Top Open R1 Alternatives

OpenEuroLLM embodies a collaborative initiative among leading AI companies and research institutions throughout Europe, focused on developing a series of open-source foundational models to enhance transparency in artificial intelligence across the continent. This project emphasizes accessibility by providing open data, comprehensive documentation, code for training and testing, and evaluation metrics, which encourages active involvement from the community. It is structured to align with European Union regulations, aiming to produce effective large language models that fulfill Europe’s specific requirements. A key feature of this endeavor is its dedication to linguistic and cultural diversity, ensuring that multilingual capacities encompass all official EU languages and potentially even more. In addition, the initiative seeks to expand access to foundational models that can be tailored for various applications, improve evaluation results in multiple languages, and increase the availability of training datasets and benchmarks for researchers and developers. By distributing tools, methodologies, and preliminary findings, transparency is maintained throughout the entire training process, fostering an environment of trust and collaboration within the AI community. Ultimately, the vision of OpenEuroLLM is to create more inclusive and versatile AI solutions that truly represent the rich tapestry of European languages and cultures, while also setting a precedent for future collaborative AI projects.

Sky-T1-32B-Preview represents a groundbreaking open-source reasoning model developed by the NovaSky team at UC Berkeley's Sky Computing Lab. It achieves performance levels similar to those of proprietary models like o1-preview across a range of reasoning and coding tests, all while being created for under $450, emphasizing its potential to provide advanced reasoning skills at a lower cost. Fine-tuned from Qwen2.5-32B-Instruct, this model was trained on a carefully selected dataset of 17,000 examples that cover diverse areas, including mathematics and programming. The training was efficiently completed in a mere 19 hours with the aid of eight H100 GPUs using DeepSpeed Zero-3 offloading technology. Notably, every aspect of this project—spanning data, code, and model weights—is fully open-source, enabling both the academic and open-source communities to not only replicate but also enhance the model's functionalities. Such openness promotes a spirit of collaboration and innovation within the artificial intelligence research and development landscape, inviting contributions from various sectors. Ultimately, this initiative represents a significant step forward in making powerful AI tools more accessible to a wider audience.

Explore the boundaries of personal alignment, artificial intelligence, open-source initiatives, and decentralization through bold experimentation that many large corporations and governmental bodies tend to avoid. Hermes 3 is equipped with advanced features such as robust long-term context retention and the capability to facilitate multi-turn dialogues, alongside complex role-playing and internal monologue functionalities, as well as enhanced agentic function-calling abilities. This model is meticulously designed to ensure accurate compliance with system prompts and instructions while remaining adaptable. By refining Llama 3.1 in various configurations—ranging from 8B to 70B and even 405B—and leveraging a dataset primarily made up of synthetically created examples, Hermes 3 not only matches but often outperforms Llama 3.1, revealing deeper potential for reasoning and innovative tasks. This series of models focused on instruction and tool usage showcases remarkable reasoning and creative capabilities, setting the stage for groundbreaking applications. Ultimately, Hermes 3 signifies a transformative leap in the realm of AI technology, promising to reshape future interactions and developments. As we continue to innovate, the possibilities for practical applications seem boundless.

Tülu 3 represents a state-of-the-art language model designed by the Allen Institute for AI (Ai2) with the objective of enhancing expertise in various domains such as knowledge, reasoning, mathematics, coding, and safety. Built on the foundation of the Llama 3 Base, it undergoes an intricate four-phase post-training process: meticulous prompt curation and synthesis, supervised fine-tuning across a diverse range of prompts and outputs, preference tuning with both off-policy and on-policy data, and a distinctive reinforcement learning approach that bolsters specific skills through quantifiable rewards. This open-source model is distinguished by its commitment to transparency, providing comprehensive access to its training data, coding resources, and evaluation metrics, thus helping to reduce the performance gap typically seen between open-source and proprietary fine-tuning methodologies. Performance evaluations indicate that Tülu 3 excels beyond similarly sized models, such as Llama 3.1-Instruct and Qwen2.5-Instruct, across multiple benchmarks, emphasizing its superior effectiveness. The ongoing evolution of Tülu 3 not only underscores a dedication to enhancing AI capabilities but also fosters an inclusive and transparent technological landscape. As such, it paves the way for future advancements in artificial intelligence that prioritize collaboration and accessibility for all users.

Stability AI, in collaboration with its CarperAI lab, proudly introduces Stable Beluga 1 and its enhanced version, Stable Beluga 2, formerly called FreeWilly, both of which are powerful new Large Language Models (LLMs) now accessible to the public. These innovations demonstrate exceptional reasoning abilities across a diverse array of benchmarks, highlighting their adaptability and robustness. Stable Beluga 1 is constructed upon the foundational LLaMA 65B model and has been carefully fine-tuned using a cutting-edge synthetically-generated dataset through Supervised Fine-Tune (SFT) in the traditional Alpaca format. Similarly, Stable Beluga 2 is based on the LLaMA 2 70B model, further advancing performance standards in the field. The introduction of these models signifies a major advancement in the progression of open access AI technology, paving the way for future developments in the sector. With their release, users can expect enhanced capabilities that could revolutionize various applications.

DeepSeek-V2 represents an advanced Mixture-of-Experts (MoE) language model created by DeepSeek-AI, recognized for its economical training and superior inference efficiency. This model features a staggering 236 billion parameters, engaging only 21 billion for each token, and can manage a context length stretching up to 128K tokens. It employs sophisticated architectures like Multi-head Latent Attention (MLA) to enhance inference by reducing the Key-Value (KV) cache and utilizes DeepSeekMoE for cost-effective training through sparse computations. When compared to its earlier version, DeepSeek 67B, this model exhibits substantial advancements, boasting a 42.5% decrease in training costs, a 93.3% reduction in KV cache size, and a remarkable 5.76-fold increase in generation speed. With training based on an extensive dataset of 8.1 trillion tokens, DeepSeek-V2 showcases outstanding proficiency in language understanding, programming, and reasoning tasks, thereby establishing itself as a premier open-source model in the current landscape. Its groundbreaking methodology not only enhances performance but also sets unprecedented standards in the realm of artificial intelligence, inspiring future innovations in the field.

