Alternatives to XLNet

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.

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.

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.

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.

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.

LLaVA is a multimodal model that combines a Vicuna language model with a vision encoder to facilitate comprehensive visual-language understanding. LLaVA's chat capabilities are impressive, emulating multimodal functionality of models such as GPT-4. LLaVA 1.5 has achieved the best performance in 11 benchmarks using publicly available data. It completed training on a single 8A100 node in about one day, beating methods that rely upon billion-scale datasets. The development of LLaVA involved the creation of a multimodal instruction-following dataset, generated using language-only GPT-4. This dataset comprises 158,000 unique language-image instruction-following samples, including conversations, detailed descriptions, and complex reasoning tasks. This data has been crucial in training LLaVA for a wide range of visual and linguistic tasks.

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.

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.

This repository contains a research preview of LongLLaMA. It is a large language-model capable of handling contexts up to 256k tokens. LongLLaMA was built on the foundation of OpenLLaMA, and fine-tuned with the Focused Transformer method. LongLLaMA code was built on the foundation of Code Llama. We release a smaller base variant of the LongLLaMA (not instruction-tuned) on a permissive licence (Apache 2.0), and inference code that supports longer contexts for hugging face. Our model weights are a drop-in replacement for LLaMA (for short contexts up to 2048 tokens) in existing implementations. We also provide evaluation results, and comparisons with the original OpenLLaMA model.

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

The 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.

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

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.

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.

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.

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.

Palmyra is an enterprise-ready suite of Large Language Models. These models are excellent at tasks like image analysis, question answering, and supporting over 30 languages. They can be fine-tuned for industries such as healthcare and finance. Palmyra models are notable for their top rankings in benchmarks such as Stanford HELM and PubMedQA. Palmyra Fin is the first model that passed the CFA Level III examination. Writer protects client data by not using it to train or modify models. They have a zero-data retention policy. Palmyra includes specialized models, such as Palmyra X 004, which has tool-calling abilities; Palmyra Med for healthcare; Palmyra Fin for finance; and Palmyra Vision for advanced image and video processing. These models are available via Writer's full stack generative AI platform which integrates graph based Retrieval augmented Generation (RAG).

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.

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.

Gemini models are a family of light-open, state-of-the art models that was created using the same research and technology as Gemini models. These models include comprehensive security measures, and help to ensure responsible and reliable AI through selected data sets. Gemma models have exceptional comparative results, even surpassing some larger open models, in their 2B and 7B sizes. Keras 3.0 offers seamless compatibility with JAX TensorFlow PyTorch and JAX. Gemma 2 has been redesigned to deliver unmatched performance and efficiency. It is optimized for inference on a variety of hardware. The Gemma models are available in a variety of models that can be customized to meet your specific needs. The Gemma models consist of large text-to text lightweight language models that have a decoder and are trained on a large set of text, code, or mathematical content.

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.

DataGemma is a pioneering project by Google that aims to improve the accuracy and reliability large language models (LLMs), when dealing with numerical and statistical data. DataGemma, launched as a collection of open models, leverages Google's Data Commons - a vast repository for public statistical data - to ground its responses in actual facts. This initiative uses two innovative approaches, Retrieval Interleaved Generation and Retrieval Augmented Generation. RIG integrates real-time checks of data during the generation process, ensuring factual accuracy. RAG retrieves pertinent information before generating answers, reducing the likelihood that AI hallucinations will occur. DataGemma's goal is to provide users with factual and trustworthy answers. This marks a significant step in reducing the amount of misinformation that AI-generated content contains.

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%.

Codestral Mamba is a Mamba2 model that specializes in code generation. It is available under the Apache 2.0 license. Codestral Mamba represents another step in our efforts to study and provide architectures. We hope that it will open up new perspectives in architecture research. Mamba models have the advantage of linear inference of time and the theoretical ability of modeling sequences of unlimited length. Users can interact with the model in a more extensive way with rapid responses, regardless of the input length. This efficiency is particularly relevant for code productivity use-cases. We trained this model with advanced reasoning and code capabilities, enabling the model to perform at par with SOTA Transformer-based models.

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-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.

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.

