Alternatives to Pythia

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.

Available in more than 20 programming languages, including Python, JavaScript and TypeScript, Ruby, TypeScript, Go, Ruby and many others. BLACKBOX AI code search was created so that developers could find the best code fragments to use when building amazing products. Integrations with IDEs include VS Code and Github Codespaces. Jupyter Notebook, Paperspace, and many more. C#, Java, C++, C# and SQL, PHP, Go and TypeScript are just a few of the languages that can be used to search code in Python, Java and C++. It is not necessary to leave your coding environment in order to search for a specific function. Blackbox allows you to select the code from any video and then simply copy it into your text editor. Blackbox supports all programming languages and preserves the correct indentation. The Pro plan allows you to copy text from over 200 languages and all programming languages.

Falcon-40B is a 40B parameter causal decoder model, built by TII. It was trained on 1,000B tokens from RefinedWeb enhanced by curated corpora. It is available under the Apache 2.0 licence. Why use Falcon-40B Falcon-40B is the best open source model available. Falcon-40B outperforms LLaMA, StableLM, RedPajama, MPT, etc. OpenLLM Leaderboard. It has an architecture optimized for inference with FlashAttention, multiquery and multiquery. It is available under an Apache 2.0 license that allows commercial use without any restrictions or royalties. This is a raw model that should be finetuned to fit most uses. If you're looking for a model that can take generic instructions in chat format, we suggest Falcon-40B Instruct.

Chinchilla has a large language. Chinchilla has the same compute budget of Gopher, but 70B more parameters and 4x as much data. Chinchilla consistently and significantly outperforms Gopher 280B, GPT-3 175B, Jurassic-1 178B, and Megatron-Turing (530B) in a wide range of downstream evaluation tasks. Chinchilla also uses less compute to perform fine-tuning, inference and other tasks. This makes it easier for downstream users to use. Chinchilla reaches a high-level average accuracy of 67.5% for the MMLU benchmark. This is a greater than 7% improvement compared to Gopher.

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.

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!

With T5, we propose re-framing all NLP into a unified format where the input and the output are always text strings. This is in contrast to BERT models which can only output a class label, or a span from the input. Our text-totext framework allows us use the same model and loss function on any NLP task. This includes machine translation, document summary, question answering and classification tasks. We can also apply T5 to regression by training it to predict a string representation of a numeric value instead of the actual number.

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.

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.

A model parallel autoregressive transformator implementation on GPUs based on the DeepSpeed Library. This repository contains EleutherAI’s library for training large language models on GPUs. Our current framework is based upon NVIDIA's Megatron Language Model, and has been enhanced with techniques from DeepSpeed, as well as some novel improvements. This repo is intended to be a central and accessible place for techniques to train large-scale autoregressive models and to accelerate research into large scale training.

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.

Phi-2 is a 2.7-billion-parameter language-model that shows outstanding reasoning and language-understanding capabilities. It represents the state-of-the art performance among language-base models with less than thirteen billion parameters. Phi-2 can match or even outperform models 25x larger on complex benchmarks, thanks to innovations in model scaling. Phi-2's compact size makes it an ideal playground for researchers. It can be used for exploring mechanistic interpretationability, safety improvements or fine-tuning experiments on a variety tasks. We have included Phi-2 in the Azure AI Studio catalog to encourage research and development of language models.

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.

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.

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.

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.

Information overload is a major barrier to scientific progress. The explosion of scientific literature and data makes it harder to find useful insights among a vast amount of information. Search engines are used to access scientific knowledge today, but they cannot organize it. Galactica is an extensive language model which can store, combine, and reason about scientific information. We train using a large corpus of scientific papers, reference material and knowledge bases, among other sources. We outperform other models in a variety of scientific tasks. Galactica performs better than the latest GPT-3 on technical knowledge probes like LaTeX Equations by 68.2% to 49.0%. Galactica is also good at reasoning. It outperforms Chinchilla in mathematical MMLU with a score between 41.3% and 35.7%. And PaLM 540B in MATH, with a score between 20.4% and 8.8%.

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.

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.

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.

ChatGPT Enterprise is the most powerful version yet, with enterprise-grade security and privacy. 1. Training models do not use customer prompts or data 2. Data encryption in transit and at rest (TLS 1.2+). 3. SOC 2 compliant 4. Easy bulk member management and dedicated admin console 5. SSO and Domain Verification 6. Use the analytics dashboard to understand usage 7. Access to GPT-4 Advanced Data Analysis and GPT-4 at high speed is unlimited 8. 32k token context window for 4X longer inputs, memory and inputs 9. Shareable chat templates to help your company collaborate

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.

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.

