Alternatives to Marco-o1

OpenAI o3 has been designed to improve reasoning by breaking complex instructions down into smaller, easier-to-understand steps. It is a significant improvement over previous AI versions, excelling at coding tasks, competitive programing, and achieving high marks in mathematics and science benchmarks. OpenAI o3 is a widely-used AI-driven decision-making and problem-solving tool that supports advanced AI. The model uses deliberative alignment to ensure that its responses are in line with established safety and ethics guidelines. This makes it a powerful tool, especially for developers, researchers and enterprises looking for sophisticated AI solutions.

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

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

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

OpenAI o3 Mini is a lightweight version o3 AI model that offers powerful reasoning capabilities, but in a more accessible and efficient package. O3-mini is designed to break complex instructions down into smaller, more manageable steps. It excels at coding tasks, competitive programing, and problem solving in mathematics and sciences. This compact model offers the same high level of precision and logic that its larger counterpart, but with reduced computation requirements. It is ideal for use in resource constrained environments. The o3 mini's deliberative alignment ensures ethical, safe and context-aware decisions. This makes it a versatile tool that can be used by developers, researchers and businesses looking for a balance between performance, efficiency and safety.

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

Gemini Flash, a large language model from Google, is specifically designed for low-latency, high-speed language processing tasks. Gemini Flash, part of Google DeepMind’s Gemini series is designed to handle large-scale applications and provide real-time answers. It's ideal for interactive AI experiences such as virtual assistants, live chat, and customer support. Gemini Flash is built on sophisticated neural structures that ensure contextual relevance, coherence, and precision. Google has built in rigorous ethical frameworks as well as responsible AI practices to Gemini Flash. It also equipped it with guardrails that manage and mitigate biased outcomes, ensuring alignment with Google's standards of safe and inclusive AI. Google's Gemini Flash empowers businesses and developers with intelligent, responsive language tools that can keep up with fast-paced environments.

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

We introduce the Granite family of decoder only code models for code generation tasks (e.g. fixing bugs, explaining codes, documenting codes), trained with code in 116 programming language. The Granite Code family has been evaluated on a variety of tasks and demonstrates that the models are consistently at the top of their game among open source code LLMs. Granite Code models have a number of key advantages. Granite Code models are able to perform at a competitive level or even at the cutting edge of technology in a variety of code-related tasks including code generation, explanations, fixing, translation, editing, and more. Demonstrating the ability to solve a variety of coding tasks. IBM's Corporate Legal team guides all models for trustworthy enterprise use. All models are trained using license-permissible datasets collected according to IBM's AI Ethics Principles.

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

Claude Pro is a large language model that can handle complex tasks with a friendly and accessible demeanor. It is trained on high-quality, extensive data and excels at understanding contexts, interpreting subtleties, and producing well structured, coherent responses to a variety of topics. Claude Pro is able to create detailed reports, write creative content, summarize long documents, and assist with coding tasks by leveraging its robust reasoning capabilities and refined knowledge base. Its adaptive algorithms constantly improve its ability learn from feedback. This ensures that its output is accurate, reliable and helpful. Whether Claude Pro is serving professionals looking for expert support or individuals seeking quick, informative answers - it delivers a versatile, productive conversational experience.

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

Gemini is Google’s advanced AI chatbot that engages in natural language conversation to boost creativity and productivity. Gemini is accessible via web and mobile apps. It integrates seamlessly with Google services such as Docs, Drive and Gmail. Users can draft content, summarize data, and manage tasks. Its multimodal capabilities enable it to process and produce diverse data types such as text images and audio. This provides comprehensive assistance in different contexts. Gemini is a constantly learning model that adapts to the user's interactions and offers personalized and context-aware answers to meet a variety of user needs.

LM-Kit.NET, a cutting edge high-level inference toolkit, is designed to bring the advanced capabilities Large Language Models into the C# ecosystem. LM-Kit.NET is a powerful Generative AI toolkit that's tailored for developers who work within.NET. It makes it easier than ever before to integrate AI functionality into your applications. The SDK offers a wide range of AI features to cater to different industries. Text completion, Natural Language Processing, content retrieval and summarization, text enrichment, language translation are just a few of the many features. Whether you want to automate content creation or build intelligent data retrieval system, LM Kit.NET provides the flexibility and performance to accelerate your project.

