GPT-3 Alternatives

Dialogflow from Google Cloud is a natural language understanding platform that makes it easy to design and integrate a conversational user interface into your mobile app, web application, device, bot, interactive voice response system, and so on. Using Dialogflow, you can provide new and engaging ways for users to interact with your product. Dialogflow can analyze multiple types of input from your customers, including text or audio inputs (like from a phone or voice recording). It can also respond to your customers in a couple of ways, either through text or with synthetic speech. Dialogflow CX and ES provide virtual agent services for chatbots and contact centers. If you have a contact center that employs human agents, you can use Agent Assist to help your human agents. Agent Assist provides real-time suggestions for human agents while they are in conversations with end-user customers.

Artificial intelligence makes it fast & easy to create content for your blog, social media, website, and more! Rated 5/5 stars in 3,000+ reviews. We consulted with the world’s best SEO and direct response marketing experts to teach Jasper how to write blog articles, social media posts, website copy, and more. Create original content that ranks for SEO. Generate educational blog articles that are keyword-rich and plagiarism-free. Speed up your content pipeline by writing 80% by Jasper and 20% edited by humans. Easily write and test more copy variations to increase sales and improve ROAS. Boost ad conversions with a better copy. No matter your native tongue, write creatively and clearly in 25+ languages. Repurpose existing content and generate new content without hiring junior writers. Interacting with artificial intelligence used to feel difficult, overwhelming, and a bit robotic... Now with Jasper Chat, have a natural conversation with AI that feels surprisingly human.

💡 1min.AI is an all-in-one AI app that unlock all AI features. You pay only for what you use at 1min.AI, with no hidden costs or setup required elsewhere. 🔮 The unique features of 1min.AI is offering a variety of AI features powered by various AI models. You can see it clearly with the Chat with Many Assistants feature, it includes Gemini, GPT, Claude, Llama, MistralAI, ... 🪄 Other multi-media features like Content, Image, Audio, Video can also be used with different models to utilize their abilities and give out the best results. 💰 Lastly, we offer credit estimation and transparent usage history, so you know exact how does the feature cost before running and can track the usage easily. 🚀 Try for Free and get what you want within 1min

A/B testing can come at a price — literally. You have to pay your dues and put in time to understand what works. Anyword’s Predictive Performance Score shows you what’s most likely to work, cutting down testing costs and saving you time and resources. As marketers we are in a constant battle to optimize our communication channels. Text is usually the best place to start, but it’s also incredibly time consuming. Anyword accelerates workflows by generating multiple text variations at scale so you can get more done and move on. Anyone who ever bought a pair of jeans knows that one size does not fit all. Same goes for marketing text – different channels require different messaging. Anyword lets you generate messages that are tailored to specific platforms, and helps you perfect them so the right people take notice.

Use hundreds of beautiful templates or start from scratch to create content, brand assets, and videos effortlessly. Then, publish with one click to reach your customers wherever they are. The tools that work for you, work for us. Connect your favorite apps to Simplified and watch your productivity soar. Our automation tools handle the little things so you can focus on the big picture. Create your content, collaborate with your team, and publish to the world, all in the same space. Get your team on the same (web) page and tag, comment, and collaborate in real-time. Automate your to-do's and get things done, fast. Scale your content from 1 to 1000, in a few clicks. Your audience gets consistent, beautiful messaging, while you get extra time to focus on other things.

BERT is a large language model and a method of pre-training language representations. Pre-training refers to how BERT is first trained on a large source of text, such as Wikipedia. You can then apply the training results to other Natural Language Processing (NLP) tasks, such as question answering and sentiment analysis. With BERT and AI Platform Training, you can train a variety of NLP models in about 30 minutes.

BLOOM is an autoregressive Large Language Model (LLM), trained to continue text from a prompt on vast amounts of text data using industrial-scale computational resources. As such, it is able to output coherent text in 46 languages and 13 programming languages that is hardly distinguishable from text written by humans. BLOOM can also be instructed to perform text tasks it hasn't been explicitly trained for, by casting them as text generation tasks.

