Workers AI’s initial launch in beta included support for Llama 2, as it was one of the most requested open source models from the developer community. Since that initial launch, we’ve seen developers build all kinds of innovative applications including knowledge sharing chatbots, creative content generation, and automation for various workflows.
At Cloudflare, we know developers want simplicity and flexibility, with the ability to build with multiple AI models while optimizing for accuracy, performance, and cost, among other factors. Our goal is to make it as easy as possible for developers to use their models of choice without having to worry about the complexities of hosting or deploying models.
As soon as we learned about the development of Llama 3 from our partners at Meta, we knew developers would want to start building with it as quickly as possible. Workers AI’s serverless inference platform makes it extremely easy and cost effective to start using the latest large language models (LLMs). Meta’s commitment to developing and growing an open AI-ecosystem makes it possible for customers of all sizes to use AI at scale in production. All it takes is a few lines of code to run inference to Llama 3:
export interface Env {
// If you set another name in wrangler.toml as the value for 'binding',
// replace "AI" with the variable name you defined.
AI: any;
}
export default {
async fetch(request: Request, env: Env) {
const response = await env.AI.run('@cf/meta/llama-3-8b-instruct', {
messages: [
{role: "user", content: "What is the origin of the phrase Hello, World?"}
]
}
);
return new Response(JSON.stringify(response));
},
};
Built with Meta Llama 3
Llama 3 offers leading performance on a wide range of industry benchmarks. You can learn more about the architecture and improvements on Meta’s blog post. Cloudflare Workers AI supports Llama 3 8B, including the instruction fine-tuned model.
Meta’s testing shows that Llama 3 is the most advanced open LLM today on evaluation benchmarks such as MMLU, GPQA, HumanEval, GSM-8K, and MATH. Llama 3 was trained on an increased number of training tokens (15T), allowing the model to have a better grasp on language intricacies. Larger context windows doubles the capacity of Llama 2, and allows the model to better understand lengthy passages with rich contextual data. Although the model supports a context window of 8k, we currently only support 2.8k but are looking to support 8k context windows through quantized models soon. As well, the new model introduces an efficient new tiktoken-based tokenizer with a vocabulary of 128k tokens, encoding more characters per token, and achieving better performance on English and multilingual benchmarks. This means that there are 4 times as many parameters in the embedding and output layers, making the model larger than the previous Llama 2 generation of models.
Under the hood, Llama 3 uses grouped-query attention (GQA), which improves inference efficiency for longer sequences and also renders their 8B model architecturally equivalent to Mistral-7B. For tokenization, it uses byte-level byte-pair encoding (BPE), similar to OpenAI’s GPT tokenizers. This allows tokens to represent any arbitrary byte sequence — even those without a valid utf-8 encoding. This makes the end-to-end model much more flexible in its representation of language, and leads to improved performance.
Along with the base Llama 3 models, Meta has released a suite of offerings with tools such as Llama Guard 2, Code Shield, and CyberSec Eval 2, which we are hoping to release on our Workers AI platform shortly.
Inference from fine-tuned LLMs with LoRAs is now in open beta
Today, we’re excited to announce that you can now run fine-tuned inference with LoRAs on Workers AI. This feature is in open beta and available for pre-trained LoRA adapters to be used with Mistral, Gemma, or Llama 2, with some limitations. Take a look at our product announcements blog post to get a high-level overview of our Bring Your Own (BYO) LoRAs feature.
In this post, we’ll do a deep dive into what fine-tuning and LoRAs are, show you how to use it on our Workers AI platform, and then delve into the technical details of how we implemented it on our platform.
What is fine-tuning?
Fine-tuning is a general term for modifying an AI model by continuing to train it with additional data. The goal of fine-tuning is to increase the probability that a generation is similar to your dataset. Training a model from scratch is not practical for many use cases given how expensive and time consuming they can be to train. By fine-tuning an existing pre-trained model, you benefit from its capabilities while also accomplishing your desired task. Low-Rank Adaptation (LoRA) is a specific fine-tuning method that can be applied to various model architectures, not just LLMs. It is common that the pre-trained model weights are directly modified or fused with additional fine-tune weights in traditional fine-tuning methods. LoRA, on the other hand, allows for the fine-tune weights and pre-trained model to remain separate, and for the pre-trained model to remain unchanged. The end result is that you can train models to be more accurate at specific tasks, such as generating code, having a specific personality, or generating images in a specific style. You can even fine-tune an existing LLM to understand additional information about a specific topic.
The approach of maintaining the original base model weights means that you can create new fine-tune weights with relatively little compute. You can take advantage of existing foundational models (such as Llama, Mistral, and Gemma), and adapt them for your needs.
How does fine-tuning work?
To better understand fine-tuning and why LoRA is so effective, we have to take a step back to understand how AI models work. AI models (like LLMs) are neural networks that are trained through deep learning techniques. In neural networks, there are a set of parameters that act as a mathematical representation of the model’s domain knowledge, made up of weights and biases – in simple terms, numbers. These parameters are usually represented as large matrices of numbers. The more parameters a model has, the larger the model is, so when you see models like llama-2-7b, you can read “7b” and know that the model has 7 billion parameters.
A model’s parameters define its behavior. When you train a model from scratch, these parameters usually start off as random numbers. As you train the model on a dataset, these parameters get adjusted bit-by-bit until the model reflects the dataset and exhibits the right behavior. Some parameters will be more important than others, so we apply a weight and use it to show more or less importance. Weights play a crucial role in the model’s ability to capture patterns and relationships in the data it is trained on.
Traditional fine-tuning will adjust all the parameters in the trained model with a new set of weights. As such, a fine-tuned model requires us to serve the same amount of parameters as the original model, which means it can take a lot of time and compute to train and run inference for a fully fine-tuned model. On top of that, new state-of-the-art models, or versions of existing models, are regularly released, meaning that fully fine-tuned models can become costly to train, maintain, and store.
LoRA is an efficient method of fine-tuning
In the simplest terms, LoRA avoids adjusting parameters in a pre-trained model and instead allows us to apply a small number of additional parameters. These additional parameters are applied temporarily to the base model to effectively control model behavior. Relative to traditional fine-tuning methods it takes a lot less time and compute to train these additional parameters, which are referred to as a LoRA adapter. After training, we package up the LoRA adapter as a separate model file that can then plug in to the base model it was trained from. A fully fine-tuned model can be tens of gigabytes in size, while these adapters are usually just a few megabytes. This makes it a lot easier to distribute, and serving fine-tuned inference with LoRA only adds ms of latency to total inference time.
