README.md

---
library_name: transformers
tags: []
---




## Model Description

This is the SFT model in our Mixture of Agents Alignment (MoAA) pipeline. This model is tuned on the Gemma-2-9b-it. MoAA is an approach that leverages collective intelligence from open‑source LLMs to advance alignment.

Two mains stages are involved in our MoAA method. In the first stage, we employ MoA  to produce high-quality synthetic data for supervised fine-tuning. In the second stage, we combines multiple LLMs as a reward model to provide preference annotations. 

Some key takeaways of our work:



- 📈**Alignment pipeline that actually works** Our MoAA method sends Llama‑3.1‑8B‑Instruct’s Arena‑Hard **19 → 48** and Gemma-2-9B-it **42→56**, handily beating GPT‑4o‑labeled sets at the time. 

- 🏆**Ensembled rewards > single critics** An MoA reward model with dynamic criteria filtering edges out competitive ArmoRM on MT‑Bench & Arena‑Hard—all while staying 100 % open source. 

- 🚀**Self‑improvement unlocked** Fine‑tune the strongest model inside the ensemble on MoAA data and it *surpasses its own teachers*—evidence that open models can push past proprietary ceilings without external supervision.


## Model Sources


For more details refer to 

- **[Paper](https://arxiv.org/abs/2505.03059)**

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## How to Get Started with the Model

Use the code below to get started with the model.

Run inference like this:
```
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("togethercomputer/gemma-2-9b-it-MoAA-SFT")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/gemma-2-9b-it-MoAA-SFT")
```

## Training Details

## Training Data

Training data are located here: https://huggingface.co/datasets/togethercomputer/MoAA-SFT. 
We subsample from two widely-used open-source instruction tuning datasets: UltraFeedback and UltraChat. Our subsampling strategy involves utilizing the entire UltraFeedback dataset and randomly selecting 5,000 samples from UltraChat.
We use MoA to generate responses. The proposers used in our study are WizardLM-2-8x22b, Gemma-2-7b-it, Qwen-2-72b-Instruct, and Llama-3.1-70b-Instruct, while Qwen-1.5-110b-Instruct serves as the aggregator.


## Evaluation & Performance

Refer to [Paper](https://arxiv.org/abs/2505.03059) for metrics.





## Citation
```
@article{wang2025improving,
title   = {Improving Model Alignment Through Collective Intelligence of Open-Source LLMS},
author  = {Junlin Wang and Roy Xie and Shang Zhu and Jue Wang and Ben Athiwaratkun and Bhuwan Dhingra and Shuaiwen Leon Song and Ce Zhang and James Zou},
year    = {2025},
journal = {arXiv preprint arXiv: 2505.03059}
}
```