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Smaug arrives!

We recently released Smaug-72B-v0.1 which has taken first place on the Open LLM Leaderboard by HuggingFace. With an average accuracy of 80.48%, it is the first open-source model to surpass an average score of 80%, and it is nearly 2% better than the next-best open-source model. We also released Smaug-34B-v0.1, the best 34B model at the time of its release.

We created both models using a new fine-tuning technique, DPOP, and new pairwise preference versions of ARC, HellaSwag, and MetaMath. We introduce both the technique and the datasets in our new arXiv paper: https://arxiv.org/abs/2402.13228.

We give theoretical and empirical evidence for a failure mode in the standard DPO loss: on datasets in which the edit distance between pairs of completions is low (such as in math-based datasets), standard DPO loss can lead to a reduction of the model's likelihood of the preferred examples, as long as the relative probability between the preferred and dispreferred classes increases. Using these insights, we design DPO-Positive (DPOP), a new loss function and training procedure which avoids this failure mode. Surprisingly, we also find that DPOP significantly outperforms DPO across a wide variety of datasets and downstream tasks, including datasets with high edit distances between completions. Using DPOP, we create Smaug-34B-v0.1 and Smaug-72B-v0.1, which achieve state-of-the-art open-source performance.

Table of Contents

1. Smaug-72B-v0.1
2. Smaug-34B-v0.1

Smaug-72B-v0.1

Smaug-72B-v0.1 is finetuned directly from moreh/MoMo-72B-lora-1.8.7-DPO and is ultimately based on Qwen-72B.

The license is therefore the Tongyi Qianwen LICENSE AGREEMENT.

Please find the model weights here.

HuggingFace Open LLM Leaderboard Results

Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
80.48	76.02	89.27	77.15	76.67	85.08	78.70

MT-Bench

We ran MT-Bench with the llama-2 conversation template and the system prompt set to the Qwen system prompt. We got the following results in single model mode:

First Turn	Second Turn	Average
8.18	7.34	7.76

We give sample MT-Bench responses in the HuggingFace model card.

Contamination Results

We generate our contamination numbers using https://github.com/swj0419/detect-pretrain-code-contamination/tree/master, with Llama7B as our reference model. Smaug-72B has the following results:

ARC	TruthfulQA	GSM8K
0.20	0.45	1.00

By comparison, MoMo-72B-lora-1.8.7-DPO has the following results:

ARC	TruthfulQA	GSM8K
0.20	0.39	1.00

Note that GSM8K often scores very highly on this contamination suite - we verified this by also running Llama-2-70B:

ARC	TruthfulQA	GSM8K
0.22	0.51	0.89

Smaug-34B-v0.1

Smaug-34B-v0.1 is finetuned directly from bagel-34b-v0.2 and is ultimately based on Yi-34B-200k.

The license is therefore the Yi Series Models Community License Agreement.

Please find the model weights here.

Evaluation Results

Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
77.29	74.23	86.76	76.66	70.22	83.66	72.18

Contamination Results

With reference model jondurbin/bagel-34b-v0.2:

ARC	TruthfulQA	GSM8K
0.08	0.38	0.88

Citation

Please cite the paper if you use data, model, or method in this repo.

@article{pal2024smaug,
  title={Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive},
  author={Pal, Arka and Karkhanis, Deep and Dooley, Samuel and Roberts, Manley and Naidu, Siddartha and White, Colin},
  journal={arXiv preprint arXiv:2402.13228},
  year={2024}
}