Alternatives to ESMFold

LM-Kit.NET is an enterprise-grade toolkit designed for seamlessly integrating generative AI into your .NET applications, fully supporting Windows, Linux, and macOS. Empower your C# and VB.NET projects with a flexible platform that simplifies the creation and orchestration of dynamic AI agents. Leverage efficient Small Language Models for on‑device inference, reducing computational load, minimizing latency, and enhancing security by processing data locally. Experience the power of Retrieval‑Augmented Generation (RAG) to boost accuracy and relevance, while advanced AI agents simplify complex workflows and accelerate development. Native SDKs ensure smooth integration and high performance across diverse platforms. With robust support for custom AI agent development and multi‑agent orchestration, LM‑Kit.NET streamlines prototyping, deployment, and scalability—enabling you to build smarter, faster, and more secure solutions trusted by professionals worldwide.

Language and its role as a means of demonstrating and facilitating understanding - or intelligence, as it is sometimes called - are fundamental to being human. It allows people to express themselves, build memories, and communicate ideas. These are the foundational components of social intelligence. Our teams at DeepMind are interested in the language processing and communication aspects, both for artificial agents and humans. As part of an broader portfolio of AI Research, we believe that the development and study more powerful language models, systems that predict and create text, have tremendous potential to build advanced AI systems. These systems can be used safely and effectively to summarise and provide expert advice, and follow instructions using natural language. Research is needed to determine the potential risks and benefits of language models before they can be developed.

GPT-4o (o for "omni") is an important step towards a more natural interaction between humans and computers. It accepts any combination as input, including text, audio and image, and can generate any combination of outputs, including text, audio and image. It can respond to audio in as little as 228 milliseconds with an average of 325 milliseconds. This is similar to the human response time in a conversation (opens in new window). It is as fast and cheaper than GPT-4 Turbo on text in English or code. However, it has a significant improvement in text in non-English language. GPT-4o performs better than existing models at audio and vision understanding.

Llama 3.3, the latest in the Llama language model series, was developed to push the limits of AI-powered communication and understanding. Llama 3.3, with its enhanced contextual reasoning, improved generation of language, and advanced fine tuning capabilities, is designed to deliver highly accurate responses across diverse applications. This version has a larger dataset for training, refined algorithms to improve nuanced understanding, and reduced biases as compared to previous versions. Llama 3.3 excels at tasks such as multilingual communication, technical explanations, creative writing and natural language understanding. It is an indispensable tool for researchers, developers and businesses. Its modular architecture enables customization in specialized domains and ensures performance at scale.

GPT-3.5 is the next evolution to GPT 3 large language model, OpenAI. GPT-3.5 models are able to understand and generate natural languages. There are four main models available with different power levels that can be used for different tasks. The main GPT-3.5 models can be used with the text completion endpoint. There are models that can be used with other endpoints. Davinci is the most versatile model family. It can perform all tasks that other models can do, often with less instruction. Davinci is the best choice for applications that require a deep understanding of the content. This includes summarizations for specific audiences and creative content generation. These higher capabilities mean that Davinci is more expensive per API call and takes longer to process than other models.

The expansion of large language model has led to significant advancements in natural language processing, understanding and generation. This study introduces a new system that uses Ascend 910 AI processing units and the MindSpore framework in order to train a language with over one trillion parameters, 1.085T specifically, called PanGu-Sigma. This model, which builds on the foundation laid down by PanGu-alpha transforms the traditional dense Transformer model into a sparse model using a concept called Random Routed Experts. The model was trained efficiently on a dataset consisting of 329 billion tokens, using a technique known as Expert Computation and Storage Separation. This led to a 6.3 fold increase in training performance via heterogeneous computer. The experiments show that PanGu-Sigma is a new standard for zero-shot learning in various downstream Chinese NLP tasks.

