rorf 0.1.2

No project description provided

RoRF - Routing on Random Forests

RoRF is a framework for training and serving random forest-based LLM routers.

Our core features include:

• 12 pre-trained routers across 6 model pairs and 2 embedding models (jinaai/jina-embeddings-v3, voyageai/voyage-large-2-instruct) that reduce costs while either maintaining or improving performance.
• Our pre-trained routers outperform existing routing solutions, including open-source and commercial offerings.

Installation

PyPI

pip install rorf

Source

git clone https://github.com/Not-Diamond/RoRF
cd RoRF
pip install -e .

Quickstart

We adopt RouteLLM's Controller to allow users to replace their existing routing setups with RoRF. Our Controller requires a router (available either locally or on Huggingface Hub) that routes between model_a (usually stronger/expensive) and model_b (usually weaker/cheaper). Our release includes 6 model pairs between different models and providers as described in Model Support.

from rorf.controller import Controller

router = Controller(
    router="notdiamond/rorf-jina-llama31405b-llama3170b",
    model_a="llama-3.1-405b-instruct",
    model_b="llama-3.1-70b-instruct",
    threshold=0.3,
)

recommended_model = router.route("What is the meaning of life?")
print(f"Recommended model: {recommended_model}")

We also provide a threshold parameter that determines the percentage of calls made to each model, allowing users to decide their own cost vs performance tradeoffs.

Training RoRF

We include our training framework for RoRF so that users can train custom routers on their own data and model pairs. trainer.py is the entry-point for training, and run_trainer.sh provides an example command to train a model router for llama-3.1-405b-instruct vs llama-3.1-70b-instruct on top of Jina AI's embeddings.

Motivation

Our experiments show that:

• Routing between a pair of strong and weak models can reduce costs while maintaining the strong model's performance.
• Routing between a pair of two strong models can reduce costs while outperforming both individual models.