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开源还是API？
发在Google准备开源Gemma之后-OpenAI系列第三篇
看到新闻的时候正巧读到这篇文章，文章出现在OpenAI发布API之后（openai.com），OpenAI模型不开源（除了Whisper）是常常被诟病的一点，甚至被调侃成CloseAI。文章里介绍了OpenAI选择API而不是开源的原因，相信在当时是符合他们初衷的
为什么OpenAI决定发布商业产品（API）？
1. 确保有足够资金让AI造福每个人
2. 无法完全安全地部署强AI，发布API与伙伴合作可以提前了解会遇见的挑战和部署情况，确保对每个人都是安全和有益的
（Ultimately, what we care about most is ensuring artificial general intelligence benefits everyone. We see developing commercial products as one of the ways to make sure we have enough funding to succeed.
We also believe that safely deploying powerful AI systems in the world will be hard to get right. In releasing the API, we are working closely with our partners to see what challenges arise when AI systems are used in the real world. This will help guide our efforts to understand how deploying future AI systems will go, and what we need to do to make sure they are safe and beneficial for everyone.）
为什么选择API而不是开源？
1. 商业化，用于研究，安全和政策工作的费用
2. 开源模型非常大，需要专业知识开发和部署，运行成本高昂。API对小型企业和组织更友好
3. API更容易应对技术滥用。预测模型下游应用很难，用API可以慢慢扩大访问范围，从本质上更安全。发布开源模型如果出现有害应用访问权限无法调整（也对，比如开源换脸faceswap）
（There are three main reasons we did this. First, commercializing the technology helps us pay for our ongoing AI research, safety, and policy efforts.
Second, many of the models underlying the API are very large, taking a lot of expertise to develop and deploy and making them very expensive to run. This makes it hard for anyone except larger companies to benefit from the underlying technology. We’re hopeful that the API will make powerful AI systems more accessible to smaller businesses and organizations.
Third, the API model allows us to more easily respond to misuse of the technology. Since it is hard to predict the downstream use cases of our models, it feels inherently safer to release them via an API and broaden access over time, rather than release an open source model where access cannot be adjusted if it turns out to have harmful applications.）
Ilya在回复马斯克为什么不开源时也表达过相似观点，我胡乱猜测之后OpenAI可能平衡模型开源和商业化的办法是：
1. 商业化的GPT模型API和传闻中开源的G3PO模型
2. 类似于付费的高版本GPT-4和免费的开源低版本GPT-3，或是差异化定价不同参数量的变体模型
话说开源模型如何盈利？一般开源会把代码，训练过程，权重和数据集都开放吗？
发在Google准备开源Gemma之后-OpenAI系列第三篇
看到新闻的时候正巧读到这篇文章，文章出现在OpenAI发布API之后（openai.com），OpenAI模型不开源（除了Whisper）是常常被诟病的一点，甚至被调侃成CloseAI。文章里介绍了OpenAI选择API而不是开源的原因，相信在当时是符合他们初衷的
为什么OpenAI决定发布商业产品（API）？
1. 确保有足够资金让AI造福每个人
2. 无法完全安全地部署强AI，发布API与伙伴合作可以提前了解会遇见的挑战和部署情况，确保对每个人都是安全和有益的
（Ultimately, what we care about most is ensuring artificial general intelligence benefits everyone. We see developing commercial products as one of the ways to make sure we have enough funding to succeed.
We also believe that safely deploying powerful AI systems in the world will be hard to get right. In releasing the API, we are working closely with our partners to see what challenges arise when AI systems are used in the real world. This will help guide our efforts to understand how deploying future AI systems will go, and what we need to do to make sure they are safe and beneficial for everyone.）
为什么选择API而不是开源？
1. 商业化，用于研究，安全和政策工作的费用
2. 开源模型非常大，需要专业知识开发和部署，运行成本高昂。API对小型企业和组织更友好
3. API更容易应对技术滥用。预测模型下游应用很难，用API可以慢慢扩大访问范围，从本质上更安全。发布开源模型如果出现有害应用访问权限无法调整（也对，比如开源换脸faceswap）
（There are three main reasons we did this. First, commercializing the technology helps us pay for our ongoing AI research, safety, and policy efforts.
Second, many of the models underlying the API are very large, taking a lot of expertise to develop and deploy and making them very expensive to run. This makes it hard for anyone except larger companies to benefit from the underlying technology. We’re hopeful that the API will make powerful AI systems more accessible to smaller businesses and organizations.
Third, the API model allows us to more easily respond to misuse of the technology. Since it is hard to predict the downstream use cases of our models, it feels inherently safer to release them via an API and broaden access over time, rather than release an open source model where access cannot be adjusted if it turns out to have harmful applications.）
Ilya在回复马斯克为什么不开源时也表达过相似观点，我胡乱猜测之后OpenAI可能平衡模型开源和商业化的办法是：
1. 商业化的GPT模型API和传闻中开源的G3PO模型
2. 类似于付费的高版本GPT-4和免费的开源低版本GPT-3，或是差异化定价不同参数量的变体模型
话说开源模型如何盈利？一般开源会把代码，训练过程，权重和数据集都开放吗？
歸藏发表了动态：见鬼了，谷歌居然开源LLM模型了，Meta要慌了。 Gemma 采用了和Gemini一样技术的开源LLM，同时质量也比同规模的模型要强。 下面是一些要点： ◈ 两种尺寸的模型权重：Gemma 2B和Gemma 7B。每种尺寸都有预训练和指导调整的变体。 ◈ 一个生成式人工智能工具包，为使用Gemma创建更安全的人工智能应用提供指导和必要工具。 ◈ 通过原生Keras 3.0为所有主要框架（JAX、PyTorch和TensorFlow）提供推理和监督微调（SFT）的工具链。 ◈ 准备好的Colab和Kaggle笔记本，以及与Hugging Face、MaxText、NVIDIA NeMo和TensorRT等流行工具的集成，使得开始使用Gemma变得非常容易。 ◈ 预先训练和经过调整的Gemma模型可以在您的笔记本电脑、工作站或Google Cloud上运行，并可以轻松部署到Vertex AI和Google Kubernetes Engine（GKE）。 ◈ 跨多个人工智能硬件平台的优化确保了行业领先的性能，包括NVIDIA GPU和Google Cloud TPU。 ◈ 允许所有组织进行负责任的商业使用和分发，无论规模大小。 ◈未来还会发布Gemma更大模型变体。 了解更多：https://blog.google/technology/developers/gemma-open-models
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