Gemini 2.0, an advanced AI model developed by Google is designed to offer groundbreaking capabilities for natural language understanding, reasoning and multimodal interaction. Gemini 2.0 builds on the success of Gemini's predecessor by integrating large language processing and enhanced problem-solving, decision-making, and interpretation abilities. This allows it to interpret and produce human-like responses more accurately and nuanced. Gemini 2.0, unlike traditional AI models, is trained to handle a variety of data types at once, including text, code, images, etc. This makes it a versatile tool that can be used in research, education, business and creative industries. Its core improvements are better contextual understanding, reduced biased, and a more effective architecture that ensures quicker, more reliable results. Gemini 2.0 is positioned to be a major step in the evolution AI, pushing the limits of human-computer interactions.

Qwen-7B, also known as Qwen-7B, is the 7B-parameter variant of the large language models series Qwen. Tongyi Qianwen, proposed by Alibaba Cloud. Qwen-7B, a Transformer-based language model, is pretrained using a large volume data, such as web texts, books, code, etc. Qwen-7B is also used to train Qwen-7B Chat, an AI assistant that uses large models and alignment techniques. The Qwen-7B features include: Pre-trained with high quality data. We have pretrained Qwen-7B using a large-scale, high-quality dataset that we constructed ourselves. The dataset contains over 2.2 trillion tokens. The dataset contains plain texts and codes and covers a wide range domains including general domain data as well as professional domain data. Strong performance. We outperform our competitors in a series benchmark datasets that evaluate natural language understanding, mathematics and coding. And more.

The Gemini 1.5 Pro AI Model is a state of the art language model that delivers highly accurate, context aware, and human like responses across a wide range of applications. It excels at natural language understanding, generation and reasoning tasks. The model has been fine-tuned to support tasks such as content creation, code-generation, data analysis, or complex problem-solving. Its advanced algorithms allow it to adapt seamlessly to different domains, conversational styles and languages. The Gemini 1.5 Pro, with its focus on scalability, is designed for both small-scale and enterprise-level implementations. It is a powerful tool to enhance productivity and innovation.

ChatGPT is an OpenAI language model. It can generate human-like responses to a variety prompts, and has been trained on a wide range of internet texts. ChatGPT can be used to perform natural language processing tasks such as conversation, question answering, and text generation. ChatGPT is a pretrained language model that uses deep-learning algorithms to generate text. It was trained using large amounts of text data. This allows it to respond to a wide variety of prompts with human-like ease. It has a transformer architecture that has been proven to be efficient in many NLP tasks. ChatGPT can generate text in addition to answering questions, text classification and language translation. This allows developers to create powerful NLP applications that can do specific tasks more accurately. ChatGPT can also process code and generate it.

GPT-4.5 marks a significant advancement in large language models, building upon the strengths of GPT-4. This iteration is expected to offer a deeper grasp of context, sharper reasoning abilities, and superior multilingual support, resulting in more seamless and intuitive interactions. It may introduce enhanced personalization, adapting dynamically to a user’s tone, writing style, and even subtle emotional cues. With a broader knowledge base and potential real-time learning, GPT-4.5 could excel at delivering the latest information, generating precise content, and tackling complex problems with greater accuracy. The goal of this evolution would be to further blur the boundaries between human and AI communication, making conversations feel even more natural. While these expectations align with the general trajectory of AI progress, official details remain unconfirmed.

Gemini Advanced is an AI model that delivers unmatched performance in natural language generation, understanding, and problem solving across diverse domains. It features a revolutionary neural structure that delivers exceptional accuracy, nuanced context comprehension, and deep reason capabilities. Gemini Advanced can handle complex and multifaceted tasks. From creating detailed technical content to writing code, to providing strategic insights and conducting in-depth analysis of data, Gemini Advanced is designed to handle them all. Its adaptability, scalability and flexibility make it an ideal solution for both enterprise-level and individual applications. Gemini Advanced is a new standard in AI-powered solutions for intelligence, innovation and reliability. Google One also includes 2 TB of storage and access to Gemini, Docs and more. Gemini Advanced offers access to Gemini Deep Research. You can perform real-time and in-depth research on virtually any subject.