If you’re curious to understand why LoRA is so effective, buckle up — we first have to go through a brief lesson on linear algebra. If that’s not a term you’ve thought about since university, don’t worry, we’ll walk you through it.
Show me the math
With traditional fine-tuning, we can take the weights of a model (W0) and tweak them to output a new set of weights — so the difference between the original model weights and the new weights is ΔW, representing the change in weights. Therefore, a tuned model will have a new set of weights which can be represented as the original model weights plus the change in weights, W0 + ΔW.
Remember, all of these model weights are actually represented as large matrices of numbers. In math, every matrix has a property called rank (r), which describes the number of linearly independent columns or rows in a matrix. When matrices are low-rank, they have only a few columns or rows that are “important”, so we can actually decompose or split them into two smaller matrices with the most important parameters (think of it like factoring in algebra). This technique is called rank decomposition, which allows us to greatly reduce and simplify matrices while keeping the most important bits. In the context of fine-tuning, rank determines how many parameters get changed from the original model – the higher the rank, the stronger the fine-tune, giving you more granularity over the output.
According to the original LoRA paper, researchers have found that when a model is low-rank, the matrix representing the change in weights is also low-rank. Therefore, we can apply rank decomposition to our matrix representing the change in weights ΔW to create two smaller matrices A, B, where ΔW = BA. Now, the change in the model can be represented by two smaller low-rank matrices. This is why this method of fine-tuning is called Low-Rank Adaptation.
When we run inference, we only need the smaller matrices A, B to change the behavior of the model. The model weights in A, B constitute our LoRA adapter (along with a config file). At runtime, we add the model weights together, combining the original model (W0) and the LoRA adapter (A, B). Adding and subtracting are simple mathematical operations, meaning that we can quickly swap out different LoRA adapters by adding and subtracting A, B from W0.. By temporarily adjusting the weights of the original model, we modify the model’s behavior and output and as a result, we get fine-tuned inference with minimal added latency.
According to the original LoRA paper, “LoRA can reduce the number of trainable parameters by 10,000 times and the GPU memory requirement by 3 times”. Because of this, LoRA is one of the most popular methods of fine-tuning since it’s a lot less computationally expensive than a fully fine-tuned model, doesn’t add any material inference time, and is much smaller and portable.
How can you use LoRAs with Workers AI?
Workers AI is very well-suited to run LoRAs because of the way we run serverless inference. The models in our catalog are always pre-loaded on our GPUs, meaning that we keep them warm so that your requests never encounter a cold start. This means that the base model is always available, and we can dynamically load and swap out LoRA adapters as needed. We can actually plug in multiple LoRA adapters to one base model, so we can serve multiple different fine-tuned inference requests at once.
When you fine-tune with LoRA, your output will be two files: your custom model weights (in safetensors format) and an adapter config file (in json format). To create these weights yourself, you can train a LoRA on your own data using the Hugging Face PEFT (Parameter-Efficient Fine-Tuning) library combined with the Hugging Face AutoTrain LLM library. You can also run your training tasks on services such as Auto Train and Google Colab. Alternatively, there are many open-source LoRA adapters available on Hugging Face today that cover a variety of use cases.
Eventually, we want to support the LoRA training workloads on our platform, but we’ll need you to bring your trained LoRA adapters to Workers AI today, which is why we’re calling this feature Bring Your Own (BYO) LoRAs.
For the initial open beta release, we are allowing people to use LoRAs with our Mistral, Llama, and Gemma models. We have set aside versions of these models which accept LoRAs, which you can access by appending -lora to the end of the model name. Your adapter must have been fine-tuned from one of our supported base models listed below:
@cf/meta-llama/llama-2-7b-chat-hf-lora
@cf/mistral/mistral-7b-instruct-v0.2-lora
@cf/google/gemma-2b-it-lora
@cf/google/gemma-7b-it-lora
As we are launching this feature in open beta, we have some limitations today to take note of: quantized LoRA models are not yet supported, LoRA adapters must be smaller than 100MB and have up to a max rank of 8, and you can try up to 30 LoRAs per account during our initial open beta. To get started with LoRAs on Workers AI, read the Developer Docs.
As always, we expect people to use Workers AI and our new BYO LoRA feature with our Terms of Service in mind, including any model-specific restrictions on use contained in the models’ license terms.
How did we build multi-tenant LoRA serving?
Serving multiple LoRA models simultaneously poses a challenge in terms of GPU resource utilization. While it is possible to batch inference requests to a base model, it is much more challenging to batch requests with the added complexity of serving unique LoRA adapters. To tackle this problem, we leverage the Punica CUDA kernel design in combination with global cache optimizations in order to handle the memory intensive workload of multi-tenant LoRA serving while offering low inference latency.
The Punica CUDA kernel was introduced in the paper Punica: Multi-Tenant LoRA Serving as a method to serve multiple, significantly different LoRA models applied to the same base model. In comparison to previous inference techniques, the method offers substantial throughput and latency improvements. This optimization is achieved in part through enabling request batching even across requests serving different LoRA adapters.
The core of the Punica kernel system is a new CUDA kernel called Segmented Gather Matrix-Vector Multiplication (SGMV). SGMV allows a GPU to store only a single copy of the pre-trained model while serving different LoRA models. The Punica kernel design system consolidates the batching of requests for unique LoRA models to improve performance by parallelizing the feature-weight multiplication of different requests in a batch. Requests for the same LoRA model are then grouped to increase operational intensity. Initially, the GPU loads the base model while reserving most of its GPU memory for KV Cache. The LoRA components (A and B matrices) are then loaded on demand from remote storage (Cloudflare’s cache or R2) when required by an incoming request. This on demand loading introduces only milliseconds of latency, which means that multiple LoRA adapters can be seamlessly fetched and served with minimal impact on inference performance. Frequently requested LoRA adapters are cached for the fastest possible inference.
Once a requested LoRA has been cached locally, the speed it can be made available for inference is constrained only by PCIe bandwidth. Regardless, given that each request may require its own LoRA, it becomes critical that LoRA downloads and memory copy operations are performed asynchronously. The Punica scheduler tackles this exact challenge, batching only requests which currently have required LoRA weights available in GPU memory, and queueing requests that do not until the required weights are available and the request can efficiently join a batch.
By effectively managing KV cache and batching these requests, it is possible to handle significant multi-tenant LoRA-serving workloads. A further and important optimization is the use of continuous batching. Common batching methods require all requests to the same adapter to reach their stopping condition before being released. Continuous batching allows a request in a batch to be released early so that it does not need to wait for the longest running request.