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

The Gemini 2.0 Flash AI represents the next-generation of high-speed intelligent computing. It is designed to set new standards in real-time decision-making and language processing. It builds on the solid foundation of its predecessor and incorporates enhanced neural technology and breakthrough advances in optimization to enable even faster and more accurate response times. Gemini 2.0 Flash was designed for applications that require instantaneous processing, adaptability, and live virtual assistants. Its lightweight and efficient design allows for seamless deployment across cloud and hybrid environments. Multitasking and improved contextual understanding make it an ideal tool to tackle complex and dynamic workflows.

Introducing DeepSeek LLM - an advanced language model with 67 billion parameters. It was trained from scratch using a massive dataset of 2 trillion tokens, both in English and Chinese. To encourage research, we made DeepSeek LLM 67B Base and DeepSeek LLM 67B Chat available as open source to the research community.

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

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.

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.

Small language models (SLMs), a powerful family of small language models, with low cost and low-latency performance. Maximize AI capabilities and lower resource usage, while ensuring cost-effective generative AI implementations across your applications. Accelerate response time in real-time interaction, autonomous systems, low latency apps, and other critical scenarios. Phi-3 can be run in the cloud, on the edge or on the device. This allows for greater flexibility in deployment and operation. Phi-3 models have been developed according to Microsoft AI principles, including accountability, transparency and fairness, reliability, safety and security, privacy, and inclusivity. Operate efficiently in offline environments, where data privacy or connectivity are limited. Expanded context window allows for more accurate, contextually relevant and coherent outputs. Deploy at edge to deliver faster response.

Scale AI is pleased to announce Defense Llama. This Large Language Model (LLM), built on Meta's Llama 3, is customized and fine-tuned for support of American national security missions. Defense Llama is available only in controlled U.S. Government environments within Scale Donovan. It empowers our servicemen and national security professionals by enabling them to apply the power generative AI for their unique use cases such as planning military operations or intelligence operations, and understanding adversary weaknesses. Defense Llama has been trained using a vast dataset that includes military doctrine, international human rights law, and relevant policy designed to align with Department of Defense (DoD), guidelines for armed conflicts, as well as DoD's Ethical Principles of Artificial Intelligence. This allows the model to respond with accurate, meaningful and relevant responses. Scale is proud that it can help U.S. national-security personnel use generative AI for defense in a safe and secure manner.

Teuken-7B, a multilingual open source language model, was developed under the OpenGPT-X project. It is specifically designed to accommodate Europe's diverse linguistic landscape. It was trained on a dataset that included over 50% non-English text, covering all 24 official European Union languages, to ensure robust performance. Teuken-7B's custom multilingual tokenizer is a key innovation. It has been optimized for European languages and enhances training efficiency. The model comes in two versions: Teuken-7B Base, a pre-trained foundational model, and Teuken-7B Instruct, a model that has been tuned to better follow user prompts. Hugging Face makes both versions available, promoting transparency and cooperation within the AI community. The development of Teuken-7B demonstrates a commitment to create AI models that reflect Europe’s diversity.

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.

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.

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.

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%.

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.

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.

Claude 3.5 Sonnet is a new benchmark for the industry in terms of graduate-level reasoning (GPQA), undergrad-level knowledge (MMLU), as well as coding proficiency (HumanEval). It is exceptional in writing high-quality, relatable content that is written with a natural and relatable tone. It also shows marked improvements in understanding nuance, humor and complex instructions. Claude 3.5 Sonnet is twice as fast as Claude 3 Opus. Claude 3.5 Sonnet is ideal for complex tasks, such as providing context-sensitive support to customers and orchestrating workflows. Claude 3.5 Sonnet can be downloaded for free from Claude.ai and Claude iOS, and subscribers to the Claude Pro and Team plans will have access to it at rates that are significantly higher. It is also accessible via the Anthropic AI, Amazon Bedrock and Google Cloud Vertex AI. The model costs $3 for every million input tokens. It costs $15 for every million output tokens. There is a 200K token window.

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

The Gemini 1.5 Flash AI is a high-speed, advanced language model that has been designed for real-time responsiveness and lightning-fast processing. It is designed to excel in dynamic, time-sensitive applications. It combines streamlined neural technology with cutting-edge optimization methods to deliver exceptional performance and accuracy. Gemini 1.5 Flash was designed for scenarios that require rapid data processing, instant decisions, and seamless multitasking. It is ideal for chatbots and customer support systems. Its lightweight but powerful design allows it to be deployed efficiently on a variety of platforms, including cloud-based environments and edge devices. This allows businesses to scale operations with unmatched flexibility.