GPT-4 (Generative Pretrained Transformer 4) a large-scale, unsupervised language model that is yet to be released. GPT-4, which is the successor of GPT-3, is part of the GPT -n series of natural-language processing models. It was trained using a dataset of 45TB text to produce text generation and understanding abilities that are human-like. GPT-4 is not dependent on additional training data, unlike other NLP models. It can generate text and answer questions using its own context. GPT-4 has been demonstrated to be capable of performing a wide range of tasks without any task-specific training data, such as translation, summarization and sentiment analysis.

Adept is a ML product and research lab that builds general intelligence by enabling computers and humans to work together creatively. Designed and specifically trained to take actions on computers in response your natural language commands. ACT-1 is the first step in a foundation model which can be used with any software tool, API or website. Adept is creating a completely new way to accomplish tasks. It takes your goals in plain language and turns them into action on the software that you use every single day. We believe AI systems should be designed with users in mind -- where machines and people work together to find new solutions, make better decisions, and give us more time to do the things we love.

GPT-3 models are capable of understanding and generating natural language. There are four main models available, each with a different level of power and suitable for different tasks. Ada is the fastest and most capable model while Davinci is our most powerful. GPT-3 models are designed to be used in conjunction with the text completion endpoint. There are models that can be used with other endpoints. Davinci is the most versatile model family. It can perform all tasks that other models can do, often with less instruction. Davinci is the best choice for applications that require a deep understanding of the content. This includes summarizations for specific audiences and creative content generation. These higher capabilities mean that Davinci is more expensive per API call and takes longer to process than other models.

Qwen2.5-VL is an advanced vision-language model in the Qwen series, offering improved visual comprehension and reasoning over its predecessor, Qwen2-VL. It can accurately interpret a wide range of visual elements, including text, charts, icons, and layouts, making it highly effective for complex image and document analysis. Acting as an intelligent visual agent, the model can dynamically interact with tools, analyze extended video content over an hour long, and identify key segments with precision. It also excels in object localization, generating bounding boxes or points with structured JSON outputs for various attributes. Additionally, Qwen2.5-VL supports structured data extraction from documents such as invoices, forms, and tables, benefiting industries like finance and commerce. Available in base and instruct versions across 3B, 7B, and 72B model sizes, it is accessible on platforms like Hugging Face and ModelScope for seamless integration.

OpenAI's mission, which is to ensure artificial general intelligence (AGI), benefits all people. This refers to highly autonomous systems that outperform humans in most economically valuable work. While we will try to build safe and useful AGI, we will also consider our mission accomplished if others are able to do the same. Our API can be used to perform any language task, including summarization, sentiment analysis and content generation. You can specify your task in English or use a few examples. Our constantly improving AI technology is available to you with a simple integration. These sample completions will show you how to integrate with the API.

InstructGPT is an open source framework that trains language models to generate natural language instruction from visual input. It uses a generative, pre-trained transformer model (GPT) and the state of the art object detector Mask R-CNN to detect objects in images. Natural language sentences are then generated that describe the image. InstructGPT has been designed to be useful in all domains including robotics, gaming, and education. It can help robots navigate complex tasks using natural language instructions or it can help students learn by giving descriptive explanations of events or processes.

The ability of large language models to learn in zero- and few shots, despite being trained for hundreds of thousands or even millions of days, has been remarkable. These models are expensive to replicate, due to their high computational cost. The few models that are available via APIs do not allow access to the full weights of the model, making it difficult to study. Open Pre-trained Transformers is a suite decoder-only pre-trained transforms with parameters ranging from 175B to 125M. We aim to share this fully and responsibly with interested researchers. We show that OPT-175B has a carbon footprint of 1/7th that of GPT-3. We will also release our logbook, which details the infrastructure challenges we encountered, as well as code for experimenting on all of the released model.