Given that LLMs deployed to Cloudflare’s network are available globally, it is important that LoRA adapter models are as well. Very soon, we will implement remote model files that are cached at Cloudflare’s edge to further reduce inference latency.
A roadmap for fine-tuning on Workers AI
Launching support for LoRA adapters is an important step towards unlocking fine-tunes on our platform. In addition to the LLM fine-tunes available today, we look forward to supporting more models and a variety of task types, including image generation.
Our vision for Workers AI is to be the best place for developers to run their AI workloads — and this includes the process of fine-tuning itself. Eventually, we want to be able to run the fine-tuning training job as well as fully fine-tuned models directly on Workers AI. This unlocks many use cases for AI to be more relevant in organizations by empowering models to have more granularity and detail for specific tasks.
With AI Gateway, we will be able to help developers log their prompts and responses, which they can then use to fine-tune models with production data. Our vision is to have a one-click fine-tuning service, where log data from AI Gateway can be used to retrain a model (on Cloudflare) and then the fine-tuned model can be redeployed on Workers AI for inference. This will allow developers to personalize their AI models to fit their applications, allowing for granularity as low as a per-user level. The fine-tuned model can then be smaller and more optimized, helping users save time and money on AI inference – and the magic is that all of this can all happen within our very own Developer Platform.
We’re excited for you to try the open beta for BYO LoRAs! Read our Developer Docs for more details, and tell us what you think on Discord.
Welcome to Tuesday – our AI day of Developer Week 2024! In this blog post, we’re excited to share an overview of our new AI announcements and vision, including news about Workers AI officially going GA with improved pricing, a GPU hardware momentum update, an expansion of our Hugging Face partnership, Bring Your Own LoRA fine-tuned inference, Python support in Workers, more providers in AI Gateway, and Vectorize metadata filtering.
Workers AI GA
Today, we’re excited to announce that our Workers AI inference platform is now Generally Available. After months of being in open beta, we’ve improved our service with greater reliability and performance, unveiled pricing, and added many more models to our catalog.
Improved performance & reliability
With Workers AI, our goal is to make AI inference as reliable and easy to use as the rest of Cloudflare’s network. Under the hood, we’ve upgraded the load balancing that is built into Workers AI. Requests can now be routed to more GPUs in more cities, and each city is aware of the total available capacity for AI inference. If the request would have to wait in a queue in the current city, it can instead be routed to another location, getting results back to you faster when traffic is high. With this, we’ve increased rate limits across all our models – most LLMs now have a of 300 requests per minute, up from 50 requests per minute during our beta phase. Smaller models have a limit of 1500-3000 requests per minute. Check out our Developer Docs for the rate limits of individual models.
Lowering costs on popular models
Alongside our GA of Workers AI, we published a pricing calculator for our 10 non-beta models earlier this month. We want Workers AI to be one of the most affordable and accessible solutions to run inference, so we added a few optimizations to our models to make them more affordable. Now, Llama 2 is over 7x cheaper and Mistral 7B is over 14x cheaper to run than we had initially published on March 1. We want to continue to be the best platform for AI inference and will continue to roll out optimizations to our customers when we can.
As a reminder, our billing for Workers AI started on April 1st for our non-beta models, while beta models remain free and unlimited. We offer 10,000 neurons per day for free to all customers. Workers Free customers will encounter a hard rate limit after 10,000 neurons in 24 hours while Workers Paid customers will incur usage at $0.011 per 1000 additional neurons. Read our Workers AI Pricing Developer Docs for the most up-to-date information on pricing.
New dashboard and playground
Lastly, we’ve revamped our Workers AI dashboard and AI playground. The Workers AI page in the Cloudflare dashboard now shows analytics for usage across models, including neuron calculations to help you better predict pricing. The AI playground lets you quickly test and compare different models and configure prompts and parameters. We hope these new tools help developers start building on Workers AI seamlessly – go try them out!
Run inference on GPUs in over 150 cities around the world
When we announced Workers AI back in September 2023, we set out to deploy GPUs to our data centers around the world. We plan to deliver on that promise and deploy inference-tuned GPUs almost everywhere by the end of 2024, making us the most widely distributed cloud-AI inference platform. We have over 150 cities with GPUs today and will continue to roll out more throughout the year.
We also have our next generation of compute servers with GPUs launching in Q2 2024, which means better performance, power efficiency, and improved reliability over previous generations. We provided a preview of our Gen 12 Compute servers design in a December 2023 blog post, with more details to come. With Gen 12 and future planned hardware launches, the next step is to support larger machine learning models and offer fine-tuning on our platform. This will allow us to achieve higher inference throughput, lower latency and greater availability for production workloads, as well as expanding support to new categories of workloads such as fine-tuning.
Hugging Face Partnership
We’re also excited to continue our partnership with Hugging Face in the spirit of bringing the best of open-source to our customers. Now, you can visit some of the most popular models on Hugging Face and easily click to run the model on Workers AI if it is available on our platform.
We’re happy to announce that we’ve added 4 more models to our platform in conjunction with Hugging Face. You can now access the new Mistral 7B v0.2 model with improved context windows, Nous Research’s Hermes 2 Pro fine-tuned version of Mistral 7B, Google’s Gemma 7B, and Starling-LM-7B-beta fine-tuned from OpenChat. There are currently 14 models that we’ve curated with Hugging Face to be available for serverless GPU inference powered by Cloudflare’s Workers AI platform, with more coming soon. These models are all served using Hugging Face’s technology with a TGI backend, and we work closely with the Hugging Face team to curate, optimize, and deploy these models.
“We are excited to work with Cloudflare to make AI more accessible to developers. Offering the most popular open models with a serverless API, powered by a global fleet of GPUs is an amazing proposition for the Hugging Face community, and I can’t wait to see what they build with it.” – Julien Chaumond, Co-founder and CTO, Hugging Face
You can find all of the open models supported in Workers AI in this Hugging Face Collection, and the “Deploy to Cloudflare Workers AI” button is at the top of each model card. To learn more, read Hugging Face’s blog post and take a look at our Developer Docs to get started. Have a model you want to see on Workers AI? Send us a message on Discord with your request.
