Alternatives to Aya

OpenGPT is a German initiative that focuses on developing large AI languages models tailored to European requirements, with an emphasis on versatility, trustworthiness and multilingual capabilities. It also emphasizes open-source accessibility. The project brings together partners to cover the whole generative AI value-chain, from scalable GPU-based infrastructure to data for training large language model to model design, practical applications, and prototypes and proofs-of concept. OpenGPT-X aims at advancing cutting-edge research, with a focus on business applications. This will accelerate the adoption of generative AI within the German economy. The project also stresses responsible AI development to ensure that the models are reliable and aligned with European values and laws. The project provides resources, such as the LLM Workbook and a three part reference guide with examples and resources to help users better understand the key features and characteristics of large AI language model.

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

There are now more versions of the open-source AI model that you can refine, distill and deploy anywhere. Choose from 1B or 3B, or build with Llama 3. Llama 3.2 consists of a collection large language models (LLMs), which are pre-trained and fine-tuned. They come in sizes 1B and 3B, which are multilingual text only. Sizes 11B and 90B accept both text and images as inputs and produce text. Our latest release allows you to create highly efficient and performant applications. Use our 1B and 3B models to develop on-device applications, such as a summary of a conversation from your phone, or calling on-device features like calendar. Use our 11B and 90B models to transform an existing image or get more information from a picture of your surroundings.

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.

We are developing large language models that are efficient for India's diverse cultural diversity and enabling GenAI applications with bespoke enterprise models. We are building a platform for enterprise-grade apps that allows you to develop and evaluate them. We believe that open-source can accelerate AI innovation. We will be contributing open-source datasets and models, and leading efforts for large data curation projects in the public-good space. We are a dynamic team of AI experts, combining expertise in research, product design, engineering and business operations. Our diverse backgrounds are united by a commitment to excellence in science, and creating societal impact. We create an environment in which tackling complex tech problems is not only a job but a passion.

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

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.

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.

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.

Hermes 3 contains advanced long-term context retention and multi-turn conversation capabilities, complex roleplaying and internal monologue abilities, and enhanced agentic function-calling. Hermes 3 has advanced long-term contextual retention, multi-turn conversation capabilities, complex roleplaying, internal monologue, and enhanced agentic functions-calling. Our training data encourages the model in a very aggressive way to follow the system prompts and instructions exactly and in a highly adaptive manner. Hermes 3 was developed by fine-tuning Llama 3.0 8B, 70B and 405B and training with a dataset primarily containing synthetic responses. The model has a performance that is comparable to Llama 3.1, but with deeper reasoning and creative abilities. Hermes 3 is an instruct and tool-use model series with strong reasoning and creativity abilities.

StableLM: Stability AI language models StableLM builds upon our experience with open-sourcing previous language models in collaboration with EleutherAI. This nonprofit research hub. These models include GPTJ, GPTNeoX and the Pythia Suite, which were all trained on The Pile dataset. Cerebras GPT and Dolly-2 are two recent open-source models that continue to build upon these efforts. StableLM was trained on a new dataset that is three times bigger than The Pile and contains 1.5 trillion tokens. We will provide more details about the dataset at a later date. StableLM's richness allows it to perform well in conversational and coding challenges, despite the small size of its dataset (3-7 billion parameters, compared to GPT-3's 175 billion). The development of Stable LM 3B broadens the range of applications that are viable on the edge or on home PCs. This means that individuals and companies can now develop cutting-edge technologies with strong conversational capabilities – like creative writing assistance – while keeping costs low and performance high.

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.

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.

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.

GPT4All provides an ecosystem for training and deploying large language models, which run locally on consumer CPUs. The goal is to be the best assistant-style language models that anyone or any enterprise can freely use and distribute. A GPT4All is a 3GB to 8GB file you can download and plug in the GPT4All ecosystem software. Nomic AI maintains and supports this software ecosystem in order to enforce quality and safety, and to enable any person or company to easily train and deploy large language models on the edge. Data is a key ingredient in building a powerful and general-purpose large-language model. The GPT4All Community has created the GPT4All Open Source Data Lake as a staging area for contributing instruction and assistance tuning data for future GPT4All Model Trains.

Stability AI, in collaboration with its CarperAI Lab, announces Stable Beluga 1 (formerly codenamed FreeWilly) and its successor Stable Beluga 2 - two powerful, new Large Language Models. Both models show exceptional reasoning abilities across a variety of benchmarks. Stable Beluga 1 leverages the original LLaMA 65B foundation model and was carefully fine-tuned with a new synthetically-generated dataset using Supervised Fine-Tune (SFT) in standard Alpaca format. Stable Beluga 2 uses the LLaMA 270B foundation model for industry-leading performance.

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.

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.

