Kyutai Labs 发布 Helium-1 预览版：专为边缘和移动环境量身定制的 2B 参数多语言基础 LLM

边缘和移动设备对人工智能模型的日益依赖凸显了重大挑战。平衡计算效率、模型大小和多语言功能仍然是一个持续存在的障碍。传统的大语言模型 (LLM) 虽然功能强大，但通常需要大量资源，这使得它们不太适合智能手机或物联网设备等边缘应用程序。

Highlighting the challenges faced by AI models on edge and mobile devices, a new 2-billion parameter multilingual base LLM has been released by Kyutai Labs. Named Helium-1 Preview, the model is designed to perform comparably or better than models like Qwen 2.5 (1.5B), Gemma 2B, and Llama 3B, despite being smaller and more efficient.

Kyutai Labs 发布了新的 20 亿参数多语言基础 LLM，强调了边缘和移动设备上的 AI 模型所面临的挑战。该模型名为 Helium-1 Preview，其设计性能与 Qwen 2.5 (1.5B)、Gemma 2B 和 Llama 3B 等模型相当或更好，尽管更小、更高效。

Released under the CC-BY license, Helium-1 aims to fill critical gaps in accessibility and practical deployment. Its focus on multilingual capabilities and edge-optimized design makes it particularly valuable for applications requiring language diversity and deployment in environments with limited computational resources.

Helium-1 在 CC-BY 许可下发布，旨在填补可访问性和实际部署方面的关键空白。它对多语言功能和边缘优化设计的关注使其对于需要语言多样性和在计算资源有限的环境中部署的应用程序特别有价值。

Key Technical Features and Advantages

主要技术特点和优势

Helium-1 Preview incorporates several technical features that enable its impressive performance. These include:

Helium-1 Preview 融合了多项技术特性，使其具有令人印象深刻的性能。这些包括：

Transformer architecture: Helium-1 is built upon the powerful transformer architecture, renowned for its ability to handle sequential data like natural language with self-attention mechanisms.

Transformer 架构：Helium-1 基于强大的 Transformer 架构构建，该架构以其通过自注意力机制处理自然语言等顺序数据的能力而闻名。

Multilingual training: The model is trained on a massive multilingual dataset covering over 100 languages, enabling it to handle diverse language inputs and generate responses in multiple languages.

多语言训练：该模型在涵盖 100 多种语言的海量多语言数据集上进行训练，使其能够处理不同的语言输入并生成多种语言的响应。

Edge-optimized design: Helium-1 is specifically designed to be deployed on edge and mobile devices with limited computational resources. Its compact size and efficient architecture ensure optimal performance in these constrained environments.

边缘优化设计：Helium-1 专门设计用于部署在计算资源有限的边缘和移动设备上。其紧凑的尺寸和高效的架构可确保在这些受限环境中实现最佳性能。

Performance and Observations

表现和观察

Initial evaluations of Helium-1 show strong performance on multilingual benchmarks. The model often surpasses or matches models like Qwen 2.5 (1.5B), Gemma 2B, and Llama 3B, demonstrating the effectiveness of its training strategies and optimizations.

Helium-1 的初步评估显示在多语言基准测试中表现强劲。该模型经常超越或匹配Qwen 2.5（1.5B）、Gemma 2B和Llama 3B等模型，证明了其训练策略和优化的有效性。

Despite its relatively small size, Helium-1 handles complex queries with accuracy, generating coherent and contextually relevant responses. This makes it suitable for applications like conversational AI, real-time translation, and mobile content summarization.

尽管 Helium-1 的尺寸相对较小，但它可以准确地处理复杂的查询，生成连贯且上下文相关的响应。这使得它适合对话式人工智能、实时翻译和移动内容摘要等应用。

Conclusion

结论

Helium-1 Preview is a significant step forward in addressing the challenges of deploying AI models on edge and mobile platforms. By effectively balancing multilingual capabilities and computational efficiency, Helium-1 sets a precedent for future developments in this space.

Helium-1 预览版是解决在边缘和移动平台上部署 AI 模型的挑战方面向前迈出的重要一步。通过有效平衡多语言能力和计算效率，Helium-1 为该领域的未来发展树立了先例。

Its scalability, coupled with Kyutai Labs’ open-source ethos, underscores its potential to broaden access to high-performing AI technologies. As development continues, Helium-1 is set to play a pivotal role in shaping the future of AI on edge and mobile devices, empowering developers and benefiting users globally.

其可扩展性，加上 Kyutai Labs 的开源精神，凸显了其扩大高性能人工智能技术访问范围的潜力。随着开发的不断进行，Helium-1 将在塑造边缘和移动设备上人工智能的未来方面发挥关键作用，为开发人员赋能并让全球用户受益。

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查看拥抱脸上的细节和模型。这项研究的所有功劳都归功于该项目的研究人员。另外，不要忘记在 Twitter 上关注我们并加入我们的 Telegram 频道和 LinkedIn 群组。不要忘记加入我们 65k+ ML SubReddit。

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🚨 推荐开源平台：Parlant 是一个框架，它改变了人工智能代理在面向客户的场景中做出决策的方式。 （已晋升）