Yi-Large, a proprietary large language engine developed by 01.AI with a 32k context size and input and output costs of $2 per million tokens. It is distinguished by its advanced capabilities in common-sense reasoning and multilingual support. It performs on par with leading models such as GPT-4 and Claude3 when it comes to various benchmarks. Yi-Large was designed to perform tasks that require complex inference, language understanding, and prediction. It is suitable for applications such as knowledge search, data classifying, and creating chatbots. Its architecture is built on a decoder only transformer with enhancements like pre-normalization, Group Query attention, and has been trained using a large, high-quality, multilingual dataset. The model's versatility, cost-efficiency and global deployment potential make it a strong competitor in the AI market.

DeepSeek-V3 is an advanced AI model built to excel in natural language comprehension, sophisticated reasoning, and decision-making across a wide range of applications. Harnessing innovative neural architectures and vast datasets, it offers exceptional capabilities for addressing complex challenges in fields like research, development, business analytics, and automation. Designed for both scalability and efficiency, DeepSeek-V3 empowers developers and organizations to drive innovation and unlock new possibilities with state-of-the-art AI solutions.

Qwen LLM is a family of large-language models (LLMs), developed by Damo Academy, an Alibaba Cloud subsidiary. These models are trained using a large dataset of text and codes, allowing them the ability to understand and generate text that is human-like, translate languages, create different types of creative content and answer your question in an informative manner. Here are some of the key features of Qwen LLMs. Variety of sizes: Qwen's series includes sizes ranging from 1.8 billion parameters to 72 billion, offering options that meet different needs and performance levels. Open source: Certain versions of Qwen have open-source code, which is available to anyone for use and modification. Qwen is multilingual and can translate multiple languages including English, Chinese and Japanese. Qwen models are capable of a wide range of tasks, including text summarization and code generation, as well as generation and translation.

John Snow Labs Medical LLM is a domain-specific large langauge model (LLM) that revolutionizes the way healthcare organizations harness artificial intelligence. This innovative platform was designed specifically for the healthcare sector, combining cutting edge natural language processing capabilities with a profound understanding of medical terminology and clinical workflows. The result is an innovative tool that allows healthcare providers, researchers and administrators to unlock new insight, improve patient outcomes and drive operational efficiency. The Healthcare LLM's comprehensive training is at the core of its functionality. This includes a vast amount of healthcare data such as clinical notes, research papers and regulatory documents. This specialized training allows for the model to accurately generate and interpret medical text. It is an invaluable tool for tasks such clinical documentation, automated coding and medical research.

BLOOM (autoregressive large language model) is trained to continue text using a prompt on large amounts of text data. It uses industrial-scale computational resources. It can produce coherent text in 46 languages and 13 programming language, which is almost impossible to distinguish from text written by humans. BLOOM can be trained to perform text tasks that it hasn’t been explicitly trained for by casting them as text generation jobs.

CodeGemma consists of powerful lightweight models that are capable of performing a variety coding tasks, including fill-in the middle code completion, code creation, natural language understanding and mathematical reasoning. CodeGemma offers 3 variants: a 7B model that is pre-trained to perform code completion, code generation, and natural language-to code chat. A 7B model that is instruction-tuned for instruction following and natural language-to code chat. You can complete lines, functions, or even entire blocks of code whether you are working locally or with Google Cloud resources. CodeGemma models are trained on 500 billion tokens primarily of English language data taken from web documents, mathematics and code. They generate code that is not only syntactically accurate but also semantically meaningful. This reduces errors and debugging times.