OLMo 2 is an open language model family developed by the Allen Institute for AI. It provides researchers and developers with open-source code and reproducible training recipes. These models can be trained with up to 5 trillion tokens, and they are competitive against other open-weight models such as Llama 3.0 on English academic benchmarks. OLMo 2 focuses on training stability by implementing techniques that prevent loss spikes in long training runs. It also uses staged training interventions to address capability deficits during late pretraining. The models incorporate the latest post-training methods from AI2's Tulu 3 resulting in OLMo 2-Instruct. The Open Language Modeling Evaluation System, or OLMES, was created to guide improvements throughout the development stages. It consists of 20 evaluation benchmarks assessing key capabilities.

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.

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.

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.

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.

OpenELM is a family of open-source language models developed by Apple. It uses a layering strategy to allocate parameters efficiently within each layer of a transformer model. This leads to improved accuracy compared to other open language models. OpenELM was trained using publicly available datasets, and it achieves the best performance for its size.

IBM® Granite™ is an AI family that was designed from scratch for business applications. It helps to ensure trust and scalability of AI-driven apps. Granite models are open source and available today. We want to make AI accessible to as many developers as we can. We have made the core Granite Code, Time Series models, Language and GeoSpatial available on Hugging Face, under a permissive Apache 2.0 licence that allows for broad commercial use. Granite models are all trained using carefully curated data. The data used to train them is transparent at a level that is unmatched in the industry. We have also made the tools that we use available to ensure that the data is of high quality and meets the standards required by enterprise-grade applications.

NVIDIA Nemotron, a family open-source models created by NVIDIA is designed to generate synthetic language data for commercial applications. The Nemotron-4 model 340B is an important release by NVIDIA. It offers developers a powerful tool for generating high-quality data, and filtering it based upon various attributes, using a reward system.

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.

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.

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.

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.

Vicuna-13B, an open-source chatbot, is trained by fine-tuning LLaMA using user-shared conversations from ShareGPT. Vicuna-13B's preliminary evaluation using GPT-4, as a judge, shows that it achieves a quality of more than 90%* for OpenAI ChatGPT or Google Bard and outperforms other models such as LLaMA or Stanford Alpaca. Vicuna-13B costs around $300 to train. The online demo and the code, along with weights, are available to non-commercial users.

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.

Our new flagship model, the Small Language Model (SLM), has all the power and performance that you would expect from a leading LLM. Excels at generalized tasks, instruction-following, and human preferences. The best 70B model available. SuperNova is a generalized task-based AI that can be used for any generalized task. It's similar to Open AI's GPT4o and Claude Sonnet 3.5. SuperNova is trained with the most advanced optimization & learning techniques to generate highly accurate responses. It is the most flexible, cost-effective, and secure language model available. Customers can save up to 95% in total deployment costs when compared with traditional closed-source models. SuperNova can be used to integrate AI in apps and products, as well as for general chat and a variety of other uses. Update your models regularly with the latest open source tech to ensure you're not locked into a single solution. Protect your data using industry-leading privacy features.

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.

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.

The training of state-of-the art language models is extremely difficult. They require large compute budgets, complex distributed computing techniques and deep ML knowledge. Few organizations are able to train large language models from scratch. The number of organizations that do not open source their results is increasing, even though they have the expertise and resources to do so. We at Cerebras believe in open access to the latest models. Cerebras is proud to announce that Cerebras GPT, a family GPT models with 111 million to thirteen billion parameters, has been released to the open-source community. These models are trained using the Chinchilla Formula and provide the highest accuracy within a given computing budget. Cerebras GPT has faster training times and lower training costs. It also consumes less power than any other publicly available model.

We have just launched the X1 large model series. Giga ML’s most powerful model can be used for pre-training, fine-tuning and on-prem deployment. We are Open AI compliant, so your existing integrations, such as long chain, llama index, and others, will work seamlessly. You can continue to pre-train LLM's using domain-specific databooks or docs, or company documents. The world of large-scale language models (LLMs), which offer unprecedented opportunities for natural language process across different domains, is rapidly expanding. Despite this, there are still some critical challenges that remain unresolved. Giga ML proudly introduces the X1 Large model 32k, a pioneering LLM solution on-premise that addresses these critical challenges.

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

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.

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.

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.

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.

Open source AI model that you can fine-tune and distill anywhere. Our latest instruction-tuned models are available in 8B 70B and 405B version. Our open ecosystem allows you to build faster using a variety of product offerings that are differentiated and support your use cases. Choose between real-time or batch inference. Download model weights for further cost-per-token optimization. Adapt to your application, improve using synthetic data, and deploy on-prem. Use Llama components and extend the Llama model using RAG and zero shot tools to build agentic behavior. Use 405B high-quality data to improve specialized model for specific use cases.

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.

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.

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).

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.

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.

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.