The QwQ-32B model, developed by the Qwen team at Alibaba Cloud, marks a notable leap forward in AI reasoning, specifically designed to enhance problem-solving capabilities. With an impressive 32 billion parameters, it competes with top-tier models like DeepSeek's R1, which boasts a staggering 671 billion parameters. This exceptional efficiency arises from its streamlined parameter usage, allowing QwQ-32B to effectively address intricate challenges, including mathematical reasoning, programming, and various problem-solving tasks, all while using fewer resources. It can manage a context length of up to 32,000 tokens, demonstrating its proficiency in processing extensive input data. Furthermore, QwQ-32B is accessible via Alibaba's Qwen Chat service and is released under the Apache 2.0 license, encouraging collaboration and innovation within the AI development community. As it combines advanced features with efficient processing, QwQ-32B has the potential to significantly influence advancements in artificial intelligence technology. Its unique capabilities position it as a valuable tool for developers and researchers alike.

DeepSeek-R1 represents a state-of-the-art open-source reasoning model developed by DeepSeek, designed to rival OpenAI's Model o1. Accessible through web, app, and API platforms, it demonstrates exceptional skills in intricate tasks such as mathematics and programming, achieving notable success on exams like the American Invitational Mathematics Examination (AIME) and MATH. This model employs a mixture of experts (MoE) architecture, featuring an astonishing 671 billion parameters, of which 37 billion are activated for every token, enabling both efficient and accurate reasoning capabilities. As part of DeepSeek's commitment to advancing artificial general intelligence (AGI), this model highlights the significance of open-source innovation in the realm of AI. Additionally, its sophisticated features have the potential to transform our methodologies in tackling complex challenges across a variety of fields, paving the way for novel solutions and advancements. The influence of DeepSeek-R1 may lead to a new era in how we understand and utilize AI for problem-solving.

DeepSeek R2 is the much-anticipated successor to the original DeepSeek R1, an AI reasoning model that garnered significant attention upon its launch in January 2025 by the Chinese startup DeepSeek. This latest iteration enhances the impressive groundwork laid by R1, which transformed the AI domain by delivering cost-effective capabilities that rival top-tier models such as OpenAI's o1. R2 is poised to deliver a notable enhancement in performance, promising rapid processing and reasoning skills that closely mimic human capabilities, especially in demanding fields like intricate coding and higher-level mathematics. By leveraging DeepSeek's advanced Mixture-of-Experts framework alongside refined training methodologies, R2 aims to exceed the benchmarks set by its predecessor while maintaining a low computational footprint. Furthermore, there is a strong expectation that this model will expand its reasoning prowess to include additional languages beyond English, potentially enhancing its applicability on a global scale. The excitement surrounding R2 underscores the continuous advancement of AI technology and its potential to impact a variety of sectors significantly, paving the way for innovations that could redefine how we interact with machines.

Janus-Pro-7B represents a significant leap forward in open-source multimodal AI technology, created by DeepSeek to proficiently analyze and generate content that includes text, images, and videos. Its unique autoregressive framework features specialized pathways for visual encoding, significantly boosting its capability to perform diverse tasks such as generating images from text prompts and conducting complex visual analyses. Outperforming competitors like DALL-E 3 and Stable Diffusion in numerous benchmarks, it offers scalability with versions that range from 1 billion to 7 billion parameters. Available under the MIT License, Janus-Pro-7B is designed for easy access in both academic and commercial settings, showcasing a remarkable progression in AI development. Moreover, this model is compatible with popular operating systems including Linux, MacOS, and Windows through Docker, ensuring that it can be easily integrated into various platforms for practical use. This versatility opens up numerous possibilities for innovation and application across multiple industries.

DeepSeek-V3 is a remarkable leap forward in the realm of artificial intelligence, meticulously crafted to demonstrate exceptional prowess in understanding natural language, complex reasoning, and effective decision-making. By leveraging cutting-edge neural network architectures, this model assimilates extensive datasets along with sophisticated algorithms to tackle challenging issues in numerous domains such as research, development, business analytics, and automation. With a strong emphasis on scalability and operational efficiency, DeepSeek-V3 provides developers and organizations with groundbreaking tools that can greatly accelerate advancements and yield transformative outcomes. Additionally, its adaptability ensures that it can be applied in a multitude of contexts, thereby enhancing its significance across various sectors. This innovative approach not only streamlines processes but also opens new avenues for exploration and growth in artificial intelligence applications.

StarCoder and StarCoderBase are sophisticated Large Language Models crafted for coding tasks, built from freely available data sourced from GitHub, which includes an extensive array of over 80 programming languages, along with Git commits, GitHub issues, and Jupyter notebooks. Similarly to LLaMA, these models were developed with around 15 billion parameters trained on an astonishing 1 trillion tokens. Additionally, StarCoderBase was specifically optimized with 35 billion Python tokens, culminating in the evolution of what we now recognize as StarCoder. Our assessments revealed that StarCoderBase outperforms other open-source Code LLMs when evaluated against well-known programming benchmarks, matching or even exceeding the performance of proprietary models like OpenAI's code-cushman-001 and the original Codex, which was instrumental in the early development of GitHub Copilot. With a remarkable context length surpassing 8,000 tokens, the StarCoder models can manage more data than any other open LLM available, thus unlocking a plethora of possibilities for innovative applications. This adaptability is further showcased by our ability to engage with the StarCoder models through a series of interactive dialogues, effectively transforming them into versatile technical aides capable of assisting with a wide range of programming challenges. Furthermore, this interactive capability enhances user experience, making it easier for developers to obtain immediate support and insights on complex coding issues.