OpenGPT is a German initiative that focuses on developing large AI languages models tailored to European requirements, with an emphasis on versatility, trustworthiness and multilingual capabilities. It also emphasizes open-source accessibility. The project brings together partners to cover the whole generative AI value-chain, from scalable GPU-based infrastructure to data for training large language model to model design, practical applications, and prototypes and proofs-of concept. OpenGPT-X aims at advancing cutting-edge research, with a focus on business applications. This will accelerate the adoption of generative AI within the German economy. The project also stresses responsible AI development to ensure that the models are reliable and aligned with European values and laws. The project provides resources, such as the LLM Workbook and a three part reference guide with examples and resources to help users better understand the key features and characteristics of large AI language model.

Falcon 3 is the latest open-source large language model (LLM) from the Technology Innovation Institute (TII), designed to bring powerful AI capabilities to a wider audience. Built for efficiency, it can run smoothly on lightweight devices, including laptops, without compromising speed or performance. The Falcon 3 ecosystem features four scalable models, each optimized for different applications, and supports multiple languages while maintaining resource efficiency. Excelling in tasks such as reasoning, language comprehension, instruction following, coding, and mathematics, Falcon 3 sets a new benchmark in AI accessibility. With its balance of high performance and low computational requirements, it aims to make advanced AI more available to users across industries.

ChatGLM-6B, a Chinese-English bilingual dialogue model based on General Language Model architecture (GLM), has 6.2 billion parameters. Users can deploy model quantization locally on consumer-grade graphic cards (only 6GB video memory required at INT4 quantization levels). ChatGLM-6B is based on technology similar to ChatGPT and optimized for Chinese dialogue and Q&A. After approximately 1T identifiers for Chinese and English bilingual training and supplemented with supervision and fine-tuning as well as feedback self-help and human feedback reinforcement learning, ChatGLM-6B, with 6.2 billion parameters, has been able generate answers that are in line with human preference.

Our large language model can understand and reason with any observability data including unstructured data so you can quickly restore software and systems back to health. Our LLM is trained to understand and mitigate critical incidents across all types of architectures. This gives enterprise developers access to one of the world's top debugging experts. Our LLM was created to solve the most difficult part of the software development process - debugging incidents in production. Our model does not require any training and can be used with any observability data systems. It can learn from feedback and fine-tune based upon past incidents and patterns within your environment, while keeping your data within your boundaries. Flip can resolve critical incidents in seconds.

Through scientific rigor and human insight, healthcare breakthroughs can change the world, bringing hope to humanity. We believe that AI can help in this area, through collaboration between researchers, healthcare organisations, and the wider ecosystem. Today, we are sharing exciting progress in these initiatives with the announcement that Google's large language model (LLM) for medical applications, called Med PaLM 2, will be available to a limited number of customers. In the coming weeks, it will be available to a small group of Google Cloud users for limited testing. We will explore use cases, share feedback, and investigate safe, responsible and meaningful ways to utilize this technology. Med-PaLM 2, which harnesses Google's LLMs aligned with the medical domain, is able to answer medical questions more accurately and safely. Med-PaLM 2 is the first LLM that has performed at an "expert" level on the MedQA dataset consisting of US Medical Licensing Examination-style questions.

EXAONE, a large-scale language model developed by LG AI Research, aims to nurture "Expert AI" across multiple domains. The Expert AI alliance was formed by leading companies from various fields in order to advance EXAONE's capabilities. Partner companies in the alliance will act as mentors and provide EXAONE with skills, knowledge, data, and other resources to help it gain expertise in relevant fields. EXAONE is akin to an advanced college student who has taken elective courses in general. It requires intensive training to become a specialist in a specific area. LG AI Research has already demonstrated EXAONE’s abilities in real-world applications such as Tilda AI human artist, which debuted at New York Fashion Week. AI applications have also been developed to summarize customer service conversations, and extract information from complex academic documents.

