# Plano Orchestrator

Plano-Orchestrator is a family of 4B and 30B-A3B routing and orchestration models designed for multi-agent systems. It analyzes user intent and conversation context to make precise routing decisions, excelling at multi-turn context understanding, multi-intent detection, and context-dependent routing.

This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.

## Getting started

1. Install Axolotl following the installation guide.

2. Install Cut Cross Entropy to reduce training VRAM usage.

3. Run the finetuning example:

       axolotl train examples/plano/plano-4b-qlora.yaml

This config uses about 5.1 GiB VRAM. Let us know how it goes. Happy finetuning! 🚀

### Orchestration Prompt

Plano-Orchestrator uses a specific orchestration prompt format for routing/agent decisions. Please check the official model card for proper prompt formatting and the `ORCHESTRATION_PROMPT` template.

### Tips

- To use the larger Plano-Orchestrator-30B-A3B MoE model, simply change `base_model: katanemo/Plano-Orchestrator-30B-A3B` in the config and enable multi-GPU training if needed.
- You can run a full finetuning by removing the `adapter: qlora` and `load_in_4bit: true` from the config.
- Read more on how to load your own dataset at docs.
- The dataset format follows the OpenAI Messages format as seen here.

## Optimization Guides

Please check the Optimizations doc.

## Related Resources

- Plano GitHub
- Axolotl Docs
- Axolotl Website
- Axolotl GitHub
- Axolotl Discord