In the early days of high-performance computing, the major tech companies of the day each invested heavily in developing their own closed source versions of Unix. It was hard to imagine at the time that any other approach could develop such advanced software. Eventually though, open source Linux gained popularity – initially because it allowed developers to modify its code however they wanted and was more affordable, and over time because it became more advanced, more secure, and had a broader ecosystem supporting more capabilities than any closed Unix. Today, Linux is the industry standard foundation for both cloud computing and the operating systems that run most mobile devices – and we all benefit from superior products because of it.
在高性能计算的早期，当时的主要科技公司都投入巨资开发自己的封闭源代码 Unix 版本。当时很难想象有其他方法可以开发出如此先进的软件。最终，开源 Linux 开始流行起来--起初是因为它允许开发人员随意修改代码，而且价格更低廉；随着时间的推移，它变得更先进、更安全，而且拥有更广泛的生态系统，支持比任何封闭式 Unix 更多的功能。如今，Linux 已成为云计算和运行大多数移动设备的操作系统的行业标准基础，我们都因此受益于卓越的产品。
I believe that AI will develop in a similar way. Today, several tech companies are developing leading closed models. But open source is quickly closing the gap. Last year, Llama 2 was only comparable to an older generation of models behind the frontier. This year, Llama 3 is competitive with the most advanced models and leading in some areas. Starting next year, we expect future Llama models to become the most advanced in the industry. But even before that, Llama is already leading on openness, modifiability, and cost efficiency.
我相信，人工智能也将以类似的方式发展。如今，一些科技公司正在开发领先的封闭模型。但开源技术正在迅速缩小差距。去年，Llama 2 只能与落后于前沿的老一代模型相媲美。今年，Llama 3 已能与最先进的机型媲美，并在某些领域处于领先地位。从明年开始，我们预计未来的 Llama 型号将成为业内最先进的型号。但在此之前，Llama 已经在开放性、可修改性和成本效益方面处于领先地位。
Today we’re taking the next steps towards open source AI becoming the industry standard. We’re releasing Llama 3.1 405B, the first frontier-level open source AI model, as well as new and improved Llama 3.1 70B and 8B models. In addition to having significantly better cost/performance relative to closed models, the fact that the 405B model is open will make it the best choice for fine-tuning and distilling smaller models.
今天，我们朝着开源人工智能成为行业标准的方向迈出了新的一步。我们将发布 Llama 3.1 405B（首个前沿级开源人工智能模型）以及全新改进的 Llama 3.1 70B 和 8B 模型。与封闭模型相比，405B 模型的性价比明显更高，此外，它的开放性还将使其成为微调和提炼较小模型的最佳选择。
Beyond releasing these models, we’re working with a range of companies to grow the broader ecosystem. Amazon, Databricks, and NVIDIA are launching full suites of services to support developers fine-tuning and distilling their own models. Innovators like Groq have built low-latency, low-cost inference serving for all the new models. The models will be available on all major clouds including AWS, Azure, Google, Oracle, and more. Companies like Scale.AI, Dell, Deloitte, and others are ready to help enterprises adopt Llama and train custom models with their own data. As the community grows and more companies develop new services, we can collectively make Llama the industry standard and bring the benefits of AI to everyone.
除了发布这些模型外，我们还与多家公司合作，共同发展更广泛的生态系统。亚马逊、Databricks 和英伟达正在推出全套服务，以支持开发人员微调和提炼自己的模型。Groq 等创新企业为所有新模型构建了低延迟、低成本的推理服务。这些模型将在包括 AWS、Azure、谷歌、甲骨文等在内的所有主要云上提供。Scale.AI、戴尔、德勤等公司已准备好帮助企业采用 Llama，并利用它们自己的数据训练定制模型。随着社区的发展和更多公司开发新服务，我们可以共同使 Llama 成为行业标准，让每个人都能享受到人工智能的好处。
Meta is committed to open source AI. I’ll outline why I believe open source is the best development stack for you, why open sourcing Llama is good for Meta, and why open source AI is good for the world and therefore a platform that will be around for the long term.