Smaug 72B is an open-source large-language model (LLM), which is known for its key features. High Performance: It is currently ranked first on the Hugging face Open LLM leaderboard. This model has surpassed models such as GPT-3.5 across a range of benchmarks. This means that it excels in tasks such as understanding, responding to and generating text similar to human speech. Open Source: Smaug-72B, unlike many other advanced LLMs is available to anyone for free use and modification, fostering collaboration, innovation, and creativity in the AI community. Focus on Math and Reasoning: It excels at handling mathematical and reasoning tasks. This is attributed to the unique fine-tuning technologies developed by Abacus, the creators Smaug 72B. Based on Qwen 72B: This is a finely tuned version of another powerful LLM, called Qwen 72B, released by Alibaba. It further improves its capabilities. Smaug-72B is a significant advance in open-source AI.

The next generation of the large language model. This release includes modelweights and starting code to pretrained and fine tuned Llama languages models, ranging from 7B-70B parameters. Llama 1 models have a context length of 2 trillion tokens. Llama 2 models have a context length double that of Llama 1. The fine-tuned Llama 2 models have been trained using over 1,000,000 human annotations. Llama 2, a new open-source language model, outperforms many other open-source language models in external benchmarks. These include tests of reasoning, coding and proficiency, as well as knowledge tests. Llama 2 has been pre-trained using publicly available online data sources. Llama-2 chat, a fine-tuned version of the model, is based on publicly available instruction datasets, and more than 1 million human annotations. We have a wide range of supporters in the world who are committed to our open approach for today's AI. These companies have provided early feedback and have expressed excitement to build with Llama 2

Our new flagship model, the Small Language Model (SLM), has all the power and performance that you would expect from a leading LLM. Excels at generalized tasks, instruction-following, and human preferences. The best 70B model available. SuperNova is a generalized task-based AI that can be used for any generalized task. It's similar to Open AI's GPT4o and Claude Sonnet 3.5. SuperNova is trained with the most advanced optimization & learning techniques to generate highly accurate responses. It is the most flexible, cost-effective, and secure language model available. Customers can save up to 95% in total deployment costs when compared with traditional closed-source models. SuperNova can be used to integrate AI in apps and products, as well as for general chat and a variety of other uses. Update your models regularly with the latest open source tech to ensure you're not locked into a single solution. Protect your data using industry-leading privacy features.

DeepSeek-V2, developed by DeepSeek-AI, is a cutting-edge Mixture-of-Experts (MoE) language model designed for cost-effective training and high-speed inference. Boasting a massive 236 billion parameters—though only 21 billion are active per token—it efficiently handles a context length of up to 128K tokens. The model leverages advanced architectural innovations such as Multi-head Latent Attention (MLA) to optimize inference by compressing the Key-Value (KV) cache and DeepSeekMoE to enable economical training via sparse computation. Compared to its predecessor, DeepSeek 67B, it slashes training costs by 42.5%, shrinks the KV cache by 93.3%, and boosts generation throughput by 5.76 times. Trained on a vast 8.1 trillion token dataset, DeepSeek-V2 excels in natural language understanding, programming, and complex reasoning, positioning itself as a premier choice in the open-source AI landscape.

GPT-4 and other foundation models have accelerated AI's development. The most powerful models, however, are closed commercial models or partially open. RedPajama aims to create a set leading, open-source models. Today, we're excited to announce that the first phase of this project is complete: the reproduction of LLaMA's training dataset of more than 1.2 trillion tokens. The most capable foundations models are currently closed behind commercial APIs. This limits research, customization and their use with sensitive information. If the open community can bridge the quality gap between closed and open models, fully open-source models could be the answer to these limitations. Recent progress has been made in this area. AI is in many ways having its Linux moment. Stable Diffusion demonstrated that open-source software can not only compete with commercial offerings such as DALL-E, but also lead to incredible creative results from community participation.

Llama (Large Language Model meta AI) is a state of the art foundational large language model that was created to aid researchers in this subfield. Llama allows researchers to use smaller, more efficient models to study these models. This further democratizes access to this rapidly-changing field. Because it takes far less computing power and resources than large language models, such as Llama, to test new approaches, validate other's work, and explore new uses, training smaller foundation models like Llama can be a desirable option. Foundation models are trained on large amounts of unlabeled data. This makes them perfect for fine-tuning for many tasks. We make Llama available in several sizes (7B-13B, 33B and 65B parameters), and also share a Llama card that explains how the model was built in line with our Responsible AI practices.

