togethercomputer
/

Llama-3.1-8B-Instruct-MoAA-SFT

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 tags: []
 ---
+## Model Description
+This is the SFT model 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 [optional]
+```
+@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}
+}
+```