Alternatives to T5

BERT is a large language model that can be used to pre-train language representations. Pre-training refers the process by which BERT is trained on large text sources such as Wikipedia. The training results can then be applied to other Natural Language Processing tasks (NLP), such as sentiment analysis and question answering. You can train many NLP models with AI Platform Training and BERT in just 30 minutes.

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.

With just a few lines, you can integrate natural language understanding and generation into the product. The Cohere API allows you to access models that can read billions upon billions of pages and learn the meaning, sentiment, intent, and intent of every word we use. You can use the Cohere API for human-like text. Simply fill in a prompt or complete blanks. You can create code, write copy, summarize text, and much more. Calculate the likelihood of text, and retrieve representations from your model. You can filter text using the likelihood API based on selected criteria or categories. You can create your own downstream models for a variety of domain-specific natural languages tasks by using representations. The Cohere API is able to compute the similarity of pieces of text and make categorical predictions based on the likelihood of different text options. The model can see ideas through multiple lenses so it can identify abstract similarities between concepts as distinct from DNA and computers.

RoBERTa is based on BERT's language-masking strategy. The system learns to predict hidden sections of text in unannotated language examples. RoBERTa was implemented in PyTorch and modifies key hyperparameters of BERT. This includes removing BERT’s next-sentence-pretraining objective and training with larger mini-batches. This allows RoBERTa improve on the masked-language modeling objective, which is comparable to BERT. It also leads to improved downstream task performance. We are also exploring the possibility of training RoBERTa with a lot more data than BERT and for a longer time. We used both existing unannotated NLP data sets as well as CC-News which was a new set of public news articles.

Amazon Bedrock is an innovative service that allows FMs to be accessed by leading AI startups as well as Amazon via API. Bedrock makes it easy for customers to create and scale AI-based generative applications, using FMs. It democratizes access for all builders. Bedrock allows users to access a variety of powerful FMs that can be used for text or images, including Amazon Titan FMs. This is done through a scalable and reliable AWS managed service. Amazon Titan FMs have been trained on large datasets and are powerful general-purpose models. You can use them as-is or customize them privately with your own data to accomplish a specific task without having to annotate large volumes of data. Titan Text is a large language model that can be used for tasks like summarization, text creation (for example creating a blog), classification, open ended Q&A and information extraction. Automate natural language tasks, such as text generation and summarization.

ALBERT is a Transformer model that can be self-supervised and was trained on large amounts of English data. It does not need manual labelling and instead uses an automated process that generates inputs and labels from the raw text. It is trained with two distinct goals in mind. Masked Language Modeling is the first. This randomly masks 15% words in an input sentence and requires that the model predict them. This technique is different from autoregressive models such as GPT and RNNs in that it allows the model learn bidirectional sentence representations. Sentence Ordering Prediction is the second objective. This involves predicting the order of two consecutive text segments during pretraining.

GPT-4, a large multimodal (accepting text and image inputs) model that can solve complex problems with greater accuracy thanks to its advanced reasoning abilities and broader general knowledge than any of our other models. GPT-4 can be found in the OpenAI API for paying customers. GPT-4, like gpt 3.5-turbo is optimized for chat, but also works well with traditional completion tasks using the Chat Completions API. Our GPT guide will teach you how to use GPT-4. GPT-4 is a newer GPT-4 model that features improved instruction following, JSON Mode, reproducible outputs and parallel function calls. Returns up to 4,096 tokens. This preview model has not yet been adapted for production traffic.

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.

Pre-trained models of language have achieved state-of the-art results for various Natural Language Processing (NLP). GPT-3 has demonstrated that scaling up language models pre-trained can further exploit their immense potential. Recently, a framework named ERNIE 3.0 for pre-training large knowledge enhanced models was proposed. This framework trained a model that had 10 billion parameters. ERNIE 3.0 performed better than the current state-of-the art models on a variety of NLP tasks. In order to explore the performance of scaling up ERNIE 3.0, we train a hundred-billion-parameter model called ERNIE 3.0 Titan with up to 260 billion parameters on the PaddlePaddle platform. We also design a self supervised adversarial and a controllable model language loss to make ERNIE Titan generate credible texts.