Qwen2.5-Max is a cutting-edge Mixture-of-Experts (MoE) model developed by the Qwen team, trained on a vast dataset of over 20 trillion tokens and improved through techniques such as Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). It outperforms models like DeepSeek V3 in various evaluations, excelling in benchmarks such as Arena-Hard, LiveBench, LiveCodeBench, and GPQA-Diamond, and also achieving impressive results in tests like MMLU-Pro. Users can access this model via an API on Alibaba Cloud, which facilitates easy integration into various applications, and they can also engage with it directly on Qwen Chat for a more interactive experience. Furthermore, Qwen2.5-Max's advanced features and high performance mark a remarkable step forward in the evolution of AI technology. It not only enhances productivity but also opens new avenues for innovation in the field.

QwQ-Max-Preview represents an advanced AI model built on the Qwen2.5-Max architecture, designed to demonstrate exceptional abilities in areas such as intricate reasoning, mathematical challenges, programming tasks, and agent-based activities. This preview highlights its improved functionalities across various general-domain applications, showcasing a strong capability to handle complex workflows effectively. Set to be launched as open-source software under the Apache 2.0 license, QwQ-Max-Preview is expected to feature substantial enhancements and refinements in its final version. In addition to its technical advancements, the model plays a vital role in fostering a more inclusive AI landscape, which is further supported by the upcoming release of the Qwen Chat application and streamlined model options like QwQ-32B, aimed at developers seeking local deployment alternatives. This initiative not only enhances accessibility for a broader audience but also stimulates creativity and progress within the AI community, ensuring that diverse voices can contribute to the field's evolution. The commitment to open-source principles is likely to inspire further exploration and collaboration among developers.

Vicuna-13B is a conversational AI created by fine-tuning LLaMA on a collection of user dialogues sourced from ShareGPT. Early evaluations, using GPT-4 as a benchmark, suggest that Vicuna-13B reaches over 90% of the performance level found in OpenAI's ChatGPT and Google Bard, while outperforming other models like LLaMA and Stanford Alpaca in more than 90% of tested cases. The estimated cost to train Vicuna-13B is around $300, which is quite economical for a model of its caliber. Furthermore, the model's source code and weights are publicly accessible under non-commercial licenses, promoting a spirit of collaboration and further development. This level of transparency not only fosters innovation but also allows users to delve into the model's functionalities across various applications, paving the way for new ideas and enhancements. Ultimately, such initiatives can significantly contribute to the advancement of conversational AI technologies.

We are excited to unveil the latest version of our open-source large language model, which includes model weights and initial code for the pretrained and fine-tuned Llama language models, ranging from 7 billion to 70 billion parameters. The Llama 2 pretrained models have been crafted using a remarkable 2 trillion tokens and boast double the context length compared to the first iteration, Llama 1. Additionally, the fine-tuned models have been refined through the insights gained from over 1 million human annotations. Llama 2 showcases outstanding performance compared to various other open-source language models across a wide array of external benchmarks, particularly excelling in reasoning, coding abilities, proficiency, and knowledge assessments. For its training, Llama 2 leveraged publicly available online data sources, while the fine-tuned variant, Llama-2-chat, integrates publicly accessible instruction datasets alongside the extensive human annotations mentioned earlier. Our project is backed by a robust coalition of global stakeholders who are passionate about our open approach to AI, including companies that have offered valuable early feedback and are eager to collaborate with us on Llama 2. The enthusiasm surrounding Llama 2 not only highlights its advancements but also marks a significant transformation in the collaborative development and application of AI technologies. This collective effort underscores the potential for innovation that can emerge when the community comes together to share resources and insights.

We provide rapid and accurate AI models tailored for effective use in production settings. Our inference API is engineered for maximum uptime, harnessing the latest NVIDIA GPUs to deliver peak performance. Additionally, we have compiled a diverse array of high-quality open-source natural language processing (NLP) models sourced from the community, making them easily accessible for your projects. You can also customize your own models, including GPT-J, or upload your proprietary models for smooth integration into production. Through a user-friendly dashboard, you can swiftly upload or fine-tune AI models, enabling immediate deployment without the complexities of managing factors like memory constraints, uptime, or scalability. You have the freedom to upload an unlimited number of models and deploy them as necessary, fostering a culture of continuous innovation and adaptability to meet your dynamic needs. This comprehensive approach provides a solid foundation for utilizing AI technologies effectively in your initiatives, promoting growth and efficiency in your workflows.

Smaug-72B stands out as a powerful open-source large language model (LLM) with several noteworthy characteristics: Outstanding Performance: It leads the Hugging Face Open LLM leaderboard, surpassing models like GPT-3.5 across various assessments, showcasing its adeptness in understanding, responding to, and producing text that closely mimics human language. Open Source Accessibility: Unlike many premium LLMs, Smaug-72B is available for public use and modification, fostering collaboration and innovation within the artificial intelligence community. Focus on Reasoning and Mathematics: This model is particularly effective in tackling reasoning and mathematical tasks, a strength stemming from targeted fine-tuning techniques employed by its developers at Abacus AI. Based on Qwen-72B: Essentially, it is an enhanced iteration of the robust LLM Qwen-72B, originally released by Alibaba, which contributes to its superior performance. In conclusion, Smaug-72B represents a significant progression in the field of open-source artificial intelligence, serving as a crucial asset for both developers and researchers. Its distinctive capabilities not only elevate its prominence but also play an integral role in the continual advancement of AI technology, inspiring further exploration and development in this dynamic field.

This repository presents the research preview for LongLLaMA, an innovative large language model capable of handling extensive contexts, reaching up to 256,000 tokens or potentially even more. Built on the OpenLLaMA framework, LongLLaMA has been fine-tuned using the Focused Transformer (FoT) methodology. The foundational code for this model comes from Code Llama. We are excited to introduce a smaller 3B base version of the LongLLaMA model, which is not instruction-tuned, and it will be released under an open license (Apache 2.0). Accompanying this release is inference code that supports longer contexts, available on Hugging Face. The model's weights are designed to effortlessly integrate with existing systems tailored for shorter contexts, particularly those that accommodate up to 2048 tokens. In addition to these features, we provide evaluation results and comparisons to the original OpenLLaMA models, thus offering a thorough insight into LongLLaMA's effectiveness in managing long-context tasks. This advancement marks a significant step forward in the field of language models, enabling more sophisticated applications and research opportunities.