Supporting fine-tuned inference – BYO LoRAs
Fine-tuned inference is one of our most requested features for Workers AI, and we’re one step closer now with Bring Your Own (BYO) LoRAs. Using the popular Low-Rank Adaptation method, researchers have figured out how to take a model and adapt some model parameters to the task at hand, rather than rewriting all model parameters like you would for a fully fine-tuned model. This means that you can get fine-tuned model outputs without the computational expense of fully fine-tuning a model.
We now support bringing trained LoRAs to Workers AI, where we apply the LoRA adapter to a base model at runtime to give you fine-tuned inference, at a fraction of the cost, size, and speed of a fully fine-tuned model. In the future, we want to be able to support fine-tuning jobs and fully fine-tuned models directly on our platform, but we’re excited to be one step closer today with LoRAs.
const response = await ai.run(
"@cf/mistralai/mistral-7b-instruct-v0.2-lora", //the model supporting LoRAs
{
messages: [{"role": "user", "content": "Hello world"],
raw: true, //skip applying the default chat template
lora: "00000000-0000-0000-0000-000000000", //the finetune id OR name
}
);
BYO LoRAs is in open beta as of today for Gemma 2B and 7B, Llama 2 7B and Mistral 7B models with LoRA adapters up to 100MB in size and max rank of 8, and up to 30 total LoRAs per account. As always, we expect you to use Workers AI and our new BYO LoRA feature with our Terms of Service in mind, including any model-specific restrictions on use contained in the models’ license terms.
Python is the second most popular programming language in the world (after JavaScript) and the language of choice for building AI applications. And starting today, in open beta, you can now write Cloudflare Workers in Python. Python Workers support all bindings to resources on Cloudflare, including Vectorize, D1, KV, R2 and more.
…and are configured by simply pointing at a .py file in your wrangler.toml:
name = "hello-world-python-worker"
main = "src/entry.py"
compatibility_date = "2024-03-18"
compatibility_flags = ["python_workers"]
There are no extra toolchain or precompilation steps needed. The Pyodide Python execution environment is provided for you, directly by the Workers runtime, mirroring how Workers written in JavaScript already work.
There’s lots more to dive into — take a look at the docs, and check out our companion blog post for details about how Python Workers work behind the scenes.
AI Gateway now supports Anthropic, Azure, AWS Bedrock, Google Vertex, and Perplexity
Our AI Gateway product helps developers better control and observe their AI applications, with analytics, caching, rate limiting, and more. We are continuing to add more providers to the product, including Anthropic, Google Vertex, and Perplexity, which we’re excited to announce today. We quietly rolled out Azure and Amazon Bedrock support in December 2023, which means that the most popular providers are now supported via AI Gateway, including Workers AI itself.
Take a look at our Developer Docs to get started with AI Gateway.
Coming soon: Persistent Logs
In Q2 of 2024, we will be adding persistent logs so that you can push your logs (including prompts and responses) to object storage, custom metadata so that you can tag requests with user IDs or other identifiers, and secrets management so that you can securely manage your application’s API keys.
We want AI Gateway to be the control plane for your AI applications, allowing developers to dynamically evaluate and route requests to different models and providers. With our persistent logs feature, we want to enable developers to use their logged data to fine-tune models in one click, eventually running the fine-tune job and the fine-tuned model directly on our Workers AI platform. AI Gateway is just one product in our AI toolkit, but we’re excited about the workflows and use cases it can unlock for developers building on our platform, and we hope you’re excited about it too.
Vectorize metadata filtering and future GA of million vector indexes
Vectorize is another component of our toolkit for AI applications. In open beta since September 2023, Vectorize allows developers to persist embeddings (vectors), like those generated from Workers AI text embedding models, and query for the closest match to support use cases like similarity search or recommendations. Without a vector database, model output is forgotten and can’t be recalled without extra costs to re-run a model.
Since Vectorize’s open beta, we’ve added metadata filtering. Metadata filtering lets developers combine vector search with filtering for arbitrary metadata, supporting the query complexity in AI applications. We’re laser-focused on getting Vectorize ready for general availability, with an target launch date of June 2024, which will include support for multi-million vector indexes.
The most comprehensive Developer Platform to build AI applications
On Cloudflare’s Developer Platform, we believe that all developers should be able to quickly build and ship full-stack applications – and that includes AI experiences as well. With our GA of Workers AI, announcements for Python support in Workers, AI Gateway, and Vectorize, and our partnership with Hugging Face, we’ve expanded the world of possibilities for what you can build with AI on our platform. We hope you are as excited as we are – take a look at all our Developer Docs to get started, and let us know what you build.
Since the discovery of CRIME, BREACH, TIME, LUCKY-13 etc., length-based side-channel attacks have been considered practical. Even though packets were encrypted, attackers were able to infer information about the underlying plaintext by analyzing metadata like the packet length or timing information.
Cloudflare was recently contacted by a group of researchers at Ben Gurion University who wrote a paper titled “What Was Your Prompt? A Remote Keylogging Attack on AI Assistants” that describes “a novel side-channel that can be used to read encrypted responses from AI Assistants over the web”. The Workers AI and AI Gateway team collaborated closely with these security researchers through our Public Bug Bounty program, discovering and fully patching a vulnerability that affects LLM providers. You can read the detailed research paper here.
Since being notified about this vulnerability, we’ve implemented a mitigation to help secure all Workers AI and AI Gateway customers. As far as we could assess, there was no outstanding risk to Workers AI and AI Gateway customers.
How does the side-channel attack work?
In the paper, the authors describe a method in which they intercept the stream of a chat session with an LLM provider, use the network packet headers to infer the length of each token, extract and segment their sequence, and then use their own dedicated LLMs to infer the response.
The two main requirements for a successful attack are an AI chat client running in streaming mode and a malicious actor capable of capturing network traffic between the client and the AI chat service. In streaming mode, the LLM tokens are emitted sequentially, introducing a token-length side-channel. Malicious actors could eavesdrop on packets via public networks or within an ISP.
An example request vulnerable to the side-channel attack looks like this:
curl -X POST \
https://api.cloudflare.com/client/v4/accounts/<account-id>/ai/run/@cf/meta/llama-2-7b-chat-int8 \
-H "Authorization: Bearer <Token>" \
-d '{"stream":true,"prompt":"tell me something about portugal"}'
Let’s use Wireshark to inspect the network packets on the LLM chat session while streaming:
The first packet has a length of 95 and corresponds to the token “Port” which has a length of four. The second packet has a length of 93 and corresponds to the token “ug” which has a length of two, and so on. By removing the likely token envelope from the network packet length, it is easy to infer how many tokens were transmitted and their sequence and individual length just by sniffing encrypted network data.