Language Understanding (LUIS), a machine learning-based service that builds natural language into apps and bots. Rapidly create custom models that are enterprise-ready and can be continuously improved. Natural language can be added to your apps. LUIS is a language model that interprets conversations to find valuable information. It extracts information from sentences (entities) and interprets user intentions (goals). LUIS is seamlessly integrated with the Azure Bot Service, making creating sophisticated bots easy. You can quickly create and deploy a solution faster by combining powerful developer tools with pre-built apps and entity dictionary, such as Music, Calendar, and Devices. The collective knowledge of the internet is used to create dictionaries. This allows your model to identify valuable information from user conversations. Active learning is used for continuous improvement of the quality of the models.

Code Llama, a large-language model (LLM), can generate code using text prompts. Code Llama, the most advanced publicly available LLM for code tasks, has the potential to improve workflows for developers and reduce the barrier for those learning to code. Code Llama can be used to improve productivity and educate programmers to create more robust, well documented software. Code Llama, a state-of the-art LLM, is capable of generating both code, and natural languages about code, based on both code and natural-language prompts. Code Llama can be used for free in research and commercial purposes. Code Llama is a new model that is built on Llama 2. It is available in 3 models: Code Llama is the foundational model of code; Codel Llama is a Python-specific language. Code Llama-Instruct is a finely tuned natural language instruction interpreter.

Qwen2 is a large language model developed by Qwen Team, Alibaba Cloud. Qwen2 is an extensive series of large language model developed by the Qwen Team at Alibaba Cloud. It includes both base models and instruction-tuned versions, with parameters ranging from 0.5 to 72 billion. It also features dense models and a Mixture of Experts model. The Qwen2 Series is designed to surpass previous open-weight models including its predecessor Qwen1.5 and to compete with proprietary model across a wide spectrum of benchmarks, such as language understanding, generation and multilingual capabilities.

Qwen2.5, an advanced multimodal AI system, is designed to provide highly accurate responses that are context-aware across a variety of applications. It builds on its predecessors' capabilities, integrating cutting edge natural language understanding, enhanced reasoning, creativity and multimodal processing. Qwen2.5 is able to analyze and generate text as well as interpret images and interact with complex data in real-time. It is highly adaptable and excels at personalized assistance, data analytics, creative content creation, and academic research. This makes it a versatile tool that can be used by professionals and everyday users. Its user-centric approach emphasizes transparency, efficiency and alignment with ethical AI.

Qwen2-VL, the latest version in the Qwen model family of vision language models, is based on Qwen2. Qwen2-VL is a newer version of Qwen-VL that has: SoTA understanding of images with different resolutions & ratios: Qwen2-VL reaches state-of-the art performance on visual understanding benchmarks including MathVista DocVQA RealWorldQA MTVQA etc. Understanding videos over 20 min: Qwen2-VL is able to understand videos longer than 20 minutes, allowing for high-quality video-based questions, dialogs, content creation, and more. Agent that can control your mobiles, robotics, etc. Qwen2-VL, with its complex reasoning and decision-making abilities, can be integrated into devices such as mobile phones, robots and other devices for automatic operation using visual environment and text instruction. Multilingual Support - To serve users worldwide, Qwen2-VL supports texts in other languages within images, besides English or Chinese.

OpenELM is a family of open-source language models developed by Apple. It uses a layering strategy to allocate parameters efficiently within each layer of a transformer model. This leads to improved accuracy compared to other open language models. OpenELM was trained using publicly available datasets, and it achieves the best performance for its size.