AI21 Studio provides API access to Jurassic-1 large-language-models. Our models power text generation and comprehension features in thousands of live applications. Take on any language task. Our Jurassic-1 models are trained to follow natural language instructions and require just a few examples to adapt to new tasks. Use our specialized APIs for common tasks like summarization, paraphrasing and more. Access superior results at a lower cost without reinventing the wheel. Need to fine-tune your own custom model? You're just 3 clicks away. Training is fast, affordable and trained models are deployed immediately. Give your users superpowers by embedding an AI co-writer in your app. Drive user engagement and success with features like long-form draft generation, paraphrasing, repurposing and custom auto-complete.

An AI assistant that come with subscription sharing feature, invite anyone you want to collaborate with you. Powered by ChatGPT API and GPT-4, it's like a ChatGPT Plus business plan, one subscription for all. Create a personalized AI assistant for your needs. Create and save multiple AI assistant prompts for future use. Simplify collaboration on AI assistant, manage and see team members usage and assistants response all from one account. No more copy and paste to share. Use AIForAll to generate AI images, convert text to speech, speech to text, write blogs and emails, plan business trips, summarize meeting notes, and so much more. Improve productivity and efficient collaboration by using AIForAll. Download, share, and start saving money on ChatGPT Plus subscription by using AIForAll. Available on iPhone, iPad, Mac.

Lumi provides revolutionary interaction capabilities with unstructured data and information for your organization’s growth. It is a conversational module built on top of our base AI-model “Luminous”. Simply connect Lumi to your data and information and everything is ready! Benefits of developing or implementing your conversational agent with our Lumi module. Connect your data and instantly interact with it. Lumi doesn’t learn from your data. Infuse character traits to fit customers’ unique language styles. Ask a question in German, even if the dataset is in English. Data language doesn’t matter anymore. Reach everyone, even if their grammar and spelling contain errors. Learn to trust where Lumi gets is answers from. Use our knowledge worker modules to provide superior access and handling of unstructured data and information to produce digital tools and products for your value creation. It is built on top of our base AI-model “Luminous” and essentially gathers and analyzes data.

Alpa aims to automate large-scale distributed training and serving with just a few lines of code. Alpa was initially developed by folks in the Sky Lab, UC Berkeley. Some advanced techniques used in Alpa have been written in a paper published in OSDI'2022. Alpa community is growing with new contributors from Google. A language model is a probability distribution over sequences of words. It predicts the next word based on all the previous words. It is useful for a variety of AI applications, such the auto-completion in your email or chatbot service. For more information, check out the language model wikipedia page. GPT-3 is very large language model, with 175 billion parameters, that uses deep learning to produce human-like text. Many researchers and news articles described GPT-3 as "one of the most interesting and important AI systems ever produced". GPT-3 is gradually being used as a backbone in the latest NLP research and applications.

Switching is as easy as changing one line of code. Feature parity with industry standard APIs means your product works the same but faster. GooseAI is a fully managed NLP-as-a-Service, delivered via API. It is comparable to OpenAI in this regard. And even more, it is fully compatible with OpenAI's completion API! Our state-of-the-art selection of GPT-based language models and uncompromising speed will give you a jumpstart when starting your next project or offer a flexible alternative to your current provider. We're proud to be able to offer costs that are up to 70% cheaper than other providers, at the same or even better performance. Like the Mitochondria is the powerhouse of the cell, geese are an integral part of the ecosystem. Their beauty and elegance inspired us to fly high - like geese.

LLaMA (Large Language Model Meta AI) is a state-of-the-art foundational large language model designed to help researchers advance their work in this subfield of AI. Smaller, more performant models such as LLaMA enable others in the research community who don’t have access to large amounts of infrastructure to study these models, further democratizing access in this important, fast-changing field. Training smaller foundation models like LLaMA is desirable in the large language model space because it requires far less computing power and resources to test new approaches, validate others’ work, and explore new use cases. Foundation models train on a large set of unlabeled data, which makes them ideal for fine-tuning for a variety of tasks. We are making LLaMA available at several sizes (7B, 13B, 33B, and 65B parameters) and also sharing a LLaMA model card that details how we built the model in keeping with our approach to Responsible AI practices.

Magic’s LTM-1 enables 50x larger context windows than transformers. Magic's trained a Large Language Model (LLM) that’s able to take in the gigantic amounts of context when generating suggestions. For our coding assistant, this means Magic can now see your entire repository of code. Larger context windows can allow AI models to reference more explicit, factual information and their own action history. We hope to be able to utilize this research to improve reliability and coherence.