Meta 致力于开源人工智能。我将概述为什么我相信开源是最适合你的开发堆栈，为什么开源 Llama 对 Meta 有利，为什么开源人工智能对世界有利，因此是一个可以长期存在的平台。
Why Open Source AI Is Good for Developers
开源人工智能为何对开发者有利
When I talk to developers, CEOs, and government officials across the world, I usually hear several themes:
当我与世界各地的开发人员、首席执行官和政府官员交谈时，我通常会听到几个主题：
- We need to train, fine-tune, and distill our own models. Every organization has different needs that are best met with models of different sizes that are trained or fine-tuned with their specific data. On-device tasks and classification tasks require small models, while more complicated tasks require larger models. Now you’ll be able to take the most advanced Llama models, continue training them with your own data and then distill them down to a model of your optimal size – without us or anyone else seeing your data.
我们需要训练、微调和提炼自己的模型。每个组织都有不同的需求，而满足这些需求的最佳方法就是使用不同规模的模型，并根据特定数据进行训练或微调。设备上的任务和分类任务需要小型模型，而更复杂的任务则需要大型模型。现在，您可以使用最先进的 Llama 模型，继续使用自己的数据对其进行训练，然后将其提炼为最适合您的模型，而我们或其他任何人都不会看到您的数据。 - We need to control our own destiny and not get locked into a closed vendor. Many organizations don’t want to depend on models they cannot run and control themselves. They don’t want closed model providers to be able to change their model, alter their terms of use, or even stop serving them entirely. They also don’t want to get locked into a single cloud that has exclusive rights to a model. Open source enables a broad ecosystem of companies with compatible toolchains that you can move between easily.
我们需要掌握自己的命运，而不是被锁定在一个封闭的供应商中。许多组织不希望依赖于自己无法运行和控制的模式。他们不希望封闭模式的供应商能够改变其模式、更改使用条款，甚至完全停止为他们提供服务。他们也不希望被锁定在对模型拥有独占权的单一云中。开放源代码可以为公司提供一个广泛的生态系统，这些公司拥有兼容的工具链，您可以在它们之间轻松切换。 - We need to protect our data. Many organizations handle sensitive data that they need to secure and can’t send to closed models over cloud APIs. Other organizations simply don’t trust the closed model providers with their data. Open source addresses these issues by enabling you to run the models wherever you want. It is well-accepted that open source software tends to be more secure because it is developed more transparently.
我们需要保护数据。许多组织需要处理敏感数据，这些数据需要安全保护，但又不能通过云 API 发送给封闭模型。其他组织则根本不信任封闭模型提供商，不信任他们的数据。开放源代码可以解决这些问题，让您在任何地方运行模型。众所周知，开源软件往往更加安全，因为其开发过程更加透明。 - We need a model that is efficient and affordable to run. Developers can run inference on Llama 3.1 405B on their own infra at roughly 50% the cost of using closed models like GPT-4o, for both user-facing and offline inference tasks.
我们需要一个运行高效且经济实惠的模型。开发人员可以在自己的基础架构上运行 Llama 3.1 405B 的推理，其成本大约是使用 GPT-4o 等封闭模型的 50%，既可用于面向用户的推理，也可用于离线推理任务。 - We want to invest in the ecosystem that’s going to be the standard for the long term. Lots of people see that open source is advancing at a faster rate than closed models, and they want to build their systems on the architecture that will give them the greatest advantage long term.
我们希望投资于将成为长期标准的生态系统。很多人都看到，开放源代码的发展速度比封闭模式更快，因此他们希望在能长期为他们带来最大优势的架构上构建自己的系统。
Why Open Source AI Is Good for Meta
开源人工智能为何有利于元
Meta’s business model is about building the best experiences and services for people. To do this, we must ensure that we always have access to the best technology, and that we’re not locking into a competitor’s closed ecosystem where they can restrict what we build.
Meta 的商业模式是为人们打造最佳体验和服务。要做到这一点，我们必须确保我们始终能够获得最好的技术，而且我们不会被竞争对手的封闭生态系统所锁定，因为他们会限制我们的发展。
One of my formative experiences has been building our services constrained by what Apple will let us build on their platforms. Between the way they tax developers, the arbitrary rules they apply, and all the product innovations they block from shipping, it’s clear that Meta and many other companies would be freed up to build much better services for people if we could build the best versions of our products and competitors were not able to constrain what we could build. On a philosophical level, this is a major reason why I believe so strongly in building open ecosystems in AI and AR/VR for the next generation of computing.
