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English(EN) I pre-trained a 700m on 18B tokens optimized for Python and Wikitext | TheOneWhoWill/Shibai-700M-Base · Hugging Face

新的700M参数模型Shibai-700M-Base在18B token上进行了训练

一位名为TheOneWhoWill的用户预训练了一个名为Shibai-700M-Base的7亿参数语言模型。该模型在180亿token上进行了训练,并针对Python和Wikitext进行了优化,计划进一步在基于docstring的Python代码上进行训练。该模型设计用于下一个token预测而非聊天,并且被认为比GPT-2有显著提升。 AI

影响 此次发布为Python代码生成等特定任务提供了一个新的、可能更高效的模型,丰富了开源LLM的多样化格局。

排序理由 该集群描述了一位独立开发者预训练和发布了一个新的、小规模的语言模型,这属于研究范畴。[lever_c_demoted from research: ic=1 ai=1.0]

在 r/LocalLLaMA 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的700M参数模型Shibai-700M-Base在18B token上进行了训练

本文如何被排名

Signal score
0 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
该集群描述了一位独立开发者预训练和发布了一个新的、小规模的语言模型,这属于研究范畴。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
model release
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
62 days old
Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/TheOneWhoWil ·

    我在180亿token上预训练了一个700m模型,针对Python和Wikitext进行了优化 | TheOneWhoWill/Shibai-700M-Base · Hugging Face

    <table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1va6dvv/i_pretrained_a_700m_on_18b_tokens_optimized_for/"> <img alt="I pre-trained a 700m on 18B tokens optimized for Python and Wikitext | TheOneWhoWill/Shibai-700M-Base · Hugging Face" src="https://external-…