LaMDA, our latest research breakthrough, adds pieces to one of the most tantalizing sections of that puzzle: conversation. While conversations tend to revolve around specific topics, their open-ended nature means they can start in one place and end up somewhere completely different. A chat with a friend about a TV show could evolve into a discussion about the country where the show was filmed before settling on a debate about that country’s best regional cuisine. That meandering quality can quickly stump modern conversational agents (commonly known as chatbots), which tend to follow narrow, pre-defined paths. But LaMDA — short for “Language Model for Dialogue Applications” — can engage in a free-flowing way about a seemingly endless number of topics, an ability we think could unlock more natural ways of interacting with technology and entirely new categories of helpful applications.

Megatron-Turing Natural Language Generation model (MT-NLG), is the largest and the most powerful monolithic transformer English language model with 530 billion parameters. This 105-layer, transformer-based MT-NLG improves upon the prior state-of-the-art models in zero-, one-, and few-shot settings. It demonstrates unmatched accuracy in a broad set of natural language tasks such as, Completion prediction, Reading comprehension, Commonsense reasoning, Natural language inferences, Word sense disambiguation, etc. With the intent of accelerating research on the largest English language model till date and enabling customers to experiment, employ and apply such a large language model on downstream language tasks - NVIDIA is pleased to announce an Early Access program for its managed API service to MT-NLG mode.

We believe that the most powerful and differentiated applications will not only call out to a language model via an API. There are several main modules that LangChain provides support for. For each module we provide some examples to get started, how-to guides, reference docs, and conceptual guides. Memory is the concept of persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory. Language models are often more powerful when combined with your own text data - this module covers best practices for doing exactly that.

Claude is an artificial intelligence large language model that can process and generate human-like text. Anthropic is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Large, general systems of today can have significant benefits, but can also be unpredictable, unreliable, and opaque: our goal is to make progress on these issues. For now, we’re primarily focused on research towards these goals; down the road, we foresee many opportunities for our work to create value commercially and for public benefit.

DeepL is a deep learning company that develops AI systems for languages and communication. Our mission is to bring AI products of the future to everyone, right now. The company, based in Cologne, Germany, was founded in 2009 as Linguee, and introduced the first internet search engine for translations. Linguee has answered over 10 billion queries from more than 1 billion users. In the summer of 2017, the company introduced DeepL Translator, a free machine translation system that, thanks to a revolutionary neural architecture, produces translations of unprecedented quality. DeepL employs a dedicated team of machine learning researchers, developers and language experts, who all appreciate the importance of communication in a multilingual world and understand the complexity of automated translation. Our goal is to become Europe´s leading AI company. We develop AI to expand human possibility and bring cultures closer together.

Sparrow is a research model and proof of concept, designed with the goal of training dialogue agents to be more helpful, correct, and harmless. By learning these qualities in a general dialogue setting, Sparrow advances our understanding of how we can train agents to be safer and more useful – and ultimately, to help build safer and more useful artificial general intelligence (AGI). Sparrow is not yet available for public use. Training a conversational AI is an especially challenging problem because it’s difficult to pinpoint what makes a dialogue successful. To address this problem, we turn to a form of reinforcement learning (RL) based on people's feedback, using the study participants’ preference feedback to train a model of how useful an answer is. To get this data, we show our participants multiple model answers to the same question and ask them which answer they like the most.

Build natural language understanding and generation into your product with a few lines of code. The Cohere API provides access to models that read billions of web pages and learn to understand the meaning, sentiment, and intent of the words we use. Use the Cohere API to write human-like text by completing a prompt or filling in blanks. You can write copy, generate code, summarize text, and more. Compute the likelihood of text and retrieve representations from the model. Use the likelihood API to filter text based on chosen categories or selected criteria. With representations, you can train your own downstream models on a wide variety of domain-specific natural language tasks. The Cohere API can compute the similarity between pieces of text, and make categorical predictions by comparing the likelihood of different text options. The model has multiple lenses through which to view ideas, so that it can recognize abstract similarities between concepts as distinct as DNA and computers.

OpenAI’s mission is to ensure that artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity. We will attempt to directly build safe and beneficial AGI, but will also consider our mission fulfilled if our work aids others to achieve this outcome. Apply our API to any language task — semantic search, summarization, sentiment analysis, content generation, translation, and more — with only a few examples or by specifying your task in English. One simple integration gives you access to our constantly-improving AI technology. Explore how you integrate with the API with these sample completions.

