Bad investment IMHO. Mistral was started by people who cheated on benchmarks with their Llama 1. It showed as they had the head start but fell far behind Gemini, DeepSeek and Qwen teams.
And have the chat history end up as an input field instead of each request response being prepared individually for the LLM endpoint?
I'm new to the library but from what I can see the Chat adapter will do this automatically if I use the forward call
Quoting the docs: "Though rarely needed, you can write custom LMs by inheriting from dspy.BaseLM. Another advanced layer in the DSPy ecosystem is that of adapters, which sit between DSPy signatures and LMs. A future version of this guide will discuss these advanced features, though you likely don't need them."
I could be wrong, I could be looking for complexity where there is none.
Have a fundamentally misunderstood how this all works, it sometimes feels like I have?
My go to framework. I wish we can use global metrics in DSPy, for examples, F1 score over the whole evaluation set (instead of a single query at the moment). The recent async support has been life saver.