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.

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.

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.

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.

XLNet, a new unsupervised language representation method, is based on a novel generalized Permutation Language Modeling Objective. XLNet uses Transformer-XL as its backbone model. This model is excellent for language tasks that require long context. Overall, XLNet achieves state of the art (SOTA) results in various downstream language tasks, including question answering, natural languages inference, sentiment analysis and document ranking.

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.

Samsung Gauss, a new AI-model developed by Samsung Electronics, is a powerful AI tool. It is a large-language model (LLM) which has been trained using a massive dataset. Samsung Gauss can generate text, translate different languages, create creative content and answer questions in a helpful way. Samsung Gauss, which is still in development, has already mastered many tasks, including Follow instructions and complete requests with care. Answering questions in an informative and comprehensive way, even when they are open-ended, challenging or strange. Creating different creative text formats such as poems, code, musical pieces, emails, letters, etc. Here are some examples to show what Samsung Gauss is capable of: Translation: Samsung Gauss is able to translate text between many languages, including English and German, as well as Spanish, Chinese, Japanese and Korean. Coding: Samsung Gauss can generate code.

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-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.

PanGu-a was developed under MindSpore, and trained on 2048 Ascend AI processors. The MindSpore Auto-parallel parallelism strategy was implemented to scale the training task efficiently to 2048 processors. This includes data parallelism as well as op-level parallelism. We pretrain PanGu-a with 1.1TB of high-quality Chinese data collected from a variety of domains in order to enhance its generalization ability. We test the generation abilities of PanGua in different scenarios, including text summarizations, question answering, dialog generation, etc. We also investigate the effects of model scaling on the few shot performances across a wide range of Chinese NLP task. The experimental results show that PanGu-a is superior in performing different tasks with zero-shot or few-shot settings.

Martian outperforms GPT-4 across OpenAI's evals (open/evals). Martian outperforms GPT-4 in all OpenAI's evaluations (open/evals). We transform opaque black boxes into interpretable visual representations. Our router is our first tool built using our model mapping method. Model mapping is being used in many other applications, including transforming transformers from unintelligible matrices to human-readable programs. Automatically reroute your customers to other providers if a company has an outage or a high latency period. Calculate how much money you could save using the Martian Model Router by using our interactive cost calculator. Enter the number of users and tokens per session. Also, specify how you want to trade off between cost and quality.

Multilingual T5 is a massively pretrained text-totext transformer model that has been trained using a similar recipe to T5. This repo can used to reproduce the experiments described in the mT5 article. The mC4 corpus covers 101 languages. Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish, Kyrgyz, Lao, Latin, Latvian, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Samoan, Scottish Gaelic, Serbian, Shona, Sindhi, and more.

PaLM 2 is Google's next-generation large language model, which builds on Google’s research and development in machine learning. It excels in advanced reasoning tasks including code and mathematics, classification and question-answering, translation and multilingual competency, and natural-language generation better than previous state-of the-art LLMs including PaLM. It is able to accomplish these tasks due to the way it has been built - combining compute-optimal scale, an improved dataset mix, and model architecture improvement. PaLM 2 is based on Google's approach for building and deploying AI responsibly. It was rigorously evaluated for its potential biases and harms, as well as its capabilities and downstream applications in research and product applications. It is being used to power generative AI tools and features at Google like Bard, the PaLM API, and other state-ofthe-art models like Sec-PaLM and Med-PaLM 2.

Gemini is multimodal by default, giving you the ability to transform any input into any output. We built Gemini responsibly, incorporating safeguards from the beginning and working with partners to make it more inclusive and safer. Integrate Gemini models in your applications using Google AI Studio and Google Cloud Vertex AI.

