Whatever language you use, we'll show you how to generate UUIDs in your own code! Just look for your favorite programming language in the list on the left-hand side of the page. UUID Generator is a free online developer tool to generate one or multiple universally unique identifiers (UUIDs). Languages don't have built-in support so you have to use a 3rd party library to generate the UUIDs. Some have a UUID generation function built into the language. Almost all programming languages in existence have some way Well, it depends on which programming language you use. So how do you generate UUIDs in your code? Than generating them directly in your own code. For your special software orīusiness projects where you always need the same set of UUIDs, our batch generation of UUIDs (and downloading andīut what if copy/pasting the UUIDs generated by our site is not practical for your needs? You might not always be able toĪnd what if you need to automatically generate UUIDs as part of one of the features of your application? YouĬould use our API to get UUIDs in your code, but that's going to be factors of magnitude slower Generating those one-off UUIDs you need when configuring software or new gadgets. Also, don’t forget to join our 17k+ ML SubReddit, Discord Channel, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more.Developer's Corner Generate UUIDs/GUIDs with Code Learn how to generate UUIDs in your favorite programming language! Because of its user-friendliness, xTuring is a great choice for people new to LLM and those with more experience.Īccording to the team, xTuring is the best option for tuning big language models since it allows for single and multi-GPU training, uses memory-efficient approaches like LoRA, and has a straightforward interface.Ĭheck out the Github, Project and Reference. All Credit For This Research Goes To the Researchers on This Project. Users may fine-tune their models with a few mouse clicks, and xTuring will do the rest. The tool’s UI is meant to be straightforward to learn and use. Getting started with xTuring couldn’t be easier. In addition, training time was reduced from 40 minutes to 20 minutes per epoch when using LoRA + DeepSpeed or LoRA + DeepSpeed + CPU offloading. The amount of RAM used by the CPU dropped from 14.9 GB to 10.2 GB. While fine-tuning with LoRA + DeepSpeed or LoRA + DeepSpeed + CPU offloading, memory use drops dramatically to 23.7 GB and 21.9 GB on the GPU, respectively. The results demonstrate that training the LLaMA 7B model for 21 hours per epoch with DeepSpeed + CPU offloading consumed 33.5GB of GPU and 190GB of CPU. 52K instructions comprise the dataset, and 335GB of CPU Memory and 4xA100 GPUs were used for testing. The LLaMA 7B model was used as a benchmark for xTuring’s fine-tuning capabilities, and the team compared xTuring to other fine-tuning techniques. Fill bytes with cryptographically secure random bytes. To generate a random UUID : Let bytes be a list with 16 elements of the type byte. By decreasing the amount of memory needed for fine-tuning, LoRA facilitates more rapid and effective model training. The randomUUID () method steps are to return the result of generating a random UUID. Memory-efficient fine-tuning techniques like LoRA are used by xTuring to speed up the learning process and cut down on hardware expenditures by as much as 90%. XTuring’s versatility as a single-GPU or multi-GPU training framework means that users can tailor their models to their specific hardware configurations. □ JOIN the fastest ML Subreddit Community
0 Comments
Leave a Reply. |