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AI models are becoming more prevalent and developers require appropriate tools to effectively utilise them.
Current approaches to working with LLMs, such as prompting and fine-tuning, are insufficient, since developers do not sufficiently understand how the models produce outputs from their inputs.
To address this, Martian has developed a model mapping technique to turn transformers into programs, allowing developers to understand how models work and make use of them more effectively.
The first application of this technique is the model router, which can determine the best LLM to use for each query and route it in real time to achieve the best performance at the lowest cost.
This is the first commercial application of large-scale AI interpretability and achieves better results than GPT-4 at a lower cost.