You can use advanced language models and coding to solve a variety of problems. To build cutting-edge applications, leverage large-scale, generative AI models that have deep understandings of code and language to allow for new reasoning and comprehension. These coding and language models can be applied to a variety use cases, including writing assistance, code generation, reasoning over data, and code generation. Access enterprise-grade Azure security and detect and mitigate harmful use. Access generative models that have been pretrained with trillions upon trillions of words. You can use them to create new scenarios, including code, reasoning, inferencing and comprehension. A simple REST API allows you to customize generative models with labeled information for your particular scenario. To improve the accuracy of your outputs, fine-tune the hyperparameters of your model. You can use the API's few-shot learning capability for more relevant results and to provide examples.

VideoPoet, a simple modeling technique, can convert any large language model or autoregressive model into a high quality video generator. It is composed of a few components. The autoregressive model learns from video, image, text, and audio modalities in order to predict the next audio or video token in the sequence. The LLM training framework introduces a mixture of multimodal generative objectives, including text to video, text to image, image-to video, video frame continuation and inpainting/outpainting, styled video, and video-to audio. Moreover, these tasks can be combined to provide additional zero-shot capabilities. This simple recipe shows how language models can edit and synthesize videos with a high level of temporal consistency.

Use generative language models for improving and optimizing your web services and applications. Get a consolidated result of textual data, whether it is information from chats at work, user reviews or other types. YandexGPT can help summarize and interpret information. Improve the quality and style of your text to speed up the creation process. Create templates for newsletters, product description for online stores, and other applications. Create a chatbot to help your customer service. Teach the bot how to answer common and complex questions. Use the API to automate processes and integrate the service into your applications.

GPT-4 with Vision (GPT-4V), our latest capability, allows users to instruct GPT-4 on how to analyze images input by the user. Some researchers and developers of artificial intelligence consider the incorporation of additional modalities, such as image inputs, into large language models. Multimodal LLMs can be used to expand the impact of existing language-only systems by providing them with novel interfaces, capabilities and experiences. In this system card we analyze the GPT-4V safety properties. We have built on the safety work for GPT-4V and here we go deeper into the evaluations and preparations for image inputs.

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.

GPT-J, a cutting edge language model developed by EleutherAI, is a leading-edge language model. GPT-J's performance is comparable to OpenAI's GPT-3 model on a variety of zero-shot tasks. GPT-J, in particular, has shown that it can surpass GPT-3 at tasks relating to code generation. The latest version of this language model is GPT-J-6B and is built on a linguistic data set called The Pile. This dataset is publically available and contains 825 gibibytes worth of language data organized into 22 subsets. GPT-J has some similarities with ChatGPT. However, GPTJ is not intended to be a chatbot. Its primary function is to predict texts. Databricks made a major development in March 2023 when they introduced Dolly, an Apache-licensed model that follows instructions.

PygmalionAI, a community of open-source projects based upon EleutherAI’s GPT-J 6B models and Meta’s LLaMA model, was founded in 2009. Pygmalion AI is designed for roleplaying and chatting. The 7B variant of the Pygmalion AI is currently actively supported. It is based on Meta AI’s LLaMA AI model. Pygmalion's chat capabilities are superior to larger language models that require much more resources. Our curated datasets of high-quality data on roleplaying ensure that your bot is the best RP partner. The model weights as well as the code used to train the model are both open-source. You can modify/re-distribute them for any purpose you like. Pygmalion and other language models run on GPUs because they require fast memory and massive processing to produce coherent text at a reasonable speed.

Our enterprise-grade multimodal Assistant is designed with privacy, efficiency, and security in mind. Yasa is trained to read text, images and videos. Tabular data will be added in the future. Use it to generate creative tasks, find answers to basic questions or gain insights from your data. With a few simple commands, you can generate, train, compress or deploy your model on-premise. Our proprietary algorithms can be used to customize our model for your data and use case. We use proprietary algorithms for retrieval, fine tuning, self-supervised instructions tuning, and reinforcement to tune our model using your datasets.

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.

Gemini Ultra is an advanced new language model by Google DeepMind. It is the most powerful and largest model in the Gemini Family, which includes Gemini Pro & Gemini Nano. Gemini Ultra was designed to handle highly complex tasks such as machine translation, code generation, and natural language processing. It is the first language model that has outperformed human experts in the Massive Multitask Language Understanding test (MMLU), achieving a score 90%.

