BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and
  Deduplication by Introducing a Competitive Large Language Model Baseline
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.
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Guosheng Dong
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2024-08-29T07:21:59.022Z
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guosheng dong
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Da Pan
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Yiding Sun
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2024-08-29T07:26:40.448Z
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Yiding Sun
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Shusen Zhang
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2024-08-29T07:22:31.178Z
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Zheng Liang
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Xin Wu
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Yanjun Shen
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2024-08-29T07:28:16.193Z
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shenyanjun
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Fan Yang
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FanYang
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Haoze Sun
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2025-01-23T15:05:41.512Z
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Alvin Sun
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Tianpeng Li
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2024-08-29T07:29:12.964Z
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Tianpeng Li
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Mingan Lin
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2024-10-14T07:49:00.811Z
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Jianhua Xu
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2024-08-29T07:29:27.331Z
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jianhua Xu
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Yufan Zhang
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Xiaonan Nie
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2024-08-29T07:30:02.122Z
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Xiaonan Nie
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codecaution
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Lei Su
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Bingning Wang
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2024-12-10T15:15:28.995Z
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Daniel Wang
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Wentao Zhang
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Jiaxin Mao
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Zenan Zhou
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2024-08-29T07:31:03.172Z
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Zenan Zhou
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Weipeng Chen
admin_assigned
2024-08-29T07:29:02.819Z
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Wei-Peng Chen
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whenfra
BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and
  Deduplication by Introducing a Competitive Large Language Model Baseline
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.
A data processing pipeline and open-sourced details for training a large language model achieve competitive performance with commercial models.
Large Language Models LLM data processing pipeline pretraining datasets open-source broad collection reweighting pretrained model BaichuanSEED supervised fine-tuning Qwen1.5 Llama3 comprehensive benchmarks mathematics coding