ChatGPT, developed by OpenAI, is a sophisticated language model that generates coherent and contextually appropriate replies by drawing from a wide selection of internet text. Its extensive training equips it to tackle a multitude of tasks in natural language processing, such as engaging in dialogues, responding to inquiries, and producing text in diverse formats. Leveraging deep learning algorithms, ChatGPT employs a transformer architecture that has demonstrated remarkable efficiency in numerous NLP tasks. Additionally, the model can be customized for specific applications, such as language translation, text categorization, and answering questions, allowing developers to create advanced NLP systems with greater accuracy. Besides its text generation capabilities, ChatGPT is also capable of interpreting and writing code, highlighting its adaptability in managing various content types. This broad range of functionalities not only enhances its utility but also paves the way for innovative integrations into an array of technological solutions. The ongoing advancements in AI technology are likely to further elevate the capabilities of models like ChatGPT, making them even more integral to our everyday interactions with machines.

ChatGLM-6B is a dialogue model that operates in both Chinese and English, constructed on the General Language Model (GLM) architecture, featuring a robust 6.2 billion parameters. Utilizing advanced model quantization methods, it can efficiently function on typical consumer graphics cards, needing just 6GB of video memory at the INT4 quantization tier. This model incorporates techniques similar to those utilized in ChatGPT but is specifically optimized to improve interactions and dialogues in Chinese. After undergoing rigorous training with around 1 trillion identifiers across both languages, it has also benefited from enhanced supervision, fine-tuning, self-guided feedback, and reinforcement learning driven by human input. As a result, ChatGLM-6B has shown remarkable proficiency in generating responses that resonate effectively with users. Its versatility and high performance render it an essential asset for facilitating bilingual communication, making it an invaluable resource in multilingual environments.

We are thrilled to unveil our new X1 large series of models, marking a significant advancement in our offerings. The most powerful model from Giga ML is now available for both pre-training and fine-tuning in an on-premises setup. Our integration with Open AI ensures seamless compatibility with existing tools such as long chain, llama-index, and more, enhancing usability. Additionally, users have the option to pre-train LLMs using tailored data sources, including industry-specific documents or proprietary company files. As the realm of large language models (LLMs) continues to rapidly advance, it presents remarkable opportunities for breakthroughs in natural language processing across diverse sectors. However, the industry still faces several substantial challenges that need addressing. At Giga ML, we are proud to present the X1 Large 32k model, an innovative on-premise LLM solution crafted to confront these key challenges head-on, empowering organizations to fully leverage the capabilities of LLMs. This launch is not just a step forward for our technology, but a major stride towards enhancing the language processing capabilities of businesses everywhere. We believe that by providing these advanced tools, we can drive meaningful improvements in how organizations communicate and operate.

Falcon-40B is a decoder-only model boasting 40 billion parameters, created by TII and trained on a massive dataset of 1 trillion tokens from RefinedWeb, along with other carefully chosen datasets. It is shared under the Apache 2.0 license, making it accessible for various uses. Why should you consider utilizing Falcon-40B? This model distinguishes itself as the premier open-source choice currently available, outpacing rivals such as LLaMA, StableLM, RedPajama, and MPT, as highlighted by its position on the OpenLLM Leaderboard. Its architecture is optimized for efficient inference and incorporates advanced features like FlashAttention and multiquery functionality, enhancing its performance. Additionally, the flexible Apache 2.0 license allows for commercial utilization without the burden of royalties or limitations. It's essential to recognize that this model is in its raw, pretrained state and is typically recommended to be fine-tuned to achieve the best results for most applications. For those seeking a version that excels in managing general instructions within a conversational context, Falcon-40B-Instruct might serve as a suitable alternative worth considering. Overall, Falcon-40B represents a formidable tool for developers looking to leverage cutting-edge AI technology in their projects.

PygmalionAI is a dynamic community dedicated to advancing open-source projects that leverage EleutherAI's GPT-J 6B and Meta's LLaMA models. In essence, Pygmalion focuses on creating AI designed for interactive dialogues and roleplaying experiences. The Pygmalion AI model is actively maintained and currently showcases the 7B variant, which is based on Meta AI's LLaMA framework. With a minimal requirement of just 18GB (or even less) of VRAM, Pygmalion provides exceptional chat capabilities that surpass those of much larger language models, all while being resource-efficient. Our carefully curated dataset, filled with high-quality roleplaying material, ensures that your AI companion will excel in various roleplaying contexts. Both the model weights and the training code are fully open-source, granting you the liberty to modify and share them as you wish. Typically, language models like Pygmalion are designed to run on GPUs, as they need rapid memory access and significant computational power to produce coherent text effectively. Consequently, users can anticipate a fluid and engaging interaction experience when utilizing Pygmalion's features. This commitment to both performance and community collaboration makes Pygmalion a standout choice in the realm of conversational AI.

Perplexity AI has unveiled R1 1776 as an open-source large language model (LLM) constructed on the DeepSeek R1 framework, aimed at promoting transparency and facilitating collaborative endeavors in AI development. This release allows researchers and developers to delve into the model's architecture and source code, enabling them to refine and adapt it for various applications. Through the public availability of R1 1776, Perplexity AI aspires to stimulate innovation while maintaining ethical principles within the AI industry. This initiative not only empowers the community but also cultivates a culture of shared knowledge and accountability among those working in AI. Furthermore, it represents a significant step towards democratizing access to advanced AI technologies.

