage in context of risk, compliance and overall AI governance.
• Weaknesses: While capable, smaller models might not match the sheer breadth and depth of very large LLMs. But used in specific expert niche (for example finance, legal, manufacturing), they can deliver very good results.

Smaller models provide better transparency and trustworthiness, some vendors like IBM for its #Granite model provide indemnification based on confidence they have in quality of data used to train the family of foundation models.

Some are mixing several small LLMs to meet benchmarks of very large models, while maintaining speed and cost efficiency of smaller ones, for example Mixtral 8x7B. The verification what is the best option is still to come.

So, which model is right for you?

The answer depends on your specific needs:

• LLMs are ideal for: Tasks requiring vast knowledge, complex reasoning, or highly creative outputs. However, be prepared for computational demands and potential interpretability challenges. They are also more prone to hallucinations, if you care about taking responsibility for outputs in commercial environment.
• Smaller models shine in: Situations where efficiency, explainability, and cost are critical. In enterprise environment specialization gets priority over creativity. They're also great starting points for experimentation or training on specific domain data.

Choosing the right LLM is all about understanding your needs and priorities. Don't get caught up in the hype – explore both options and find the model that empowers you to achieve your goals. Pay attention to total cost of ownership (model, infrastructure, skills) and compliance/risks when using Generative AI in business.

?What are your experiences with LLMs and smaller models? Share your thoughts in the comments.

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