Inflection AI, a leading artificial intelligence research and technology company, focuses on developing advanced AI systems that interact with humans more naturally and intuitively. The company was founded in 2022 by entrepreneurs like Mustafa Suleyman (one of the cofounders of DeepMind) and Reid Hoffman (co-founder of LinkedIn). Its mission is to make powerful AI accessible and aligned to human values. Inflection AI is a company that specializes in creating large-scale language systems to enhance human-AI interaction. It aims to transform industries from customer service to productivity by designing AI systems that are intelligent, responsive and ethical. The company's focus is on safety, transparency and user control to ensure that their innovations are positive for society while addressing the potential risks associated with AI.

DeepSeek Coder, a cutting edge software tool, is designed to revolutionize data analysis and coding. It allows users to seamlessly integrate data analysis, visualization, and querying into their workflow by leveraging advanced machine-learning algorithms and natural language processing. DeepSeek Coder's intuitive interface allows both novice and experienced coders to efficiently write, optimize, and test code. Its powerful set of features include real-time code completion, intelligent syntax checking, and comprehensive debugging, all designed to streamline coding. DeepSeek Coder can also understand and interpret complex data, allowing users to create sophisticated data-driven apps with ease.

Aya is an open-source, state-of-the art, massively multilingual large language research model (LLM), which covers 101 different languages. This is more than twice the number of languages that are covered by open-source models. Aya helps researchers unlock LLMs' powerful potential for dozens of cultures and languages that are largely ignored by the most advanced models available today. We open-source both the Aya Model, as well as the most comprehensive multilingual instruction dataset with 513 million words covering 114 different languages. This data collection contains rare annotations by native and fluent speakers from around the world. This ensures that AI technology is able to effectively serve a global audience who have had limited access up until now.

AI21 Studio provides API access to Jurassic-1 large-language-models. Our models are used to generate text and provide comprehension features in thousands upon thousands of applications. You can tackle any language task. Our Jurassic-1 models can follow natural language instructions and only need a few examples to adapt for new tasks. Our APIs are perfect for common tasks such as paraphrasing, summarization, and more. Superior results at a lower price without having to reinvent the wheel Do you need to fine-tune your custom model? Just 3 clicks away. Training is quick, affordable, and models can be deployed immediately. Embed an AI co-writer into your app to give your users superpowers. Features like paraphrasing, long-form draft generation, repurposing, and custom auto-complete can increase user engagement and help you to achieve success.

GPT-5 is OpenAI's Generative Pretrained Transformer. It is a large-language model (LLM), which is still in development. LLMs have been trained to work with massive amounts of text and can generate realistic and coherent texts, translate languages, create different types of creative content and answer your question in a way that is informative. It's still not available to the public. OpenAI has not announced a release schedule, but some believe it could launch in 2024. It's expected that GPT-5 will be even more powerful. GPT-4 has already proven to be impressive. It is capable of writing creative content, translating languages and generating text of human-quality. GPT-5 will be expected to improve these abilities, with improved reasoning, factual accuracy and ability to follow directions.

Google AI Studio is an online tool that's free and allows individuals and small groups to create apps and chatbots by using natural language prompting. It allows users to create API keys and prompts for app development. Google AI Studio allows users to discover Gemini Pro's APIs, create prompts and fine-tune Gemini. It also offers generous free quotas, allowing 60 requests a minute. Google has also developed a Generative AI Studio based on Vertex AI. It has models of various types that allow users to generate text, images, or audio content.

Google's Gemini Nano is a lightweight and energy-efficient AI model that delivers high performance even in environments with limited resources. Gemini Nano is a lightweight, energy-efficient AI model designed for edge computing and mobile apps. It combines Google's advanced AI with cutting-edge techniques to deliver seamless performance. It excels at tasks such as voice recognition, natural-language processing, real time translation, and personalized suggestions despite its small size. Gemini Nano is a local data processor that focuses on privacy and efficiency. This minimizes reliance on cloud infrastructure, while maintaining robust security. Its adaptability, low power consumption, and robust security make it a great choice for smart devices and IoT ecosystems.