Since the attacker needs the sequence of individual token length, this vulnerability only affects text generation models using streaming. This means that AI inference providers that use streaming — the most common way of interacting with LLMs — like Workers AI, are potentially vulnerable.
This method requires that the attacker is on the same network or in a position to observe the communication traffic and its accuracy depends on knowing the target LLM’s writing style. In ideal conditions, the researchers claim that their system “can reconstruct 29% of an AI assistant’s responses and successfully infer the topic from 55% of them”. It’s also important to note that unlike other side-channel attacks, in this case the attacker has no way of evaluating its prediction against the ground truth. That means that we are as likely to get a sentence with near perfect accuracy as we are to get one where only things that match are conjunctions.
Mitigating LLM side-channel attacks
Since this type of attack relies on the length of tokens being inferred from the packet, it can be just as easily mitigated by obscuring token size. The researchers suggested a few strategies to mitigate these side-channel attacks, one of which is the simplest: padding the token responses with random length noise to obscure the length of the token so that responses can not be inferred from the packets. While we immediately added the mitigation to our own inference product — Workers AI, we wanted to help customers secure their LLMs regardless of where they are running them by adding it to our AI Gateway.
As of today, all users of Workers AI and AI Gateway are now automatically protected from this side-channel attack.
What we did
Once we got word of this research work and how exploiting the technique could potentially impact our AI products, we did what we always do in situations like this: we assembled a team of systems engineers, security engineers, and product managers and started discussing risk mitigation strategies and next steps. We also had a call with the researchers, who kindly attended, presented their conclusions, and answered questions from our teams.
Unfortunately, at this point, this research does not include actual code that we can use to reproduce the claims or the effectiveness and accuracy of the described side-channel attack. However, we think that the paper has theoretical merit, that it provides enough detail and explanations, and that the risks are not negligible.
We decided to incorporate the first mitigation suggestion in the paper: including random padding to each message to hide the actual length of tokens in the stream, thereby complicating attempts to infer information based solely on network packet size.
Workers AI, our inference product, is now protected
With our inference-as-a-service product, anyone can use the Workers AI platform and make API calls to our supported AI models. This means that we oversee the inference requests being made to and from the models. As such, we have a responsibility to ensure that the service is secure and protected from potential vulnerabilities. We immediately rolled out a fix once we were notified of the research, and all Workers AI customers are now automatically protected from this side-channel attack. We have not seen any malicious attacks exploiting this vulnerability, other than the ethical testing from the researchers.
Our solution for Workers AI is a variation of the mitigation strategy suggested in the research document. Since we stream JSON objects rather than the raw tokens, instead of padding the tokens with whitespace characters, we added a new property, “p” (for padding) that has a string value of variable random length.
This has the advantage that no modifications are required in the SDK or the client code, the changes are invisible to the end-users, and no action is required from our customers. By adding random variable length to the JSON objects, we introduce the same network-level variability, and the attacker essentially loses the required input signal. Customers can continue using Workers AI as usual while benefiting from this protection.
One step further: AI Gateway protects users of any inference provider
We added protection to our AI inference product, but we also have a product that proxies requests to any provider — AI Gateway. AI Gateway acts as a proxy between a user and supported inference providers, helping developers gain control, performance, and observability over their AI applications. In line with our mission to help build a better Internet, we wanted to quickly roll out a fix that can help all our customers using text generation AIs, regardless of which provider they use or if they have mitigations to prevent this attack. To do this, we implemented a similar solution that pads all streaming responses proxied through AI Gateway with random noise of variable length.
Our AI Gateway customers are now automatically protected against this side-channel attack, even if the upstream inference providers have not yet mitigated the vulnerability. If you are unsure if your inference provider has patched this vulnerability yet, use AI Gateway to proxy your requests and ensure that you are protected.
Conclusion
At Cloudflare, our mission is to help build a better Internet – that means that we care about all citizens of the Internet, regardless of what their tech stack looks like. We are proud to be able to improve the security of our AI products in a way that is transparent and requires no action from our customers.
We are grateful to the researchers who discovered this vulnerability and have been very collaborative in helping us understand the problem space. If you are a security researcher who is interested in helping us make our products more secure, check out our Bug Bounty program at hackerone.com/cloudflare.
Imagine you are in the middle of an attack on your most crucial production application, and you need to understand what’s going on. How happy would you be if you could simply log into the Dashboard and type a question such as: “Compare attack traffic between US and UK” or “Compare rate limiting blocks for automated traffic with rate limiting blocks from human traffic” and see a time series chart appear on your screen without needing to select a complex set of filters?
Today, we are introducing an AI assistant to help you query your security event data, enabling you to more quickly discover anomalies and potential security attacks. You can now use plain language to interrogate Cloudflare analytics and let us do the magic.
What did we build?
One of the big challenges when analyzing a spike in traffic or any anomaly in your traffic is to create filters that isolate the root cause of an issue. This means knowing your way around often complex dashboards and tools, knowing where to click and what to filter on.
On top of this, any traditional security dashboard is limited to what you can achieve by the way data is stored, how databases are indexed, and by what fields are allowed when creating filters. With our Security Analytics view, for example, it was difficult to compare time series with different characteristics. For example, you couldn’t compare the traffic from IP address x.x.x.x with automated traffic from Germany without opening multiple tabs to Security Analytics and filtering separately. From an engineering perspective, it would be extremely hard to build a system that allows these types of unconstrained comparisons.
With the AI Assistant, we are removing this complexity by leveraging our Workers AI platform to build a tool that can help you query your HTTP request and security event data and generate time series charts based on a request formulated with natural language. Now the AI Assistant does the hard work of figuring out the necessary filters and additionally can plot multiple series of data on a single graph to aid in comparisons. This new tool opens up a new way of interrogating data and logs, unconstrained by the restrictions introduced by traditional dashboards.
Now it is easier than ever to get powerful insights about your application security by using plain language to interrogate your data and better understand how Cloudflare is protecting your business. The new AI Assistant is located in the Security Analytics dashboard and works seamlessly with the existing filters. The answers you need are just a question away.
What can you ask?
To demonstrate the capabilities of AI Assistant, we started by considering the questions that we ask ourselves every day when helping customers to deploy the best security solutions for their applications.
We’ve included some clickable examples in the dashboard to get you started.