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.

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.

Solar's Chat API allows you to create a simple agent that can have a conversation. Function Calling, the method of connecting LLM with external tools, is now supported. The embedding vectors are useful for retrieval and classification. Context-aware English to Korean translation that uses previous dialogues for unmatched coherence in your conversations. Verifies that the LLM's generated answers are appropriate based on the question asked by the user and the search results. A healthcare LLM is being developed to automate patient communications, personalize treatment plans and aid in clinical decision-support. It will also support medical transcription. The goal is to make it easy for business owners and companies, to deploy generative AI bots on mobile apps and websites. This will provide human-like customer support.

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.

Introducing MPT-7B - the latest addition to our MosaicML Foundation Series. MPT-7B, a transformer that is trained from scratch using 1T tokens of code and text, is the latest entry in our MosaicML Foundation Series. It is open-source, available for commercial purposes, and has the same quality as LLaMA-7B. MPT-7B trained on the MosaicML Platform in 9.5 days, with zero human interaction at a cost $200k. You can now train, fine-tune and deploy your private MPT models. You can either start from one of our checkpoints, or you can start from scratch. For inspiration, we are also releasing three finetuned models in addition to the base MPT-7B: MPT-7B-Instruct, MPT-7B-Chat, and MPT-7B-StoryWriter-65k+, the last of which uses a context length of 65k tokens!

NVIDIA NeMo megatron is an end to-end framework that can be used to train and deploy LLMs with billions or trillions of parameters. NVIDIA NeMo Megatron is part of the NVIDIAAI platform and offers an efficient, cost-effective, and cost-effective containerized approach to building and deploying LLMs. It is designed for enterprise application development and builds upon the most advanced technologies of NVIDIA research. It provides an end-to–end workflow for automated distributed processing, training large-scale customized GPT-3 and T5 models, and deploying models to infer at scale. The validation of converged recipes that allow for training and inference is a key to unlocking the power and potential of LLMs. The hyperparameter tool makes it easy to customize models. It automatically searches for optimal hyperparameter configurations, performance, and training/inference for any given distributed GPU cluster configuration.

Gemini Nano is a tiny version of the Gemini family. It is the latest generation of Google DeepMind multimodal language models. Nano is a super-powered AI that fits snugly into your smartphone. Nano is the smallest (along with its siblings Ultra and Pro), but it packs a powerful punch. It is specifically designed to run on mobile devices, such as your phone, and brings powerful AI capabilities to your fingertips even when you are offline. Imagine it as your ultimate assistant on your device, whispering intelligent suggestions and automating tasks effortlessly. Want to summarize that long recorded lecture quickly? Nano has you covered. Want to create the perfect response to a tricky text message? Nano will give you options that will make your friends think you're an expert wordsmith.

Gemma is the family of lightweight open models that are built using the same research and technology as the Gemini models. Gemma was developed by Google DeepMind, along with other teams within Google. The name is derived from the Latin gemma meaning "precious stones". We're also releasing new tools to encourage developer innovation, encourage collaboration, and guide responsible use of Gemma model. Gemma models are based on the same infrastructure and technical components as Gemini, Google's largest and most powerful AI model. Gemma 2B, 7B and other open models can achieve the best performance possible for their size. Gemma models can run directly on a desktop or laptop computer for developers. Gemma is able to surpass much larger models in key benchmarks, while adhering our rigorous standards of safe and responsible outputs.

Instruction-following models such as GPT-3.5 (text-DaVinci-003), ChatGPT, Claude, and Bing Chat have become increasingly powerful. These models are now used by many users, and some even for work. However, despite their widespread deployment, instruction-following models still have many deficiencies: they can generate false information, propagate social stereotypes, and produce toxic language. It is vital that the academic community engages in order to make maximum progress towards addressing these pressing issues. Unfortunately, doing research on instruction-following models in academia has been difficult, as there is no easily accessible model that comes close in capabilities to closed-source models such as OpenAI's text-DaVinci-003. We are releasing our findings about an instruction-following language model, dubbed Alpaca, which is fine-tuned from Meta's LLaMA 7B model.