Reka Flash 3 stands as a state-of-the-art multimodal AI model, boasting 21 billion parameters and developed by Reka AI, to excel in diverse tasks such as engaging in general conversations, coding, adhering to instructions, and executing various functions. This innovative model skillfully processes and interprets a wide range of inputs, which includes text, images, video, and audio, making it a compact yet versatile solution fit for numerous applications. Constructed from the ground up, Reka Flash 3 was trained on a diverse collection of datasets that include both publicly accessible and synthetic data, undergoing a thorough instruction tuning process with carefully selected high-quality information to refine its performance. The concluding stage of its training leveraged reinforcement learning techniques, specifically the REINFORCE Leave One-Out (RLOO) method, which integrated both model-driven and rule-oriented rewards to enhance its reasoning capabilities significantly. With a remarkable context length of 32,000 tokens, Reka Flash 3 effectively competes against proprietary models such as OpenAI's o1-mini, making it highly suitable for applications that demand low latency or on-device processing. Operating at full precision, the model requires a memory footprint of 39GB (fp16), but this can be optimized down to just 11GB through 4-bit quantization, showcasing its flexibility across various deployment environments. Furthermore, Reka Flash 3's advanced features ensure that it can adapt to a wide array of user requirements, thereby reinforcing its position as a leader in the realm of multimodal AI technology. This advancement not only highlights the progress made in AI but also opens doors to new possibilities for innovation across different sectors.

The newest version of the open-source AI framework, which can be customized and utilized across different platforms, is available in several configurations: 1B, 3B, 11B, and 90B, while still offering the option to use Llama 3.1. Llama 3.2 includes a selection of large language models (LLMs) that are pretrained and fine-tuned specifically for multilingual text processing in 1B and 3B sizes, whereas the 11B and 90B models support both text and image inputs, generating text outputs. This latest release empowers users to build highly effective applications that cater to specific requirements. For applications running directly on devices, such as summarizing conversations or managing calendars, the 1B or 3B models are excellent selections. On the other hand, the 11B and 90B models are particularly suited for tasks involving images, allowing users to manipulate existing pictures or glean further insights from images in their surroundings. Ultimately, this broad spectrum of models opens the door for developers to experiment with creative applications across a wide array of fields, enhancing the potential for innovation and impact.

Our sophisticated multimodal assistant has been thoughtfully designed with an emphasis on privacy, security, and operational efficiency. Yasa is equipped to analyze a range of content types, such as text, images, videos, and tables, with ambitions to broaden its capabilities in the future. It serves as a valuable resource for generating ideas for creative endeavors, addressing basic inquiries, and extracting meaningful insights from your proprietary data. With only a few simple commands, you can create, train, compress, or implement it on your own infrastructure. Our unique algorithms allow for customization of the model to suit your individual data and needs. We employ cutting-edge methods that include retrieval, fine-tuning, self-supervised instruction tuning, and reinforcement learning to enhance our model, ensuring it aligns effectively with your specific operational demands. This approach not only improves user satisfaction but also fosters productivity and innovation in a rapidly evolving landscape. As we continue to refine our technology, we remain committed to providing solutions that empower users to achieve their goals.

Aya stands as a pioneering open-source generative large language model that supports a remarkable 101 languages, far exceeding the offerings of other open-source alternatives. This expansive language support allows researchers to harness the powerful capabilities of LLMs for numerous languages and cultures that have frequently been neglected by dominant models in the industry. Alongside the launch of the Aya model, we are also unveiling the largest multilingual instruction fine-tuning dataset, which contains 513 million entries spanning 114 languages. This extensive dataset is enriched with distinctive annotations from native and fluent speakers around the globe, ensuring that AI technology can address the needs of a diverse international community that has often encountered obstacles to access. Therefore, Aya not only broadens the horizons of multilingual AI but also fosters inclusivity among various linguistic groups, paving the way for future advancements in the field. By creating an environment where linguistic diversity is celebrated, Aya stands to inspire further innovations that can bridge gaps in communication and understanding.

We are excited to unveil an open-source AI model that offers the ability to be fine-tuned, distilled, and deployed across a wide range of platforms. Our latest instruction-tuned model is available in three different sizes: 8B, 70B, and 405B, allowing you to select an option that best fits your unique needs. The open ecosystem we provide accelerates your development journey with a variety of customized product offerings tailored to meet your specific project requirements. You can choose between real-time inference and batch inference services, depending on what your project requires, giving you added flexibility to optimize performance. Furthermore, downloading model weights can significantly enhance cost efficiency per token while you fine-tune the model for your application. To further improve performance, you can leverage synthetic data and seamlessly deploy your solutions either on-premises or in the cloud. By taking advantage of Llama system components, you can also expand the model's capabilities through the use of zero-shot tools and retrieval-augmented generation (RAG), promoting more agentic behaviors in your applications. Utilizing the extensive 405B high-quality data enables you to fine-tune specialized models that cater specifically to various use cases, ensuring that your applications function at their best. In conclusion, this empowers developers to craft innovative solutions that not only meet efficiency standards but also drive effectiveness in their respective domains, leading to a significant impact on the technology landscape.

Mistral 7B is a cutting-edge language model boasting 7.3 billion parameters, which excels in various benchmarks, even surpassing larger models such as Llama 2 13B. It employs advanced methods like Grouped-Query Attention (GQA) to enhance inference speed and Sliding Window Attention (SWA) to effectively handle extensive sequences. Available under the Apache 2.0 license, Mistral 7B can be deployed across multiple platforms, including local infrastructures and major cloud services. Additionally, a unique variant called Mistral 7B Instruct has demonstrated exceptional abilities in task execution, consistently outperforming rivals like Llama 2 13B Chat in certain applications. This adaptability and performance make Mistral 7B a compelling choice for both developers and researchers seeking efficient solutions. Its innovative features and strong results highlight the model's potential impact on natural language processing projects.