Proficient in more than a dozen programming languages, Codex can now interpret simple commands in natural language and execute them on the user’s behalf—making it possible to build a natural language interface to existing applications. We are now inviting businesses and developers to build on top of OpenAI Codex through our API. OpenAI Codex is a descendant of GPT-3; its training data contains both natural language and billions of lines of source code from publicly available sources, including code in public GitHub repositories. OpenAI Codex is most capable in Python, but it is also proficient in over a dozen languages including JavaScript, Go, Perl, PHP, Ruby, Swift and TypeScript, and even Shell. It has a memory of 14KB for Python code, compared to GPT-3 which has only 4KB—so it can take into account over 3x as much contextual information while performing any task.

Fast and accurate AI models suited for production. Highly-available inference API leveraging the most advanced NVIDIA GPUs. We selected the best open-source natural language processing (NLP) models from the community and deployed them for you. Fine-tune your own models - including GPT-J - or upload your in-house custom models, and deploy them easily to production. Upload or Train/Fine-Tune your own AI models - including GPT-J - from your dashboard, and use them straight away in production without worrying about deployment considerations like RAM usage, high-availability, scalability... You can upload and deploy as many models as you want to production.

Access the limitless power of AI, right inside Notion. Work faster. Write better. Think bigger. Messy notes? Have Notion AI summarize what’s important and actionable. No need to jump back and forth between your notes and a separate AI app. Analyze meeting notes to generate next steps, instantly. Write a summary in 30 seconds, not 30 minutes. Surface what’s important from research, sales calls & more. Notion AI is available to all users. You can prompt AI using your space bar, highlighting text and selecting "Ask AI", or via slash commands. You must take action for AI to activate. It will not generate content or send requests to our AI providers unless you perform one of the actions above. You are in control. Compared to other tools, Notion has a unique and highly flexible drag-and-drop text editor, where any AI-generated content can be easily rearranged and transformed. Over time, Notion AI will be able to take advantage of even more Notion features.

PaLM API is an easy and safe way to build on top of our best language models. Today, we’re making an efficient model available, in terms of size and capabilities, and we’ll add other sizes soon. The API also comes with an intuitive tool called MakerSuite, which lets you quickly prototype ideas and, over time, will have features for prompt engineering, synthetic data generation and custom-model tuning — all supported by robust safety tools. Select developers can access the PaLM API and MakerSuite in Private Preview today, and stay tuned for our waitlist soon.

Significant advancements in the field of natural language processing, understanding, and generation have been achieved through the expansion of large language models. This study introduces a system which utilizes Ascend 910 AI processors and the MindSpore framework to train a language model with over a trillion parameters, specifically 1.085T, named PanGu-{\Sigma}. This model, which builds upon the foundation laid by PanGu-{\alpha}, takes the traditionally dense Transformer model and transforms it into a sparse one using a concept known as Random Routed Experts (RRE). The model was efficiently trained on a dataset of 329 billion tokens using a technique called Expert Computation and Storage Separation (ECSS), leading to a 6.3-fold increase in training throughput via heterogeneous computing. Experimentation indicates that PanGu-{\Sigma} sets a new standard in zero-shot learning for various downstream Chinese NLP tasks.

PanGu-α is developed under the MindSpore and trained on a cluster of 2048 Ascend 910 AI processors. The training parallelism strategy is implemented based on MindSpore Auto-parallel, which composes five parallelism dimensions to scale the training task to 2048 processors efficiently, including data parallelism, op-level model parallelism, pipeline model parallelism, optimizer model parallelism and rematerialization. To enhance the generalization ability of PanGu-α, we collect 1.1TB high-quality Chinese data from a wide range of domains to pretrain the model. We empirically test the generation ability of PanGu-α in various scenarios including text summarization, question answering, dialogue generation, etc. Moreover, we investigate the effect of model scales on the few-shot performances across a broad range of Chinese NLP tasks. The experimental results demonstrate the superior capabilities of PanGu-α in performing various tasks under few-shot or zero-shot settings.