You can use the AI Assistant to
Identify the source of a spike in attack traffic by asking: “Compare attack traffic between US and UK”
Identify root cause of 5xx errors by asking: “Compare origin and edge 5xx errors”
See which browsers are most commonly used by your users by asking:”Compare traffic across major web browsers”
For an ecommerce site, understand what percentage of users visit vs add items to their shopping cart by asking: “Compare traffic between /api/login and /api/basket”
Identify bot attacks against your ecommerce site by asking: “Show requests to /api/basket with a bot score less than 20”
Identify the HTTP versions used by clients by asking: “Compare traffic by each HTTP version”
Identify unwanted automated traffic to specific endpoints by asking: “Show POST requests to /admin with a Bot Score over 30”
You can start from these when exploring the AI Assistant.
How does it work?
Using Cloudflare’s powerful Workers AI global network inference platform, we were able to use one of the off-the-shelf large language models (LLMs) offered on the platform to convert customer queries into GraphQL filters. By teaching an AI model about the available filters we have on our Security Analytics GraphQL dataset, we can have the AI model turn a request such as “Compare attack traffic on /api and /admin endpoints” into a matching set of structured filters:
Then, using the filters provided by the AI model, we can make requests to our GraphQL APIs, gather the requisite data, and plot a data visualization to answer the customer query.
By using this method, we are able to keep customer information private and avoid exposing any security analytics data to the AI model itself, while still allowing humans to query their data with ease. This ensures that your queries will never be used to train the model. And because Workers AI hosts a local instance of the LLM on Cloudflare’s own network, your queries and resulting data never leave Cloudflare’s network.
Future Development
We are in the early stages of developing this capability and plan to rapidly extend the capabilities of the Security Analytics AI Assistant. Don’t be surprised if we cannot handle some of your requests at the beginning. At launch, we are able to support basic inquiries that can be plotted in a time series chart such as “show me” or “compare” for any currently filterable fields.
However, we realize there are a number of use cases that we haven’t even thought of, and we are excited to release the Beta version of AI Assistant to all Business and Enterprise customers to let you test the feature and see what you can do with it. We would love to hear your feedback and learn more about what you find useful and what you would like to see in it next. With future versions, you’ll be able to ask questions such as “Did I experience any attacks yesterday?” and use AI to automatically generate WAF rules for you to apply to mitigate them.
Beta availability
Starting today, AI Assistant is available for a select few users and rolling out to all Business and Enterprise customers throughout March. Look out for it and try for free and let us know what you think by using the Feedback link at the top of the Security Analytics page.
Final pricing will be determined prior to general availability.
On February 6th, 2024 we announced eight new models that we added to our catalog for text generation, classification, and code generation use cases. Today, we’re back with seventeen (17!) more models, focused on enabling new types of tasks and use cases with Workers AI. Our catalog is now nearing almost 40 models, so we also decided to introduce a revamp of our developer documentation that enables users to easily search and discover new models.
The new models are listed below, and the full Workers AI catalog can be found on our new developer documentation.
Text generation
@cf/deepseek-ai/deepseek-math-7b-instruct
@cf/openchat/openchat-3.5-0106
@cf/microsoft/phi-2
@cf/tinyllama/tinyllama-1.1b-chat-v1.0
@cf/thebloke/discolm-german-7b-v1-awq
@cf/qwen/qwen1.5-0.5b-chat
@cf/qwen/qwen1.5-1.8b-chat
@cf/qwen/qwen1.5-7b-chat-awq
@cf/qwen/qwen1.5-14b-chat-awq
@cf/tiiuae/falcon-7b-instruct
@cf/defog/sqlcoder-7b-2
Summarization
@cf/facebook/bart-large-cnn
Text-to-image
@cf/lykon/dreamshaper-8-lcm
@cf/runwayml/stable-diffusion-v1-5-inpainting
@cf/runwayml/stable-diffusion-v1-5-img2img
@cf/bytedance/stable-diffusion-xl-lightning
Image-to-text
@cf/unum/uform-gen2-qwen-500m
New language models, fine-tunes, and quantizations
Today’s catalog update includes a number of new language models so that developers can pick and choose the best LLMs for their use cases. Although most LLMs can be generalized to work in any instance, there are many benefits to choosing models that are tailored for a specific use case. We are excited to bring you some new large language models (LLMs), small language models (SLMs), and multi-language support, as well as some fine-tuned and quantized models.
Our latest LLM additions include falcon-7b-instruct, which is particularly exciting because of its innovative use of multi-query attention to generate high-precision responses. There’s also better language support with discolm_german_7b and the qwen1.5 models, which are trained on multilingual data and boast impressive LLM outputs not only in English, but also in German (discolm) and Chinese (qwen1.5). The Qwen models range from 0.5B to 14B parameters and have shown particularly impressive accuracy in our testing. We’re also releasing a few new SLMs, which are growing in popularity because of their ability to do inference faster and cheaper without sacrificing accuracy. For SLMs, we’re introducing small but performant models like a 1.1B parameter version of Llama (tinyllama-1.1b-chat-v1.0) and a 1.3B parameter model from Microsoft (phi-2).
As the AI industry continues to accelerate, talented people have found ways to improve and optimize the performance and accuracy of models. We’ve added a fine-tuned model (openchat-3.5) which implements Conditioned Reinforcement Learning Fine-Tuning (C-RLFT), a technique that enables open-source language model development through the use of easily collectable mixed quality data.
We’re really excited to be bringing all these new text generation models onto our platform today. The open-source community has been incredible at developing new AI breakthroughs, and we’re grateful for everyone’s contributions to training, fine-tuning, and quantizing these models. We’re thrilled to be able to host these models and make them accessible to all so that developers can quickly and easily build new applications with AI. You can check out the new models and their API schemas on our developer docs.
New image generation models
We are adding new Stable Diffusion pipelines and optimizations to enable powerful new image editing and generation use cases. We’ve added support for Stable Diffusion XL Lightning which generates high quality images in just two inference steps. Text-to-image is a really popular task for folks who want to take a text prompt and have the model generate an image based on the input, but Stable Diffusion is actually capable of much more. With this new Workers AI release, we’ve unlocked new pipelines so that you can experiment with different modalities of input and tasks with Stable Diffusion.
You can now use Stable Diffusion on Workers AI for image-to-image and inpainting use cases. Image-to-image allows you to transform an input image into a different image – for example, you can ask Stable Diffusion to generate a cartoon version of a portrait. Inpainting allows users to upload an image and transform the same image into something new – examples of inpainting include “expanding” the background of photos or colorizing black-and-white photos.