我的成长经历之一，就是在苹果公司的平台上构建服务时受到其限制。很明显，如果我们能打造出最好的产品版本，而竞争对手又不能限制我们的产品，那么 Meta 和许多其他公司就能解放出来，为人们打造更好的服务。在哲学层面上，这也是我为什么如此坚信要为下一代计算建立人工智能和 AR/VR 开放生态系统的主要原因。
People often ask if I’m worried about giving up a technical advantage by open sourcing Llama, but I think this misses the big picture for a few reasons:
人们常问我是否担心开放 Llama 的源代码会放弃技术上的优势，但我认为这种担心是大错特错的，原因有以下几点：
First, to ensure that we have access to the best technology and aren’t locked into a closed ecosystem over the long term, Llama needs to develop into a full ecosystem of tools, efficiency improvements, silicon optimizations, and other integrations. If we were the only company using Llama, this ecosystem wouldn’t develop and we’d fare no better than the closed variants of Unix.
首先，为了确保我们能够获得最好的技术，并且不会长期被锁定在一个封闭的生态系统中，Llama 需要发展成为一个包含工具、效率改进、硅优化和其他集成的完整生态系统。如果只有我们一家公司使用 Llama，这个生态系统就不会发展起来，我们的处境也不会比封闭的 Unix 变种更好。
Second, I expect AI development will continue to be very competitive, which means that open sourcing any given model isn’t giving away a massive advantage over the next best models at that point in time. The path for Llama to become the industry standard is by being consistently competitive, efficient, and open generation after generation.
其次，我预计人工智能的发展将继续保持非常激烈的竞争态势，这意味着开放任何给定模型的源代码，都不会使其比当时的下一个最佳模型具有更大的优势。Llama 要想成为行业标准，就必须一代又一代地保持竞争力、效率和开放性。
Third, a key difference between Meta and closed model providers is that selling access to AI models isn’t our business model. That means openly releasing Llama doesn’t undercut our revenue, sustainability, or ability to invest in research like it does for closed providers. (This is one reason several closed providers consistently lobby governments against open source.)
第三，Meta 与封闭模型提供商的一个关键区别在于，出售人工智能模型的访问权并不是我们的商业模式。这意味着公开发布 Llama 不会像封闭式提供商那样削弱我们的收入、可持续性或投资研究的能力。(这也是一些封闭式供应商不断游说政府反对开源的原因之一）。
Finally, Meta has a long history of open source projects and successes. We’ve saved billions of dollars by releasing our server, network, and data center designs with Open Compute Project and having supply chains standardize on our designs. We benefited from the ecosystem’s innovations by open sourcing leading tools like PyTorch, React, and many more tools. This approach has consistently worked for us when we stick with it over the long term.
最后，Meta 在开源项目和成功方面有着悠久的历史。我们通过开放计算项目（Open Compute Project）发布服务器、网络和数据中心设计，并让供应链以我们的设计为标准，从而节省了数十亿美元。通过开源 PyTorch、React 等领先工具，我们从生态系统的创新中获益匪浅。只要我们长期坚持，这种方法就会始终奏效。
Why Open Source AI Is Good for the World
为什么开源人工智能对世界有益？
I believe that open source is necessary for a positive AI future. AI has more potential than any other modern technology to increase human productivity, creativity, and quality of life – and to accelerate economic growth while unlocking progress in medical and scientific research. Open source will ensure that more people around the world have access to the benefits and opportunities of AI, that power isn’t concentrated in the hands of a small number of companies, and that the technology can be deployed more evenly and safely across society.
我相信，开源是人工智能积极未来的必要条件。在提高人类生产力、创造力和生活质量方面，人工智能比其他任何现代技术都更有潜力，它还能加速经济增长，同时推动医学和科学研究的进步。开源将确保全世界更多的人能够享受到人工智能带来的好处和机遇，确保权力不会集中在少数公司手中，确保这项技术能够在全社会得到更均衡、更安全的应用。
There is an ongoing debate about the safety of open source AI models, and my view is that open source AI will be safer than the alternatives. I think governments will conclude it’s in their interest to support open source because it will make the world more prosperous and safer.