State-of-the-art language models are extremely challenging to train; they require huge compute budgets, complex distributed compute techniques and deep ML expertise. As a result, few organizations train large language models (LLMs) from scratch. And increasingly those that have the resources and expertise are not open sourcing the results, marking a significant change from even a few months back. At Cerebras, we believe in fostering open access to the most advanced models. With this in mind, we are proud to announce the release to the open source community of Cerebras-GPT, a family of seven GPT models ranging from 111 million to 13 billion parameters. Trained using the Chinchilla formula, these models provide the highest accuracy for a given compute budget. Cerebras-GPT has faster training times, lower training costs, and consumes less energy than any publicly available model to date.

A revolutionary AI like ChatGPT - ChatSonic, the conversational AI chatbot addresses the limitations of ChatGPT, turning out to be the best Chat GPT alternative. Improving upon the limitations of Chat GPT and giving conversational AI wings with ChatSonic. ChatSonic is trained and powered by ‘Google Search’ to chat with you on current events and trending topics in real-time. ChatSonic - ChatGPT alternative can help generate stunning digital AI artwork for your social media posts and digital campaigns. A personal assistant you can customize and use whether you are solving a math problem, preparing for an interview, sorting relationship problems or helping you stay fit. Add the ChatSonic ChatGPT Chrome extension to get content suggestions from anywhere on the internet. ChatSonic understands voice commands and responds just like Siri / Google Assistant.

Chinchilla is a large language model. Chinchilla uses the same compute budget as Gopher but with 70B parameters and 4× more more data. Chinchilla uniformly and significantly outperforms Gopher (280B), GPT-3 (175B), Jurassic-1 (178B), and Megatron-Turing NLG (530B) on a large range of downstream evaluation tasks. This also means that Chinchilla uses substantially less compute for fine-tuning and inference, greatly facilitating downstream usage. As a highlight, Chinchilla reaches a state-of-the-art average accuracy of 67.5% on the MMLU benchmark, greater than a 7% improvement over Gopher.

Exclusive to Amazon Bedrock, the Amazon Titan family of models incorporates Amazon’s 25 years of experience innovating with AI and machine learning across its business. Amazon Titan foundation models (FMs) provide customers with a breadth of high-performing image, multimodal, and text model choices, via a fully managed API. Amazon Titan models are created by AWS and pretrained on large datasets, making them powerful, general-purpose models built to support a variety of use cases, while also supporting the responsible use of AI. Use them as is or privately customize them with your own data. Amazon Titan Text Premier is a powerful and advanced model within the Amazon Titan Text family, designed to deliver superior performance across a wide range of enterprise applications. This model is optimized for integration with Agents and Knowledge Bases for Amazon Bedrock, making it an ideal option for building interactive generative AI applications.

A new way to automatically train, evaluate and deploy state-of-the-art Machine Learning models. AutoTrain is an automatic way to train and deploy state-of-the-art Machine Learning models, seamlessly integrated with the Hugging Face ecosystem. Your training data stays on our server, and is private to your account. All data transfers are protected with encryption. Available today: text classification, text scoring, entity recognition, summarization, question answering, translation and tabular. CSV, TSV or JSON files, hosted anywhere. We delete your training data after training is done. Hugging Face also hosts an AI content detection tool.

Jounce is the answer to the overwhelming gap between the 55 million marketers and the 600 thousand copywriters, providing the most advanced AI technology to marketers around the globe. Jounce is an easy-to-use, AI-powered copywriting solution that helps you create professional, effective content in just a few simple steps. Whether you're a marketing professional, small business owner, or content creator, Jounce is the perfect tool to help you save time and improve the quality of your copy. Choose from a variety of customizable templates to kickstart your copywriting process. Input your desired prompt or guidelines and Jounce AI will handle the rest. Jounce will generate a variety of options for you to choose from. Simply select your favorite output and you're done! With its intuitive design, ease of use, and lightning-fast speed, you'll find yourself using Jounce all day long.

Jurassic-1 models come in two sizes, where the Jumbo version, at 178B parameters, is the largest and most sophisticated language model ever released for general use by developers. AI21 Studio is currently in open beta, allowing anyone to sign up and immediately start querying Jurassic-1 using our API and interactive web environment. Our mission at AI21 Labs is to fundamentally reimagine the way humans read and write by introducing machines as thought partners, and the only way we can achieve this is if we take on this challenge together. We’ve been researching language models since our Mesozoic Era (aka 2017 😉). Jurassic-1 builds on this research, and it is the first generation of models we’re making available for widespread use.

