Chat2DB is an open source and free multi-database client tool that supports local installation on windows and mac, server-side deployment and web page access. Compared with traditional database client software Navicat and DBeaver, Chat2DB integrates the ability of AIGC, which can convert natural language into SQL, and can also convert SQL into natural language, and […]
Chat2DB, a database client tool integrated with AIGC Read More »
Ink is a library that supports using React to build command-line applications—the same component-based UI building experience that React provides in the browser. It’s just that InK is geared towards command-line applications. So Ink is actually a React renderer, which converts the React component tree into a string, and then outputs it to the terminal.
SuperCLUE is an evaluation benchmark for general large models available in Chinese. The main question it answers is: the effect of the Chinese large-scale model under the current situation of vigorous development of the general-purpose large-scale model. including but not limited to: The effect of these models on different tasks To what extent
Chinese-LLaMA-Alpaca contains the Chinese LLaMA model and the Alpaca large model fine-tuned with instructions. Based on the original LLaMA, these models expand the Chinese vocabulary and use Chinese data for secondary pre-training, which further improves the ability to understand the basic semantics of Chinese. At the same time, the Chinese Alpaca model further uses Chinese
OpenLLaMA is an open source rendition of Meta AI’s LLaMA large language model under a permissive license. The repository contains a public preview of the trained 200 billion token 7B OpenLLaMA model, and provides PyTorch and Jax weights for the pretrained OpenLLaMA model, as well as evaluation results and comparisons to the original LLaMA model.
MLC LLM is a general solution that allows any language model to be deployed locally on various hardware backends and native applications. In addition, MLC LLM also provides an efficient framework for users to further optimize model performance according to their needs. MLC LLM is designed to enable everyone to develop, optimize and deploy AI
SM4Utils is a symmetric encryption and decryption tool that encapsulates the national secret SM4 algorithm, supports JavaScript and Java, and this is the Java version.import using icu.xuyijie SM4Utils 1.4.1 Use /** * ECB encryption mode */ //Do not use custom secretKey, generally used for back-end self-encryption//If it is front-end encryption… #National #secret #SM4 #encryption #decryption
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Base64Util canSave the file generated by Base64 decoding to the specified path; or convert the file into Base64 code, supported language: java. <!– https://mvnrepository.com/artifact/icu.xuyijie/Base64Utils –> <dependency> <groupId>icu.xuyijie</groupId> <artifactId>Base64Utils</artifactId> <version>1.2.3</version> </dependency> // 将文件编码成Base64，可传入文件全路径，或者一个 File 对象 String s = Base64Util.transferToBase64(“D:/下载/Screenshot_20221008-090627.png”); File file = new File(filePath); String s = Base64Util.transferToBase64(file); System.out.println(s); // 将Base64转换成文件保存到指定位置，可传入文件全路径或者分别传入保存位置和文件名 String s1 = Base64Util.generateFile(s, “D:/下载/aaa.png”);
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WizardLM is a fine-tuned 7B LLaMA model. It fine-tunes following the dialogue through a large number of commands of varying difficulty. The novelty of this model is the use of LLM to automatically generate training data. The WizardLM model uses a new method called Evol-Instruct (a new method to improve the ability of LLM by
StarCoder (15 billion parameters) is a free large-scale language model released by Hugging Face and ServiceNow. The main purpose of this model is to generate code after training. The purpose is to fight against AI-based programming tools such as GitHub Copilot and Amazon CodeWhisperer. Its training data consists of more than 80 different programming languages
BBT-2 is a general-purpose large language model containing 12 billion parameters. On the basis of BBT-2, professional models such as code, finance, and Vincent graphs have been trained. The series of models based on BBT-2 include: BBT-2-12B-Text: Chinese basic model with 12 billion parameters BBT-2.5-13B-Text: Chinese + English bilingual basic model with 13 billion parameters
SantaCoder is a language model with 1.1 billion parameters that can be used for code generation and completion suggestions in several programming languages such as Python, Java, and JavaScript. According to the official information, the basis for training SantaCoder is The Stack (v1.1) data set. Although SantaCoder is relatively small in size, with only 1.1
“Enlightenment” is a bilingual multimodal pre-training model with a scale of 1.75 trillion parameters.There are currently 7 open source model results in the project, and the model parameter files need to beEnlightenment platformMake a download request. Graphics CogView The CogView parameter is 4 billion. The model can generate images from text, and after fine-tuning, it
Pengcheng Pangu α is the industry’s first 200 billion parameter pre-trained language model with Chinese as the core. Currently, two versions are open source: Pengcheng Pangu α and Pengcheng Pangu α enhanced version, and support both NPU and GPU. It supports rich scene applications, and has outstanding performance in text generation fields such as knowledge