关于开源人工智能模型的安全性一直存在争论，而我的观点是，开源人工智能将比替代品更安全。我认为政府会得出支持开源符合其利益的结论，因为这将使世界更加繁荣和安全。
My framework for understanding safety is that we need to protect against two categories of harm: unintentional and intentional. Unintentional harm is when an AI system may cause harm even when it was not the intent of those running it to do so. For example, modern AI models may inadvertently give bad health advice. Or, in more futuristic scenarios, some worry that models may unintentionally self-replicate or hyper-optimize goals to the detriment of humanity. Intentional harm is when a bad actor uses an AI model with the goal of causing harm.
我对安全性的理解框架是，我们需要防范两类伤害：无意伤害和有意伤害。无意伤害是指人工智能系统可能会造成伤害，即使运行该系统的人并非有意为之。例如，现代人工智能模型可能会无意中给出错误的健康建议。或者，在更未来的情况下，一些人担心模型可能会无意中自我复制或过度优化目标，从而损害人类。故意伤害是指不良行为者以造成伤害为目的使用人工智能模型。
It’s worth noting that unintentional harm covers the majority of concerns people have around AI – ranging from what influence AI systems will have on the billions of people who will use them to most of the truly catastrophic science fiction scenarios for humanity. On this front, open source should be significantly safer since the systems are more transparent and can be widely scrutinized. Historically, open source software has been more secure for this reason. Similarly, using Llama with its safety systems like Llama Guard will likely be safer and more secure than closed models. For this reason, most conversations around open source AI safety focus on intentional harm.
值得注意的是，无意伤害涵盖了人们对人工智能的大部分担忧--从人工智能系统将对数十亿使用它们的人产生何种影响，到大多数真正灾难性的科幻小说中的人类场景。在这方面，开源软件应该更加安全，因为这些系统更加透明，可以受到广泛的审查。从历史上看，开放源码软件因此更加安全。同样，使用 Llama 及其安全系统（如 Llama Guard）可能会比封闭模式更安全可靠。因此，围绕开源人工智能安全性的讨论大多集中在故意伤害方面。
Our safety process includes rigorous testing and red-teaming to assess whether our models are capable of meaningful harm, with the goal of mitigating risks before release. Since the models are open, anyone is capable of testing for themselves as well. We must keep in mind that these models are trained by information that’s already on the internet, so the starting point when considering harm should be whether a model can facilitate more harm than information that can quickly be retrieved from Google or other search results.
我们的安全流程包括严格的测试和红队，以评估我们的模型是否会造成有意义的伤害，目的是在发布前降低风险。由于模型是开放的，因此任何人都可以自行测试。我们必须牢记，这些模型是通过互联网上已有的信息训练出来的，因此在考虑危害时，出发点应该是一个模型是否能比从谷歌或其他搜索结果中快速检索到的信息带来更大的危害。
When reasoning about intentional harm, it’s helpful to distinguish between what individual or small scale actors may be able to do as opposed to what large scale actors like nation states with vast resources may be able to do.
在对蓄意伤害进行推理时，区分个人或小规模行为者与拥有大量资源的民族国家等大规模行为者的能力是有帮助的。
At some point in the future, individual bad actors may be able to use the intelligence of AI models to fabricate entirely new harms from the information available on the internet. At this point, the balance of power will be critical to AI safety. I think it will be better to live in a world where AI is widely deployed so that larger actors can check the power of smaller bad actors. This is how we’ve managed security on our social networks – our more robust AI systems identify and stop threats from less sophisticated actors who often use smaller scale AI systems. More broadly, larger institutions deploying AI at scale will promote security and stability across society. As long as everyone has access to similar generations of models – which open source promotes – then governments and institutions with more compute resources will be able to check bad actors with less compute.
