Ripping off RouteLLM

The context

RouteLLM, a project by LMSYS and Anyscale, is designed to route queries between LLMs. They trained models to convert query embeddings into performance scores, using various data sources including human preferences and GPT-based scoring. This lets you route between LLMs based on the query's difficulting, improving the quality of responses while saving costs.
I have a project where children can chat with AI (thanks to Groq's free services). The main LLM is Gemma 2 9b, but it lets users choose smarter ones if they want. The conundrum I faced was that Llama 3 70b was definitely better in some cases, but not all, and I needed a router to decide when to use it. RouteLLM let me do that.
The goal

Inspired by RouteLLM's models, I figured I could create vectors that could act as routers. These vectors, when dotted with a prompt's embedding, output a LLM's expected performance.
How

Step 1: training data

Take RouteLLM's GPT-augmented dataset. Make a new one that includes the prompt, the LLM, and the LLM's score. (This was done by sampling challenging prompts from the dataset and comparing the reference answer to each model's response.)
Step 2: train in data

Turn the dataset into one dataset per LLM, where the input is the prompt's embedding and the output is the LLM's score. Get gradient descent code from GPT so I can train a "model" - a vector that can be dotted with the embedding - on this. Success.
Step 3: post the results online

Actual vectors
Gemma:
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