【南加大研究揭示ChatGPT参数规模新发现：或仅为70亿】
据量子位报道，美国南加州大学的一项最新研究为OpenAI的ChatGPT模型参数规模提供了新的线索。研究团队的三位学者成功破解了未公开的gpt-3.5-turbo模型的嵌入向量维度，结果显示这一维度为4096或4608。这一发现对于理解ChatGPT的规模至关重要，因为据现有开源大模型如Llama和Mistral的经验，当嵌入向量维度为4096时，参数规模通常约为70亿（7B）。
研究指出，保持合适的网络结构比例对于模型性能至关重要。如果偏离这一比例，可能会导致模型过宽或过窄，从而影响其性能。因此，南加大团队推测，除非gpt-3.5-turbo采用了特殊的模型架构，如Mixture of Experts（MoE）设计，否则其参数规模很可能也在70亿左右。这一结论对人工智能领域的研究具有重要启示，为优化大模型设计提供了新的参考点。
OpenAI的ChatGPT自推出以来，因其出色的对话能力和广泛的应用前景引起了全球关注。然而，其具体的技术细节，尤其是参数规模，一直是业界猜测的焦点。南加大团队的这项研究，为解开ChatGPT的神秘面纱提供了重要一步，也为未来AI模型的优化和开发提供了理论依据。
英语如下：
**News Title:** “USC Study Suggests ChatGPT’s Parameter Count May Be Just 7 Billion, Breaking the Mold for Large Models?”
**Keywords:** USC study, ChatGPT parameters, model performance
**News Content:**
**USC Research Reveals New Insight into ChatGPT’s Parameter Scale: Possibly Only 7 Billion**
According to QbitAI, a recent study from the University of Southern California (USC) has shed new light on the parameter size of OpenAI’s ChatGPT model. Researchers from the team successfully decoded the embedding vector dimensions of the unreleased gpt-3.5-turbo model, finding it to be either 4096 or 4608. This discovery is crucial for understanding ChatGPT’s scale, as past experience with open-source large models like Llama and Mistral indicates that a 4096 embedding vector dimension usually corresponds to a parameter count of around 7 billion (7B).
The study emphasizes the importance of maintaining appropriate network architecture proportions for model performance. Deviations from this balance can lead to models being overly wide or narrow, negatively impacting their effectiveness. As a result, the USC team speculates that unless gpt-3.5-turbo employs a specialized architecture, such as a Mixture of Experts (MoE) design, its parameter count is likely around 7 billion. This finding holds significant implications for the AI research community, offering a new reference point for optimizing large model design.
Since its launch, ChatGPT has garnered global attention for its impressive conversational capabilities and broad application prospects. However, the specifics of its technology, particularly its parameter scale, have been a subject of industry speculation. The USC team’s research represents a significant step in demystifying ChatGPT and provides theoretical grounds for optimizing and developing future AI models.
【来源】https://mp.weixin.qq.com/s/y0RQ0aOrHGLzLJKxbyGxMw
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