74 lines
2.8 KiB
Markdown
74 lines
2.8 KiB
Markdown
---
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library_name: transformers
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tags: []
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---
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## Model Description
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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.
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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.
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Some key takeaways of our work:
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- 📈**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.
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- 🏆**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.
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- 🚀**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.
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## Model Sources
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For more details refer to
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- **[Paper](https://arxiv.org/abs/2505.03059)**
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<!-- - **[twitter](https://arxiv.org/abs/2505.03059)**
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- **[blgopost](https://arxiv.org/abs/2505.03059)** -->
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## How to Get Started with the Model
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Use the code below to get started with the model.
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Run inference like this:
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```
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("togethercomputer/Llama-3.1-8B-Instruct-MoAA-SFT")
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model = AutoModelForCausalLM.from_pretrained("togethercomputer/Llama-3.1-8B-Instruct-MoAA-SFT")
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```
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## Training Data
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Training data are located here: https://huggingface.co/datasets/togethercomputer/MoAA-SFT.
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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.
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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.
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## Evaluation & Performance
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Refer to [Paper](https://arxiv.org/abs/2505.03059) for metrics.
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## Citation
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```
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@article{wang2025improving,
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title = {Improving Model Alignment Through Collective Intelligence of Open-Source LLMS},
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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},
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year = {2025},
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journal = {arXiv preprint arXiv: 2505.03059}
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}
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``` |