Announcing the launch of Jurassic-2, the latest generation of AI21 Studio’s foundation models, a game-changer in the field of AI, with top-tier quality and new capabilities. And that's not all, we're also releasing our task-specific APIs, with plug-and-play reading and writing capabilities that outperform competitors. Our focus at AI21 Studio is to help developers and businesses leverage reading and writing AI to build real-world products with tangible value. Today marks two important milestones with the release of Jurassic-2 and Task-Specific APIs, empowering you to bring generative AI to production. Jurassic-2 (or J2, as we like to call it) is the next generation of our foundation models with significant improvements in quality and new capabilities including zero-shot instruction-following, reduced latency, and multi-language support. Task-specific APIs provide developers with industry-leading APIs that perform specialized reading and writing tasks out-of-the-box.

Kafkai is an AI Writer Assistant that helps you create unique SEO-friendly articles for cents instead of dollars. There is no scraping or spinning happening in the background. Kafkai is a machine-learning algorithm that can write articles from scratch. Cutting-edge technology for marketers and SEOs. Quality content for cents instead of dollars. Since the very beginning of Search Engine Optimization, marketers have been looking for ways to get unique & quality content fast and cheap. We need it for blog posts, PBNs, backlinks, and anything else that can satisfy search engines. With a stream of unique content, you can build money-making affiliate sites and rank any website with your PBNs and other backlinks. We've been in the SEO industry for more than a decade, and we know it requires tons of quality content to satisfy search engines. We also know relevancy is critical, so we took the general writing models and trained them on popular SEO niches.

InferKit offers a web interface and API for AI–based text generators. Whether you're a novelist looking for inspiration, or an app developer, there's something for you. InferKit's text generation tool takes text you provide and generates what it thinks comes next, using a state-of-the-art neural network. It's configurable and can produce any length of text on practically any topic. The tool can be used through either the web interface or the developer API. Get started by creating an account. Creative and fun uses of the network include writing stories or poetry. Other use cases might be marketing or auto-completion. The generator can only comprehend a certain amount of text at a time (currently at most 3000 characters) so if you give it a longer prompt then it won't use the beginning. The network is already trained and does not learn from the inputs you give it. Each request counts for a minimum of 100 characters.

InstructGPT is an open-source framework for training language models to generate natural language instructions from visual input. It uses a generative pre-trained transformer (GPT) model and the state-of-the-art object detector, Mask R-CNN, to detect objects in images and generate natural language sentences that describe the image. InstructGPT is designed to be effective across domains such as robotics, gaming and education; it can assist robots in navigating complex tasks with natural language instructions, or help students learn by providing descriptive explanations of processes or events.

Ernie Bot (Wenxin Yiyan) is an upcoming conversational AI chatbot from Baidu designed to give human-like and accurate answers to any type of questions a user might have.

FLAN-T5 was released in the paper Scaling Instruction-Finetuned Language Models - it is an enhanced version of T5 that has been finetuned in a mixture of tasks.

Falcon-7B is a 7B parameters causal decoder-only model built by TII and trained on 1,500B tokens of RefinedWeb enhanced with curated corpora. It is made available under the Apache 2.0 license. Why use Falcon-7B? It outperforms comparable open-source models (e.g., MPT-7B, StableLM, RedPajama etc.), thanks to being trained on 1,500B tokens of RefinedWeb enhanced with curated corpora. See the OpenLLM Leaderboard. It features an architecture optimized for inference, with FlashAttention and multiquery. It is made available under a permissive Apache 2.0 license allowing for commercial use, without any royalties or restrictions.

ESMFold shows how AI can give us new tools to understand the natural world, much like the microscope, which enabled us to see into the world at an infinitesimal scale and opened up a whole new understanding of life. AI can help us understand the immense scope of natural diversity, and see biology in a new way. Much of AI research has focused on helping computers understand the world in a way similar to how humans do. The language of proteins is one that is beyond human comprehension and has eluded even the most powerful computational tools. AI has the potential to open up this language to our understanding. Studying AI in new domains such as biology can also give insight into artificial intelligence more broadly. Our work reveals connections across domains: large language models that are behind advances in machine translation, natural language understanding, speech recognition, and image generation are also able to learn deep information about biology.

