OpenPipe is a fully-managed fine-tuning platform for developers that has already saved over $2M in inference costs for its users. They have recently released a stronger variant of their recommended model, Mistral 7B, called Mistral 7B Fine-Tune Optimized. This new model outperforms GPT-4 in multiple customer tasks and has been carefully optimized for instruction understanding and reasoning ability. The process of fine-tuning allows the model to learn specific tasks and develop efficient strategies for solving them. OpenPipe has created and evaluated multiple fine-tuned models to determine their performance and has found that model merging can produce even stronger models. They have validated the performance of their merge model on new tasks and are now making Mistral Fine-Tune Optimized their new default base model.
| Signal | Change | 10y horizon | Driving force |
|---|---|---|---|
| OpenPipe releases Mistral 7B Fine-Tune Optimized | Improvement in fine-tuned models | More efficient and effective fine-tuned models | Customer demand for cost and time savings |
| Fine-tuned models outperform GPT-4 | Improved performance of fine-tuned models | Increased usage of fine-tuned models | Desire for more specialized and efficient models |
| Model merging results in stronger models | Effectiveness of model merging | Widespread use of model merging to create stronger models | Advancements in deep learning techniques |
| Mistral Fine-Tune Optimized becomes new default base model | Evolving default base models | Continued development of stronger, faster, and cheaper base models | Improvement in base model capabilities |
| Student model trained on data generated by a teacher model can exceed performance of teacher model | Potential for student models to outperform teacher models | Increased applications of training student models on teacher model outputs | Regularization and improved generalization techniques |