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Training a model may be preferable to using a foundation model in certain scenarios that require:
In these cases, training a model from scratch allows for greater control over the model’s architecture, training process, and performance optimization.
While pre-training a model is resource-intensive, requiring significant computational power, time, and data, there are specific scenarios where the benefits outweigh the costs, such as those described above.
Nonetheless, for most enterprise use cases, fine-tuning an existing pre-trained foundation model is the best approach as it offers a practical balance between customization and resource efficiency. Pre-trained models like GPT or Llama 2 have already learned a vast amount of general knowledge, allowing businesses to tailor these models to specific tasks with minimal additional resources. This process significantly reduces the need for extensive computational power and data, making it a practical choice for achieving high-performance results in applications ranging from customer service automation to content generation.