Leverage advanced coding and linguistic models across a wide range of applications. Tap into the capabilities of extensive generative AI models that offer a profound understanding of both language and programming, facilitating innovative reasoning and comprehension essential for creating cutting-edge applications. These models find utility in various areas, such as writing assistance, code generation, and data analytics, all while adhering to responsible AI guidelines to mitigate any potential misuse, supported by robust Azure security measures. Utilize generative models that have been exposed to extensive datasets, enabling their use in multiple contexts like language processing, coding assignments, logical reasoning, inferencing, and understanding. Customize these generative models to suit your specific requirements by employing labeled datasets through an easy-to-use REST API. You can improve the accuracy of your outputs by refining the model’s hyperparameters and applying few-shot learning strategies to provide the API with examples, resulting in more relevant outputs and ultimately boosting application effectiveness. By implementing appropriate configurations and optimizations, you can significantly enhance your application's performance while ensuring a commitment to ethical practices in AI application. Additionally, the continuous evolution of these models allows for ongoing improvements, keeping pace with advancements in technology.

The latest iteration in the Llama series, Llama 3.3, marks a notable leap forward in the realm of language models, designed to improve AI's abilities in both understanding and communication. It features enhanced contextual reasoning, more refined language generation, and state-of-the-art fine-tuning capabilities that yield remarkably accurate, human-like responses for a wide array of applications. This version benefits from a broader training dataset, advanced algorithms that allow for deeper comprehension, and reduced biases when compared to its predecessors. Llama 3.3 excels in various domains such as natural language understanding, creative writing, technical writing, and multilingual conversations, making it an invaluable tool for businesses, developers, and researchers. Furthermore, its modular design lends itself to adaptable deployment across specific sectors, ensuring consistent performance and flexibility even in expansive applications. With these significant improvements, Llama 3.3 is set to transform the benchmarks for AI language models and inspire further innovations in the field. It is an exciting time for AI development as this new version opens doors to novel possibilities in human-computer interaction.

We are thrilled to unveil Phi-2, a language model boasting 2.7 billion parameters that demonstrates exceptional reasoning and language understanding, achieving outstanding results when compared to other base models with fewer than 13 billion parameters. In rigorous benchmark tests, Phi-2 not only competes with but frequently outperforms larger models that are up to 25 times its size, a remarkable achievement driven by significant advancements in model scaling and careful training data selection. Thanks to its streamlined architecture, Phi-2 is an invaluable asset for researchers focused on mechanistic interpretability, improving safety protocols, or experimenting with fine-tuning across a diverse array of tasks. To foster further research and innovation in the realm of language modeling, Phi-2 has been incorporated into the Azure AI Studio model catalog, promoting collaboration and development within the research community. Researchers can utilize this powerful model to discover new insights and expand the frontiers of language technology, ultimately paving the way for future advancements in the field. The integration of Phi-2 into such a prominent platform signifies a commitment to enhancing collaborative efforts and driving progress in language processing capabilities.

Dolly stands out as a cost-effective large language model, showcasing an impressive capability for following instructions akin to that of ChatGPT. The research conducted by the Alpaca team has shown that advanced models can be trained to significantly improve their adherence to high-quality instructions; however, our research suggests that even earlier open-source models can exhibit exceptional behavior when fine-tuned with a limited amount of instructional data. By making slight modifications to an existing open-source model containing 6 billion parameters from EleutherAI, Dolly has been enhanced to better follow instructions, demonstrating skills such as brainstorming and text generation that were previously lacking. This strategy not only emphasizes the untapped potential of older models but also invites exploration into new and innovative uses of established technologies. Furthermore, the success of Dolly encourages further investigation into how legacy models can be repurposed to meet contemporary needs effectively.

Yi-Lightning, developed by 01.AI under the guidance of Kai-Fu Lee, represents a remarkable advancement in large language models, showcasing both superior performance and affordability. It can handle a context length of up to 16,000 tokens and boasts a competitive pricing strategy of $0.14 per million tokens for both inputs and outputs. This makes it an appealing option for a variety of users in the market. The model utilizes an enhanced Mixture-of-Experts (MoE) architecture, which incorporates meticulous expert segmentation and advanced routing techniques, significantly improving its training and inference capabilities. Yi-Lightning has excelled across diverse domains, earning top honors in areas such as Chinese language processing, mathematics, coding challenges, and complex prompts on chatbot platforms, where it achieved impressive rankings of 6th overall and 9th in style control. Its development entailed a thorough process of pre-training, focused fine-tuning, and reinforcement learning based on human feedback, which not only boosts its overall effectiveness but also emphasizes user safety. Moreover, the model features notable improvements in memory efficiency and inference speed, solidifying its status as a strong competitor in the landscape of large language models. This innovative approach sets the stage for future advancements in AI applications across various sectors.

The Falcon-7B model is a causal decoder-only architecture with a total of 7 billion parameters, created by TII, and trained on a vast dataset consisting of 1,500 billion tokens from RefinedWeb, along with additional carefully curated corpora, all under the Apache 2.0 license. What are the benefits of using Falcon-7B? This model excels compared to other open-source options like MPT-7B, StableLM, and RedPajama, primarily because of its extensive training on an unimaginably large dataset of 1,500 billion tokens from RefinedWeb, supplemented by thoughtfully selected content, which is clearly reflected in its performance ranking on the OpenLLM Leaderboard. Furthermore, it features an architecture optimized for rapid inference, utilizing advanced technologies such as FlashAttention and multiquery strategies. In addition, the flexibility offered by the Apache 2.0 license allows users to pursue commercial ventures without worrying about royalties or stringent constraints. This unique blend of high performance and operational freedom positions Falcon-7B as an excellent option for developers in search of sophisticated modeling capabilities. Ultimately, the model's design and resourcefulness make it a compelling choice in the rapidly evolving landscape of machine learning.

