74 lines
2.8 KiB
Markdown
74 lines
2.8 KiB
Markdown
|
---
|
|||
|
|
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 Llama-3.1-8b-Instruct. 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)**
|
|||
|
|
|
|||
|
|
<!-- - **[twitter](https://arxiv.org/abs/2505.03059)**
|
|||
|
|
- **[blgopost](https://arxiv.org/abs/2505.03059)** -->
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
## 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/Llama-3.1-8B-Instruct-MoAA-SFT")
|
|||
|
|
model = AutoModelForCausalLM.from_pretrained("togethercomputer/Llama-3.1-8B-Instruct-MoAA-SFT")
|
|||
|
|
```
|
|||
|
|
|
|||
|
|
|
|||
|
|
## 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}
|
|||
|
|
}
|
|||
|
|
```
|