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.

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

OpenAI o1 is a new series AI models developed by OpenAI that focuses on enhanced reasoning abilities. These models, such as o1 preview and o1 mini, are trained with a novel reinforcement-learning approach that allows them to spend more time "thinking through" problems before presenting answers. This allows o1 excel in complex problem solving tasks in areas such as coding, mathematics, or science, outperforming other models like GPT-4o. The o1 series is designed to tackle problems that require deeper thinking processes. This marks a significant step in AI systems that can think more like humans.

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.

OpenAI o1 pro is an enhanced version of OpenAI’s o1 model. It was designed to handle more complex and demanding tasks, with greater reliability. It has significant performance improvements compared to its predecessor, the OpenAI o1 Preview, with a noticeable 34% reduction in errors and the ability think 50% faster. This model excels at math, physics and coding where it can provide accurate and detailed solutions. The o1 Pro mode is also capable of processing multimodal inputs including text and images. It is especially adept at reasoning tasks requiring deep thought and problem solving. ChatGPT Pro subscriptions offer unlimited usage as well as enhanced capabilities to users who need advanced AI assistance.

CodeQwen, developed by the Qwen Team, Alibaba Cloud, is the code version. It is a transformer based decoder only language model that has been pre-trained with a large number of codes. A series of benchmarks shows that the code generation is strong and that it performs well. Supporting long context generation and understanding with a context length of 64K tokens. CodeQwen is a 92-language coding language that provides excellent performance for text-to SQL, bug fixes, and more. CodeQwen chat is as simple as writing a few lines of code using transformers. We build the tokenizer and model using pre-trained methods and use the generate method for chatting. The chat template is provided by the tokenizer. Following our previous practice, we apply the ChatML Template for chat models. The model will complete the code snippets in accordance with the prompts without any additional formatting.

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.

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.

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.

Doubao, an intelligent language model created by ByteDance, is a powerful tool for learning new languages. It has provided users with useful answers and insights on a wide range topics. Doubao is able to handle complex questions and provide detailed explanations. It can also engage in meaningful conversation. Its advanced language understanding and generation abilities continue to help people solve problems, explore new ideas, and seek knowledge. Doubao can be used for academic inquiries, inspiration for creative projects, or just a simple conversation.

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.

Sky-T1-32B is an open-source reasoning tool developed by the NovaSky group at UC Berkeley’s Sky Computing Lab. It is comparable to proprietary models such as o1 preview on reasoning and coding tests, but was trained for less than $450. This shows the feasibility of cost-effective high-level reasoning abilities. The model was fine-tuned using Qwen2.5 32B-Instruct and a curated dataset with 17,000 examples from diverse domains including math and coding. The training took 19 hours using eight H100 GPUs and DeepSpeed Zero-3 offloading. All aspects of the project are open-source including the data, code and model weights. This allows the academic and open source communities to duplicate and enhance the performance.

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.

Phi-4 is the latest small language model (SLM), with 14B parameters. It excels in complex reasoning, including math, as well as conventional language processing. Phi-4, the latest member of the Phi family of SLMs, demonstrates what is possible as we continue exploring the boundaries of SLMs. Phi-4 will be available in Hugging Face and Azure AI Foundry, under a Microsoft Research License Agreement. Phi-4 is superior to comparable and larger models in math-related reasoning thanks to improvements throughout the process, including the use high-quality synthetic data, curation of organic data of high quality, and innovations post-training. Phi-4 continues pushing the boundaries of size vs. quality.

A small model with superior textual Intelligence and multimodal reasoning. GPT-4o Mini's low cost and low latency enable a wide range of tasks, including applications that chain or paralelize multiple model calls (e.g. calling multiple APIs), send a large amount of context to the models (e.g. full code base or history of conversations), or interact with clients through real-time, fast text responses (e.g. customer support chatbots). GPT-4o Mini supports text and vision today in the API. In the future, it will support text, image and video inputs and outputs. The model supports up to 16K outputs tokens per request and has knowledge until October 2023. It has a context of 128K tokens. The improved tokenizer shared by GPT-4o makes it easier to handle non-English text.

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.