IBM® Granite™ offers a collection of AI models tailored for business use, developed with a strong emphasis on trustworthiness and scalability in AI solutions. At present, the open-source Granite models are readily available for use. Our mission is to democratize AI access for developers, which is why we have made the core Granite Code, along with Time Series, Language, and GeoSpatial models, available as open-source on Hugging Face. These resources are shared under the permissive Apache 2.0 license, enabling broad commercial usage without significant limitations. Each Granite model is crafted using carefully curated data, providing outstanding transparency about the origins of the training material. Furthermore, we have released tools for validating and maintaining the quality of this data to the public, adhering to the high standards necessary for enterprise applications. This unwavering commitment to transparency and quality not only underlines our dedication to innovation but also encourages collaboration within the AI community, paving the way for future advancements.

We are excited to unveil Mistral NeMo, our latest and most sophisticated small model, boasting an impressive 12 billion parameters and a vast context length of 128,000 tokens, all available under the Apache 2.0 license. In collaboration with NVIDIA, Mistral NeMo stands out in its category for its exceptional reasoning capabilities, extensive world knowledge, and coding skills. Its architecture adheres to established industry standards, ensuring it is user-friendly and serves as a smooth transition for those currently using Mistral 7B. To encourage adoption by researchers and businesses alike, we are providing both pre-trained base models and instruction-tuned checkpoints, all under the Apache license. A remarkable feature of Mistral NeMo is its quantization awareness, which enables FP8 inference while maintaining high performance levels. Additionally, the model is well-suited for a range of global applications, showcasing its ability in function calling and offering a significant context window. When benchmarked against Mistral 7B, Mistral NeMo demonstrates a marked improvement in comprehending and executing intricate instructions, highlighting its advanced reasoning abilities and capacity to handle complex multi-turn dialogues. Furthermore, its design not only enhances its performance but also positions it as a formidable option for multi-lingual tasks, ensuring it meets the diverse needs of various use cases while paving the way for future innovations.

Phi-4 is an innovative small language model (SLM) with 14 billion parameters, demonstrating remarkable proficiency in complex reasoning tasks, especially in the realm of mathematics, in addition to standard language processing capabilities. Being the latest member of the Phi series of small language models, Phi-4 exemplifies the strides we can make as we push the horizons of SLM technology. Currently, it is available on Azure AI Foundry under a Microsoft Research License Agreement (MSRLA) and will soon be launched on Hugging Face. With significant enhancements in methodologies, including the use of high-quality synthetic datasets and meticulous curation of organic data, Phi-4 outperforms both similar and larger models in mathematical reasoning challenges. This model not only showcases the continuous development of language models but also underscores the important relationship between the size of a model and the quality of its outputs. As we forge ahead in innovation, Phi-4 serves as a powerful example of our dedication to advancing the capabilities of small language models, revealing both the opportunities and challenges that lie ahead in this field. Moreover, the potential applications of Phi-4 could significantly impact various domains requiring sophisticated reasoning and language comprehension.

In tribute to Cleopatra, whose dramatic story ended with the fateful encounter with a snake, we proudly present Codestral Mamba, a Mamba2 language model tailored for code generation and made available under an Apache 2.0 license. Codestral Mamba marks a pivotal step forward in our commitment to pioneering and refining innovative architectures. This model is available for free use, modification, and distribution, and we hope it will pave the way for new discoveries in architectural research. The Mamba models stand out due to their linear time inference capabilities, coupled with a theoretical ability to manage sequences of infinite length. This unique characteristic allows users to engage with the model seamlessly, delivering quick responses irrespective of the input size. Such remarkable efficiency is especially beneficial for boosting coding productivity; hence, we have integrated advanced coding and reasoning abilities into this model, ensuring it can compete with top-tier transformer-based models. As we push the boundaries of innovation, we are confident that Codestral Mamba will not only advance coding practices but also inspire new generations of developers. This exciting release underscores our dedication to fostering creativity and productivity within the tech community.

Teuken-7B is a cutting-edge multilingual language model designed to address the diverse linguistic landscape of Europe, emerging from the OpenGPT-X initiative. This model has been trained on a dataset where more than half comprises non-English content, effectively encompassing all 24 official languages of the European Union to ensure robust performance across these tongues. One of the standout features of Teuken-7B is its specially crafted multilingual tokenizer, which has been optimized for European languages, resulting in improved training efficiency and reduced inference costs compared to standard monolingual tokenizers. Users can choose between two distinct versions of the model: Teuken-7B-Base, which offers a foundational pre-trained experience, and Teuken-7B-Instruct, fine-tuned to enhance its responsiveness to user inquiries. Both variations are easily accessible on Hugging Face, promoting transparency and collaboration in the artificial intelligence sector while stimulating further advancements. The development of Teuken-7B not only showcases a commitment to fostering AI solutions but also underlines the importance of inclusivity and representation of Europe's rich cultural tapestry in technology. This initiative ultimately aims to bridge communication gaps and facilitate understanding among diverse populations across the continent.

Models designed to follow instructions, such as GPT-3.5 (text-DaVinci-003), ChatGPT, Claude, and Bing Chat, have experienced remarkable improvements in their functionalities, resulting in a notable increase in their utilization by users in various personal and professional environments. While their rising popularity and integration into everyday activities is evident, these models still face significant challenges, including the potential to spread misleading information, perpetuate detrimental stereotypes, and utilize offensive language. Addressing these pressing concerns necessitates active engagement from researchers and academics to further investigate these models. However, the pursuit of research on instruction-following models in academic circles has been complicated by the lack of accessible alternatives to proprietary systems like OpenAI’s text-DaVinci-003. To bridge this divide, we are excited to share our findings on Alpaca, an instruction-following language model that has been fine-tuned from Meta’s LLaMA 7B model, as we aim to enhance the dialogue and advancements in this domain. By shedding light on Alpaca, we hope to foster a deeper understanding of instruction-following models while providing researchers with a more attainable resource for their studies and explorations. This initiative marks a significant stride toward improving the overall landscape of instruction-following technologies.

