BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and Deduplication by Introducing a Competitive Large Language Model Baseline

guosheng dong, Da Pan, Yiding Sun, zss, Zheng Liang, Xin Wu, shenyanjun, FanYang, Alvin Sun, Tianpeng Li, lma, jianhua Xu, Yufan Zhang, Xiaonan Nie, Lei Su, Daniel Wang, Wentao Zhang, Jiaxin Mao, Zenan Zhou, Wei-Peng Chen

BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and Deduplication by Introducing a Competitive Large Language Model Baseline: 56 upvotes on Hugging Face Daily Papers, #2 of 13 papers on 2024-08-29. Day-by-day upvote history.

The general capabilities of Large Language Models (LLM) highly rely on the composition and selection on extensive pretraining datasets, treated as commercial secrets by several institutions. To mitigate this issue, we open-source the details of a universally applicable data processing pipeline and validate its effectiveness and potential by introducing a competitive LLM baseline. Specifically, the data processing pipeline consists of broad collection to scale up and reweighting to improve quality. We then pretrain a 7B model BaichuanSEED with 3T tokens processed by our pipeline without any deliberate downstream task-related optimization, followed by an easy but effective supervised fine-tuning stage. BaichuanSEED demonstrates consistency and predictability throughout training and achieves comparable performance on comprehensive benchmarks with several commercial advanced large language models, such as Qwen1.5 and Llama3. We also conduct several heuristic experiments to discuss the potential for further optimization of downstream tasks, such as mathematics and coding.

Paper page on Hugging Face · arXiv

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