在未来的某个时刻，个别不良行为者可能会利用人工智能模型的智能，从互联网上的信息中编造出全新的危害。在这一点上，力量平衡对人工智能安全至关重要。我认为，我们最好生活在一个广泛部署人工智能的世界里，这样规模较大的行为者就可以制衡规模较小的不良行为者。这就是我们管理社交网络安全的方式--我们更强大的人工智能系统可以识别并阻止来自不太复杂的行为者的威胁，而这些行为者通常使用规模较小的人工智能系统。更广泛地说，大规模部署人工智能的大型机构将促进整个社会的安全与稳定。只要每个人都能使用类似的模型，那么拥有更多计算资源的政府和机构就能用更少的计算资源来遏制坏人。
The next question is how the US and democratic nations should handle the threat of states with massive resources like China. The United States’ advantage is decentralized and open innovation. Some people argue that we must close our models to prevent China from gaining access to them, but my view is that this will not work and will only disadvantage the US and its allies. Our adversaries are great at espionage, stealing models that fit on a thumb drive is relatively easy, and most tech companies are far from operating in a way that would make this more difficult. It seems most likely that a world of only closed models results in a small number of big companies plus our geopolitical adversaries having access to leading models, while startups, universities, and small businesses miss out on opportunities. Plus, constraining American innovation to closed development increases the chance that we don’t lead at all. Instead, I think our best strategy is to build a robust open ecosystem and have our leading companies work closely with our government and allies to ensure they can best take advantage of the latest advances and achieve a sustainable first-mover advantage over the long term.
下一个问题是，美国和民主国家应如何应对像中国这样拥有大量资源的国家的威胁。美国的优势在于分散和开放的创新。有些人认为，我们必须关闭我们的模型，以防止中国获得这些模型，但我认为这行不通，只会对美国及其盟国不利。我们的对手擅长间谍活动，窃取装在优盘里的模型也相对容易，而且大多数科技公司的运营方式远没有达到增加难度的程度。一个只有封闭模型的世界最有可能导致的结果是，少数大公司和我们的地缘政治对手能够获得领先的模型，而初创企业、大学和小企业却错失良机。此外，将美国的创新局限于封闭式发展也会增加我们无法领先的可能性。相反，我认为我们最好的战略是建立一个强大的开放生态系统，让我们的领先企业与我们的政府和盟友紧密合作，确保他们能最好地利用最新进展，实现长期可持续的先发优势。
When you consider the opportunities ahead, remember that most of today’s leading tech companies and scientific research are built on open source software. The next generation of companies and research will use open source AI if we collectively invest in it. That includes startups just getting off the ground as well as people in universities and countries that may not have the resources to develop their own state-of-the-art AI from scratch.
在考虑未来的机遇时，请记住当今大多数领先的科技公司和科学研究都是建立在开源软件的基础上的。如果我们共同投资，下一代公司和研究将使用开源人工智能。这包括刚刚起步的初创公司，以及那些可能没有资源从零开始开发自己的最先进人工智能的大学和国家的人们。
The bottom line is that open source AI represents the world’s best shot at harnessing this technology to create the greatest economic opportunity and security for everyone.
最重要的是，开源人工智能是世界上利用这项技术为每个人创造最大经济机遇和安全的最佳机会。
Let’s Build This Together
让我们携手共建
With past Llama models, Meta developed them for ourselves and then released them, but didn’t focus much on building a broader ecosystem. We’re taking a different approach with this release. We’re building teams internally to enable as many developers and partners as possible to use Llama, and we’re actively building partnerships so that more companies in the ecosystem can offer unique functionality to their customers as well.
在过去的 Llama 模型中，Meta 只为自己开发，然后发布，但并不注重建立更广泛的生态系统。这次发布我们采取了不同的方法。我们正在建立内部团队，让尽可能多的开发人员和合作伙伴使用 Llama，我们还在积极建立合作伙伴关系，让生态系统中更多的公司也能为他们的客户提供独特的功能。
I believe the Llama 3.1 release will be an inflection point in the industry where most developers begin to primarily use open source, and I expect that approach to only grow from here. I hope you’ll join us on this journey to bring the benefits of AI to everyone in the world.
我相信，Llama 3.1 版本的发布将成为行业的一个拐点，大多数开发人员将开始主要使用开源技术，而且我预计这种做法只会有增无减。我希望您能加入我们的行列，让世界上的每个人都能享受到人工智能带来的好处。
You can access the models now at llama.meta.com.
您现在就可以通过 llama.meta.com 访问这些模型。
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MZ