[GH-ISSUE #36] Training data distribution #22

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opened 2026-10-26 21:01:47 +00:00 by bsakaguchi · 1 comment
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Originally created by @pluiez on GitHub (Jan 17, 2024).
Original GitHub issue: https://github.com/deepseek-ai/DeepSeek-LLM/issues/36

Hi, the paper is very detailed in most aspects, but the training data is not mentioned in as much detail.

Specifically, I am interested in the following:

  • The composition of the training dataset, including the types of data (e.g., text, code, images) and the sources of the data.
  • How the sampling ratio for each subset is determined, e.g., which principle is followed.
Originally created by @pluiez on GitHub (Jan 17, 2024). Original GitHub issue: https://github.com/deepseek-ai/DeepSeek-LLM/issues/36 Hi, the paper is very detailed in most aspects, but the training data is not mentioned in as much detail. Specifically, I am interested in the following: - The composition of the training dataset, including the types of data (e.g., text, code, images) and the sources of the data. - How the sampling ratio for each subset is determined, e.g., which principle is followed.
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@luofuli commented on GitHub (Feb 4, 2024):

Hi, the paper is very detailed in most aspects, but the training data is not mentioned in as much detail.

Specifically, I am interested in the following:

  • The composition of the training dataset, including the types of data (e.g., text, code, images) and the sources of the data.
  • How the sampling ratio for each subset is determined, e.g., which principle is followed.

The details we can reveal so far are already in the paper.

<!-- gh-comment-id:1925799485 --> @luofuli commented on GitHub (Feb 4, 2024): > Hi, the paper is very detailed in most aspects, but the training data is not mentioned in as much detail. > > Specifically, I am interested in the following: > > * The composition of the training dataset, including the types of data (e.g., text, code, images) and the sources of the data. > * How the sampling ratio for each subset is determined, e.g., which principle is followed. The details we can reveal so far are already in the paper.
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deepseek-ai/DeepSeek-LLM#22
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