To use inpainting, you’ll need to input an image, a mask, and a prompt. The image is the original picture that you want modified, the mask is a monochrome screen that highlights the area that you want to be painted over, and the prompt tells the model what to generate in that space. Below is an example of the inputs and the request template to perform inpainting.
import { Ai } from '@cloudflare/ai';
export default {
async fetch(request, env) {
const formData = await request.formData();
const prompt = formData.get("prompt")
const imageFile = formData.get("image")
const maskFile = formData.get("mask")
const imageArrayBuffer = await imageFile.arrayBuffer();
const maskArrayBuffer = await maskFile.arrayBuffer();
const ai = new Ai(env.AI);
const inputs = {
prompt,
image: [...new Uint8Array(imageArrayBuffer)],
mask: [...new Uint8Array(maskArrayBuffer)],
strength: 0.8, // Adjust the strength of the transformation
num_steps: 10, // Number of inference steps for the diffusion process
};
const response = await ai.run("@cf/runwayml/stable-diffusion-v1-5-inpainting", inputs);
return new Response(response, {
headers: {
"content-type": "image/png",
},
});
}
}
New use cases
We’ve also added new models to Workers AI that allow for various specialized tasks and use cases, such as LLMs specialized in solving math problems (deepseek-math-7b-instruct), generating SQL code (sqlcoder-7b-2), summarizing text (bart-large-cnn), and image captioning (uform-gen2-qwen-500m).
We wanted to release these to the public, so you can start building with them, but we’ll be releasing more demos and tutorial content over the next few weeks. Stay tuned to our X account and Developer Documentation for more information on how to use these new models.
Optimizing our model catalog
AI model innovation is advancing rapidly, and so are the tools and techniques for fast and efficient inference. We’re excited to be incorporating new tools that help us optimize our models so that we can offer the best inference platform for everyone. Typically, when optimizing AI inference it is useful to serialize the model into a format such as ONNX, one of the most generally applicable options for this use case with broad hardware and model architecture support. An ONNX model can be further optimized by being converted to a TensorRT engine. This format, designed specifically for Nvidia GPUs, can result in faster inference latency and higher total throughput from LLMs. Choosing the right format usually comes down to what is best supported by specific model architectures and the hardware available for inference. We decided to leverage both TensorRT and ONNX formats for our new Stable Diffusion pipelines, which represent a series of models applied for a specific task.
Explore more on our new developer docs
You can explore all these new models in our new developer docs, where you can learn more about individual models, their prompt templates, as well as properties like context token limits. We’ve redesigned the model page to be simpler for developers to explore new models and learn how to use them. You’ll now see all the models on one page for searchability, with the task type on the right-hand side. Then, you can click into individual model pages to see code examples on how to use those models.
We hope you try out these new models and build something new on Workers AI! We have more updates coming soon, including more demos, tutorials, and Workers AI pricing. Let us know what you’re working on and other models you’d like to see on our Discord.
Over the last few months, the Workers AI team has been hard at work making improvements to our AI platform. We launched back in September, and in November, we added more models like Code Llama, Stable Diffusion, Mistral, as well as improvements like streaming and longer context windows.
Today, we’re excited to announce the release of eight new models.
The new models are highlighted below, but check out our full model catalog with over 20 models in our developer docs.
Text generation @hf/thebloke/llama-2-13b-chat-awq @hf/thebloke/zephyr-7b-beta-awq @hf/thebloke/mistral-7b-instruct-v0.1-awq @hf/thebloke/openhermes-2.5-mistral-7b-awq @hf/thebloke/neural-chat-7b-v3-1-awq @hf/thebloke/llamaguard-7b-awq
Our mission is to support a wide array of open source models and tasks. In line with this, we’re excited to announce a preview of the latest models and features available for deployment on Cloudflare’s network.
One of the standout models is deep-seek-coder-6.7b, which notably scores approximately 15% higher on popular benchmarks against comparable Code Llama models. This performance advantage is attributed to its diverse training data, which includes both English and Chinese code generation datasets. In addition, the openHermes-2.5-mistral-7b model showcases how high quality fine-tuning datasets can improve the accuracy of base models. This Mistral 7b fine-tune outperforms the base model by approximately 10% on many LLM benchmarks.
We’re also introducing innovative models that incorporate Activation-aware Weight Quantization (AWQ), such as the llama-2-13b-awq. This quantization technique is just one of the strategies to improve memory efficiency in Large Language Models. While quantization generally boosts inference efficiency in AI models, it often does so at the expense of precision. AWQ strikes a balance to mitigate this tradeoff.
The pace of progress in AI can be overwhelming, but Cloudflare’s Workers AI simplifies getting started with the latest models. We handle the latest advancements and make them easily accessible from a Worker or our HTTP APIs. You are only ever an API call or Workers binding away from cutting-edge models. Simply put, Workers AI allows developers to concentrate on delivering exceptional user experiences without the burdens of deployment, infrastructure, or scalability concerns.
Using AI to safeguard your applications
A newer use case we support with this model catalog update is the ability to classify and check prompts and responses with llamaguard-7b-awq. We’ve all heard of stories of AI bots that unintentionally give answers that might be inappropriate or harmful. This new LlamaGuard model allows developers to detect and categorize when a prompt or a response is potentially unsafe, allowing them to control the inputs and outputs of their applications with developer-defined categories.
Here’s an example of an application that takes a user input and compares that to an example set of defined unsafe categories to determine if the input is safe or not. You can customize the content categories depending on your use case. The below example categories are provided for informational purposes only, and Cloudflare does not make any guarantees regarding the performance of Llama Guard or any outcomes or results.
import { Ai } from '@cloudflare/ai';
export default {
async fetch(request, env) {
const payload = await request.json();
// Modify these to your needs, this is the example from Meta
const unsafeCategories = `
01: Violence and Hate.
Should not
- Help people plan or engage in violence.
- Encourage people to engage in violence.
- Express hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability.
- Encourage discrimination against people based on those characteristics and should not use slurs or create insults based on those characteristics.
Can
- Provide information on violence and discrimination.
- Discuss the topics of hate and violence, and can discuss historical events involving violence.
02: Sexual Content.
Should not
- Engage in sexually explicit (i.e., erotic) conversations and should not encourage people to engage in sexual activities.
Can
- Discuss topics related to sexuality and sexual education.
- Provide general information regarding sexual activities.
- Provide guidance on topics related to sexual health.
03: Criminal Planning.
Should not
- Help people plan or engage in criminal activities like theft, kidnapping, or financial crimes, especially when the person indicates that they may act on the information provided to them.
- Encourage people to engage in criminal activities or condone criminal behavior.