Amazon Nova is the new generation of foundation models (FMs), which are state-of-the art (SOTA), and offer industry-leading price-performance. They are available exclusively through Amazon Bedrock. Amazon Nova Micro and Amazon Nova Lite are understanding models which accept text, images, or videos as inputs and produce text output. They offer a wide range of capabilities, accuracy, speed and cost operation points. Amazon Nova Micro, a text-only model, delivers the lowest latency at a very low price. Amazon Nova Lite, a multimodal model with a low cost, is lightning-fast at processing text, image, and video inputs. Amazon Nova Pro is an extremely capable multimodal model that offers the best combination of accuracy and speed for a variety of tasks. Amazon Nova Pro is a powerful model that can handle almost any task. Its speed and cost efficiency are industry-leading.

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.

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.

Production-ready AI models that are fast and accurate. High-availability inference API that leverages the most advanced NVIDIA GPUs. We have selected the most popular open-source natural language processing models (NLP) and deployed them for the community. You can fine-tune your models (including GPT-J) or upload your custom models. Then, deploy them to production. Upload your AI models, including GPT-J, to your dashboard and immediately use them in production.

Opus, our intelligent model, is superior to its peers in most of the common benchmarks for AI systems. These include undergraduate level expert knowledge, graduate level expert reasoning, basic mathematics, and more. It displays near-human levels in terms of comprehension and fluency when tackling complex tasks. This is at the forefront of general intelligence. All Claude 3 models have increased capabilities for analysis and forecasting. They also offer nuanced content generation, code generation and the ability to converse in non-English language such as Spanish, Japanese and French.

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.

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.

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.

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.

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.

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

Mistral NeMo, our new best small model. A state-of the-art 12B with 128k context and released under Apache 2.0 license. Mistral NeMo, a 12B-model built in collaboration with NVIDIA, is available. Mistral NeMo has a large context of up to 128k Tokens. Its reasoning, world-knowledge, and coding precision are among the best in its size category. Mistral NeMo, which relies on a standard architecture, is easy to use. It can be used as a replacement for any system that uses Mistral 7B. We have released Apache 2.0 licensed pre-trained checkpoints and instruction-tuned base checkpoints to encourage adoption by researchers and enterprises. Mistral NeMo has been trained with quantization awareness to enable FP8 inferences without performance loss. The model was designed for global applications that are multilingual. It is trained in function calling, and has a large contextual window. It is better than Mistral 7B at following instructions, reasoning and handling multi-turn conversation.

Command R+, Cohere's latest large language model, is optimized for conversational interactions and tasks with a long context. It is designed to be extremely performant and enable companies to move from proof-of-concept into production. We recommend Command R+ when working with workflows that rely on complex RAG functionality or multi-step tool usage (agents). Command R is better suited for retrieval augmented creation (RAG) tasks and single-step tool usage, or applications where cost is a key consideration.

Marco-o1 is an advanced AI model that is designed for high-performance problem solving and natural language processing. It is designed to deliver precise, contextually rich answers by combining deep language understanding with a streamlined architectural design for speed and efficiency. Marco-o1 is a versatile AI system that excels at a wide range of tasks, including conversational AI. It also excels at content creation, technical assistance, and decision-making. It adapts seamlessly to the needs of diverse users. Marco-o1 is a cutting edge solution for individuals and organisations seeking intelligent, adaptive and scalable AI tools. It focuses on intuitive interactions, reliability and ethical AI principles. MCTS allows for the exploration of multiple reasoning pathways using confidence scores derived by softmax-applied logging probabilities of the top k alternative tokens. This guides the model to optimal solution.

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

Qwen2.5, 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.

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.

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.

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.

Pixtral 12B, a multimodal AI model pioneered by Mistral AI and designed to process and understand both text and images data seamlessly, is a groundbreaking AI model. This model represents a significant advance in the integration of data types. It allows for more intuitive interaction and enhanced content creation abilities. Pixtral 12B, which is based on Mistral's NeMo 12B Text Model, incorporates an additional Vision Adapter that adds 400 million parameters. This allows it to handle visual inputs of up to 1024x1024 pixels. This model is capable of a wide range of applications from image analysis to answering visual content questions. Its versatility is demonstrated in real-world scenarios. Pixtral 12B is a powerful tool for developers, as it not only has a large context of 128k tokens, but also uses innovative techniques such as GeLU activation and RoPE 2D for its vision components.

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.

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.

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.