Baichuan-13B, a large-scale language model with 13 billion parameters that is open source and available commercially by Baichuan Intelligent, was developed following Baichuan -7B. It has the best results for a language model of the same size in authoritative Chinese and English benchmarks. This release includes two versions of pretraining (Baichuan-13B Base) and alignment (Baichuan-13B Chat). Baichuan-13B has more data and a larger size. It expands the number parameters to 13 billion based on Baichuan -7B, and trains 1.4 trillion coins on high-quality corpus. This is 40% more than LLaMA-13B. It is open source and currently the model with the most training data in 13B size. Support Chinese and English bi-lingual, use ALiBi code, context window is 4096.

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.

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.

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.

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.

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.

Alpa aims automate large-scale distributed training. Alpa was originally developed by people at UC Berkeley's Sky Lab. Alpa's advanced techniques were described in a paper published by OSDI'2022. Google is adding new members to the Alpa community. A language model is a probabilistic distribution of probability over a sequence of words. It uses all the words it has seen to predict the next word. It is useful in a variety AI applications, including the auto-completion of your email or chatbot service. You can find more information on the language model Wikipedia page. GPT-3 is a large language model with 175 billion parameters that uses deep learning to produce text that looks human-like. GPT-3 was described by many researchers and news articles as "one the most important and interesting AI systems ever created." GPT-3 is being used as a backbone for the latest NLP research.

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.

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.

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.

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.

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.

Sparrow is a research model that serves as a proof of concept. It was created with the goal to train dialogue agents to be more helpful and correct. Sparrow helps us understand how to train agents to be more helpful and safer, and ultimately to help create safer and more useful artificial intelligence (AGI). Sparrow is currently not available for public use. Because it is difficult to determine what makes a conversation successful, training conversational AI can be a challenging problem. We use reinforcement learning (RL) to address this problem. This is a form that uses people's feedback and the preference feedback of study participants to train a model about how useful an answer is. We show participants multiple models of the same question, and ask them which one they prefer.

We've developed a model, called ChatGPT, that interacts in a conversational manner. ChatGPT can use the dialogue format to answer questions, admit mistakes, challenge incorrect premises and reject inappropriate requests. ChatGPT is the sibling model of InstructGPT. InstructGPT is trained to follow a prompt, and then provide a detailed answer. ChatGPT Plus, a subscription plan to ChatGPT, a conversational AI. ChatGPT Plus is $20/month and subscribers receive a variety of benefits. - ChatGPT is available to all users, even at peak times - Faster response time Access to GPT-4 ChatGPT plugins Chat with Web-browsingGPT - Priority access for new features and improvements ChatGPT Plus will be available to all customers in the United States. We will begin inviting people on our waitlist within the next few weeks. We plan to extend access and support to other countries and regions in the near future.

Magic's LTM-1 provides context windows 50x larger than transformers. Magic has trained a Large Language Model that can take in huge amounts of context to generate suggestions. Magic, our coding assistant can now see all of your code. AI models can refer to more factual and explicit information with larger context windows. They can also reference their own actions history. This research will hopefully improve reliability and coherence.

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.

We are proud to announce we have completed the training on Inflection-2. It is the best model for its compute class in the entire world and the second most powerful LLM. Inflection's mission is to create an AI that is personal for everyone. Inflection-2 is a new model that is significantly more capable than Inflection-1. It has better factual knowledge, better style control, and dramatically enhanced reasoning. Inflection-2 has been trained on 5,000 NVIDIA GPUs at fp8 mixed accuracy for 1025 FLOPs. This puts Inflection-2 in the same training compute category as Google's flagship PaLM 2 Large Model. Inflection-2 also outperforms the majority of standard AI performance benchmarks including the well-known MMLU, TriviaQA, HellaSwag & GSM8k. Inflection-2, designed with efficiency in mind, will soon power Pi. We were able to reduce costs by switching from A100 to the H100 GPUs and optimizing our inference implementation.

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

ESMFold demonstrates how AI can provide new tools for understanding the natural world. It is similar to the microscope which allowed us to see the world at a tiny scale and gave us a new understanding of the world. AI can help us see biology in a different way and understand the vastness of nature. AI research has largely focused on helping computers understand the world in a similar way to humans. The language of proteins is a language that is beyond human comprehension. Even the most powerful computational tools have failed to understand it. AI has the potential of opening up this language to our comprehension. AI can be studied in new domains like biology to gain a better understanding of artificial intelligence. Our research reveals connections across domains. Large language models that are behind machine translation, natural speech understanding, speech recognition, image generation, and machine translation are also able learn deep information about biology.

We are proud to introduce Codestral, the first code model we have ever created. Codestral is a generative AI model that is open-weight and specifically designed for code generation. It allows developers to interact and write code using a shared API endpoint for instructions and completion. It can be used for advanced AI applications by software developers as it is able to master both code and English. Codestral has been trained on a large dataset of 80+ languages, including some of the most popular, such as Python and Java. It also includes C, C++ JavaScript, Bash, C, C++. It also performs well with more specific ones, such as Swift and Fortran. Codestral's broad language base allows it to assist developers in a variety of coding environments and projects.

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.

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.

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.

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.

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.

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.

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.