A MLLM system that accepts any form of referral and grounds anything in response. Ferret Model- Hybrid Region representation + Spatial-aware visual sampler allows for fine-grained and open vocabulary referring and grounding. GRIT Dataset - A large-scale, hierarchical, robust ground-and refer instruction tuning dataset. Ferret Bench - A multimodal benchmark that requires Referring/Grounding as well as Semantics, Knowledge and Reasoning.

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.

Integrate our Python SDK and Rest API within minutes to ship faster. No data formatting or setup required. As soon as your first customer and data are in, you can start using the system. We have developed and fine-tuned our custom language models in order to recognize entities, crawl the web in real time and select the best match. We can also assign labels with superhuman precision in a fraction the time. Everyone has a data-enrichment model that tries to excel at one thing - whether it's for the US or Europe, or business or consumers. These models are not able to generalize and cannot produce output at the level of a human. You can embed the largest and most efficient models in your products at a fractional cost and time.

Command's outputs are accompanied by clear citations, which reduce the risk of hallucinations. They also allow for the retrieval of additional context in the source material. Command can help you write product descriptions, draft emails, provide example press releases and more. Ask Command a series of questions about a particular document to assign it a category, extract information, or answer an overall question. Answering a few questions can save you minutes, but doing it for thousands can save an entire company years. This family of scalable AI models balances high accuracy with high efficiency to allow enterprises to move beyond proof-of-concept into production grade AI.

Dolly is an inexpensive LLM that demonstrates a surprising amount of the capabilities of ChatGPT. Whereas the work from the Alpaca team showed that state-of-the-art models could be coaxed into high quality instruction-following behavior, we find that even years-old open source models with much earlier architectures exhibit striking behaviors when fine tuned on a small corpus of instruction training data. Dolly uses an open source model with 6 billion parameters from EleutherAI, which is modified to include new capabilities like brainstorming and text creation that were not present in the original.

Gemini was designed from the ground-up to be multimodal. It is highly efficient in tool and API integrations, and it is built to support future innovations like memory and planning. We're seeing multimodal capabilities that were not present in previous models. Gemini is our most flexible model to date -- it can run on anything from data centers to smartphones. Its cutting-edge capabilities will improve the way developers and enterprises build and scale AI. We've optimized Gemini 1.0 for three different sizes. Gemini Ultra - Our largest and most capable model, designed for highly complex tasks. Gemini Pro is our best model to scale across a variety of tasks. Gemini Nano -- our most efficient model for on-device tasks.

Megatron-Turing Natural Language Generation Model (MT-NLG) is the largest and most powerful monolithic English language model. It has 530 billion parameters. This 105-layer transformer-based MTNLG improves on the previous state-of-the art models in zero, one, and few shot settings. It is unmatched in its accuracy across a wide range of natural language tasks, including Completion prediction and Reading comprehension. NVIDIA has announced an Early Access Program for its managed API service in MT-NLG Mode. This program will allow customers to experiment with, employ and apply a large language models on downstream language tasks.

StarCoderBase and StarCoder are Large Language Models (Code LLMs), trained on permissively-licensed data from GitHub. This includes data from 80+ programming language, Git commits and issues, Jupyter Notebooks, and Git commits. We trained a 15B-parameter model for 1 trillion tokens, similar to LLaMA. We refined the StarCoderBase for 35B Python tokens. The result is a new model we call StarCoder. StarCoderBase is a model that outperforms other open Code LLMs in popular programming benchmarks. It also matches or exceeds closed models like code-cushman001 from OpenAI, the original Codex model which powered early versions GitHub Copilot. StarCoder models are able to process more input with a context length over 8,000 tokens than any other open LLM. This allows for a variety of interesting applications. By prompting the StarCoder model with a series dialogues, we allowed them to act like a technical assistant.