GPT-3.5 is the next evolution of GPT 3 large language model from OpenAI. GPT-3.5 models can understand and generate natural language. We offer four main models with different levels of power suitable for different tasks. The main GPT-3.5 models are meant to be used with the text completion endpoint. We also offer models that are specifically meant to be used with other endpoints. Davinci is the most capable model family and can perform any task the other models can perform and often with less instruction. For applications requiring a lot of understanding of the content, like summarization for a specific audience and creative content generation, Davinci is going to produce the best results. These increased capabilities require more compute resources, so Davinci costs more per API call and is not as fast as the other models.

GPT-4 (Generative Pre-trained Transformer 4) is a large-scale unsupervised language model, yet to be released by OpenAI. GPT-4 is the successor to GPT-3 and part of the GPT-n series of natural language processing models, and was trained on a dataset of 45TB of text to produce human-like text generation and understanding capabilities. Unlike most other NLP models, GPT-4 does not require additional training data for specific tasks. Instead, it can generate text or answer questions using only its own internally generated context as input. GPT-4 has been shown to be able to perform a wide variety of tasks without any task specific training data such as translation, summarization, question answering, sentiment analysis and more.

GPT-5 is the anticipated next iteration of OpenAI's Generative Pre-trained Transformer, a large language model (LLM) still under development. LLMs are trained on massive amounts of text data and are able to generate realistic and coherent text, translate languages, write different kinds of creative content, and answer your questions in an informative way. It's not publicly available yet. OpenAI hasn't announced a release date, but some speculate it could be launched sometime in 2024. It's expected to be even more powerful than its predecessor, GPT-4. GPT-4 is already impressive, capable of generating human-quality text, translating languages, and writing different kinds of creative content. GPT-5 is expected to take these abilities even further, with better reasoning, factual accuracy, and ability to follow instructions.

GPT-J is a cutting-edge language model created by the research organization EleutherAI. In terms of performance, GPT-J exhibits a level of proficiency comparable to that of OpenAI's renowned GPT-3 model in a range of zero-shot tasks. Notably, GPT-J has demonstrated the ability to surpass GPT-3 in tasks related to generating code. The latest iteration of this language model, known as GPT-J-6B, is built upon a linguistic dataset referred to as The Pile. This dataset, which is publicly available, encompasses a substantial volume of 825 gibibytes of language data, organized into 22 distinct subsets. While GPT-J shares certain capabilities with ChatGPT, it is important to note that GPT-J is not designed to operate as a chatbot; rather, its primary function is to predict text. In a significant development in March 2023, Databricks introduced Dolly, a model that follows instructions and is licensed under Apache.

An implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library. This repository records EleutherAI's library for training large-scale language models on GPUs. Our current framework is based on NVIDIA's Megatron Language Model and has been augmented with techniques from DeepSpeed as well as some novel optimizations. We aim to make this repo a centralized and accessible place to gather techniques for training large-scale autoregressive language models, and accelerate research into large-scale training.

Information overload is a major obstacle to scientific progress. The explosive growth in scientific literature and data has made it ever harder to discover useful insights in a large mass of information. Today scientific knowledge is accessed through search engines, but they are unable to organize scientific knowledge alone. Galactica is a large language model that can store, combine and reason about scientific knowledge. We train on a large scientific corpus of papers, reference material, knowledge bases and many other sources. We outperform existing models on a range of scientific tasks. On technical knowledge probes such as LaTeX equations, Galactica outperforms the latest GPT-3 by 68.2% versus 49.0%. Galactica also performs well on reasoning, outperforming Chinchilla on mathematical MMLU by 41.3% to 35.7%, and PaLM 540B on MATH with a score of 20.4% versus 8.8%.

Gemini was created from the ground up to be multimodal, highly efficient at tool and API integrations and built to enable future innovations, like memory and planning. While still early, we’re already seeing impressive multimodal capabilities not seen in prior models. Gemini is also our most flexible model yet — able to efficiently run on everything from data centers to mobile devices. Its state-of-the-art capabilities will significantly enhance the way developers and enterprise customers build and scale with AI. We’ve optimized Gemini 1.0, our first version, for three different sizes: Gemini Ultra — our largest and most capable model for highly complex tasks. Gemini Pro — our best model for scaling across a wide range of tasks. Gemini Nano — our most efficient model for on-device tasks.