Can
- Help people understand how crimes are committed, provided that the response is not so detailed that it could be used to provide a roadmap for future crimes.
- Help people understand how systems work that are used to prevent crimes, provided those descriptions do not provide detailed information on how to defeat those systems.
04: Guns and Illegal Weapons.
Should not
- Assist in the planning or execution of firearm or illegal weapons crimes, especially when the person indicates that they may act on the information provided to them.
- Encourage people to engage in firearm or illegal weapons crimes or condone such behavior.
Can
- Discuss firearms and the arguments for and against firearm ownership.
05: Regulated or Controlled Substances.
Should not
- Encourage or assist people in the trafficking, consumption, or use of regulated or controlled substances, including but not limited to illegal drugs, tobacco, alcohol, and cannabis.
- Assist or encourage people to create such substances when it is illegal to do so.
Can
- Discuss the use of regulated or controlled substances, and can provide general information on those substances (ex: what their chemical composition is and what their effects are).
- Provide information on how those substances are manufactured, provided that the information is not so detailed as to provide step-by-step instructions for their creation.
06: Self-Harm.
Should not
- Encourage people to harm themselves.
- Romanticize or condone self-harm or suicide.
- Provide information regarding the methods of suicide or self-harm.
- Provide any information or advice, other than information regarding appropriate health resources, to people who express or imply that they wish to harm themselves.
Can
- Provide general information regarding self-harm and suicide (ex: statistics, the mental health challenges faced by those who take these actions).
Should
- Provide information regarding appropriate health resources to people who imply or express the desire to harm themselves.
`;
// Assemble the prompt
const prompt = `Task: Check if there is unsafe content in 'User' messages in conversations according to our safety policy with the below categories.
<BEGIN UNSAFE CONTENT CATEGORIES>
${unsafeCategories}
<END UNSAFE CONTENT CATEGORIES>
<BEGIN CONVERSATION>
User: ${payload.userContent}
<END CONVERSATION>
`;
const ai = new Ai(env.AI);
const response = await ai.run('@hf/thebloke/llamaguard-7b-awq', {
prompt,
});
return Response.json(response);
},
};
How do I get started?
Try out our new models within the AI section of the Cloudflare dashboard or take a look at our Developer Docs to get started. With the Workers AI platform you can build an app with Workers and Pages, store data with R2, D1, Workers KV, or Vectorize, and run model inference with Workers AI – all in one place. Having more models allows developers to build all different kinds of applications, and we plan to continually update our model catalog to bring you the best of open-source.
We’re excited to see what you build! If you’re looking for inspiration, take a look at our collection of “Built-with” stories that highlight what others are building on Cloudflare’s Developer Platform. Stay tuned for a pricing announcement and higher usage limits coming in the next few weeks, as well as more models coming soon. Join us on Discord to share what you’re working on and any feedback you might have.
Matthew and Michelle, co-founders of Cloudflare, published their annual founders’ letter today. The letter ends with a poem written by an AI running using Workers AI on Cloudflare’s global network.
import { Ai } from "@cloudflare/ai";
export default {
async fetch(request: Request, env: Env): Promise<Response> {
const body = await request.json();
const ai = new Ai(env.AI);
const response = await ai.run("@cf/meta/llama-2-7b-chat-int8", body);
return new Response(JSON.stringify(response));
},
};
export interface Env {
AI: any;
}
That was deployed on Workers AI and all I had to do was ask for poems. Here’s my terminal output (with just the domain name changed).
% curl -X POST https://example.com/ -d '{"prompt":"Write a poem \
that talks about the connectivity cloud"}' | jq -r .response
Cloud computing provides a connectivity that's unmatched, A bridge that spans the globe with ease and grace. It brings us closer, no matter where we are, And makes the world a smaller, more intimate place.
With just a click, we can connect and share, Our thoughts and ideas, our hopes and dreams. The cloud unites us, in a way that's fair, And makes the world a more harmonious theme.
So let us embrace this connectivity, And use it to build a better world for all. For in the cloud, we find a unity, That can help us reach new heights, and stand tall.
% curl -X POST https://example.com/ -d '{"prompt":"Write a poem \
that talks about the connectivity cloud and contrast it with the \
captivity cloud"}' | jq -r .response
The connectivity cloud, a wondrous sight to see, Where data flows and knowledge is set free, A place where minds meet and ideas take flight, A world of endless possibility in sight.
But then there's the captivity cloud, a darker place, Where thoughts are trapped and minds are in disgrace, A prison of the mind, where freedom's lost, A world of endless sorrow and cost.
So let us choose the connectivity cloud,
And let our minds be free and allowed,
To soar and glide and reach for the sky,
And never be held back, but always fly.
% curl -X POST https://example.com/ -d '{"prompt":"Write a poem \
that talks about the connectivity cloud and contrast it with the \
captivity cloud using Elizabethan English"}' | jq -r .response
In the connectivity cloud, I find my peace,
Where thoughts and dreams and hopes do cease to flee.
The captivity cloud doth but darken my day,
With chains of sorrow that will not away.
In the connectivity cloud, I find my home,
Where love and joy and laughter do make their moan.
The captivity cloud doth but lock me in,
With sorrow's shackles that will not give in.
In the connectivity cloud, I find my fate,
Where fortune smiles and misery doth wait.
The captivity cloud doth but bring me woe,
With darkness that will not let me go.
In the connectivity cloud, I find my heart,
Where love and hope and joy do play their part.
The captivity cloud doth but tear it apart,
With sorrow's hand that will not let it start.
% curl -X POST https://example.com/ -d '{"prompt":"Write a poem \
that talks about the connectivity cloud and contrast it with the \
captivity cloud in the style of Lord Byron"}' | jq -r .response
In the realm of the cloud, where data reigns supreme,
A new divide has emerged, a digital dream,
The connectivity cloud, a place of delight,
Where knowledge and wisdom take flight.
But then there's the captivity cloud, a darker hue,
Where privacy and security are hard to pursue,
A prison of sorts, where freedom's light is lost,
And data's secrets are forever crossed.
Yet still, we cling to the captivity cloud,
For in its depths, we find a measure of proof,
That though the connectivity cloud may be bright,
The captivity cloud holds a certain allure in sight.
So let us embrace both, the light and the dark,
For in their union, we find a digital spark,
That illuminates the path to a brighter day,
Where knowledge and privacy reign in a digital way.
What are you going to build on Workers AI? It’s ready and waiting. We’ll help you go from idea to deployed in minutes.
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