AI21 Studio offers API access to its Jurassic-1 large language models, which are utilized for text generation and comprehension in countless applications. With our advanced models, you can address any language-related task. The Jurassic-1 models excel at following natural language instructions and require only a handful of examples to adapt to new challenges. Our APIs are ideally suited for standard tasks, including paraphrasing and summarization, providing exceptional results at competitive prices without the need for extensive reworking. If you're looking to fine-tune a personalized model, achieving that is just a few clicks away. The training process is swift and cost-effective, allowing for immediate deployment of the models. By integrating an AI co-writer into your application, you can empower your users with enhanced features. Capabilities such as paraphrasing, long-form draft creation, content repurposing, and tailored auto-complete options can significantly boost user engagement, paving the way for your success and growth in the industry. Ultimately, our tools are designed to streamline your workflows and elevate the overall user experience.

Palmyra is a sophisticated suite of Large Language Models (LLMs) meticulously crafted to provide precise and dependable results within various business environments. These models excel in a range of functions, such as responding to inquiries, interpreting images, and accommodating over 30 languages, while also offering fine-tuning options tailored to industries like healthcare and finance. Notably, Palmyra models have achieved leading rankings in respected evaluations, including Stanford HELM and PubMedQA, with Palmyra-Fin making history as the first model to pass the CFA Level III examination successfully. Writer prioritizes data privacy by not using client information for training or model modifications, adhering strictly to a zero data retention policy. The Palmyra lineup includes specialized models like Palmyra X 004, equipped with tool-calling capabilities; Palmyra Med, designed for the healthcare sector; Palmyra Fin, tailored for financial tasks; and Palmyra Vision, which specializes in advanced image and video analysis. Additionally, these cutting-edge models are available through Writer's extensive generative AI platform, which integrates graph-based Retrieval Augmented Generation (RAG) to enhance their performance. As Palmyra continues to evolve through ongoing enhancements, it strives to transform the realm of enterprise-level AI solutions, ensuring that businesses can leverage the latest technological advancements effectively. The commitment to innovation positions Palmyra as a leader in the AI landscape, facilitating better decision-making and operational efficiency across various sectors.

Tencent has introduced the Hunyuan T1, a sophisticated AI model now available to users through the Tencent Yuanbao platform. This model excels in understanding multiple dimensions and potential logical relationships, making it well-suited for addressing complex problems. Users can also explore a variety of AI models on the platform, such as DeepSeek-R1 and Tencent Hunyuan Turbo. Excitement is growing for the upcoming official release of the Tencent Hunyuan T1 model, which promises to offer external API access along with enhanced services. Built on the robust foundation of Tencent's Hunyuan large language model, Yuanbao is particularly noted for its capabilities in Chinese language understanding, logical reasoning, and efficient task execution. It improves user interaction by offering AI-driven search functionalities, document summaries, and writing assistance, thereby facilitating thorough document analysis and stimulating prompt-based conversations. This diverse range of features is likely to appeal to many users searching for cutting-edge solutions, enhancing the overall user engagement on the platform. As the demand for innovative AI tools continues to rise, Yuanbao aims to position itself as a leading resource in the field.

NVIDIA has developed the Nemotron series of open-source models designed to generate synthetic data for the training of large language models (LLMs) for commercial applications. Notably, the Nemotron-4 340B model is a significant breakthrough, offering developers a powerful tool to create high-quality data and enabling them to filter this data based on various attributes using a reward model. This innovation not only improves the data generation process but also optimizes the training of LLMs, catering to specific requirements and increasing efficiency. As a result, developers can more effectively harness the potential of synthetic data to enhance their language models.

A sophisticated End-to-End MLLM has been developed to accommodate various types of references and effectively ground its responses. The Ferret Model employs a unique combination of Hybrid Region Representation and a Spatial-aware Visual Sampler, which facilitates detailed and adaptable referring and grounding functions within the MLLM framework. Serving as a foundational element, the GRIT Dataset consists of about 1.1 million entries, specifically designed as a large-scale and hierarchical dataset aimed at enhancing instruction tuning in the ground-and-refer domain. Moreover, the Ferret-Bench acts as a thorough multimodal evaluation benchmark that concurrently measures referring, grounding, semantics, knowledge, and reasoning, thus providing a comprehensive assessment of the model's performance. This elaborate configuration is intended to improve the synergy between language and visual information, which could lead to more intuitive AI systems that better understand and interact with users. Ultimately, advancements in these models may significantly transform how we engage with technology in our daily lives.

Scale AI is thrilled to unveil Defense Llama, a dedicated Large Language Model developed from Meta’s Llama 3, specifically designed to bolster initiatives aimed at enhancing American national security. This innovative model is intended for use exclusively within secure U.S. government environments through Scale Donovan, empowering military personnel and national security specialists with the generative AI capabilities necessary for a variety of tasks, such as strategizing military operations and assessing potential adversary vulnerabilities. Underpinned by a diverse range of training materials, including military protocols and international humanitarian regulations, Defense Llama operates in accordance with the Department of Defense (DoD) guidelines concerning armed conflict and complies with the DoD's Ethical Principles for Artificial Intelligence. This well-structured foundation not only enables the model to provide accurate and relevant insights tailored to user requirements but also ensures that its output is sensitive to the complexities of defense-related scenarios. By offering a secure and effective generative AI platform, Scale is dedicated to augmenting the effectiveness of U.S. defense personnel in their essential missions, paving the way for innovative solutions to national security challenges. The deployment of such advanced technology signals a notable leap forward in achieving strategic objectives in the realm of national defense.

The Gemini 1.5 Pro AI model stands as a leading achievement in the realm of language modeling, crafted to deliver incredibly accurate, context-aware, and human-like responses that are suitable for numerous applications. Its cutting-edge neural architecture empowers it to excel in a variety of tasks related to natural language understanding, generation, and logical reasoning. This model has been carefully optimized for versatility, enabling it to tackle a wide array of functions such as content creation, software development, data analysis, and complex problem-solving. With its advanced algorithms, it possesses a profound grasp of language, facilitating smooth transitions across different fields and conversational styles. Emphasizing both scalability and efficiency, the Gemini 1.5 Pro is structured to meet the needs of both small projects and large enterprise implementations, positioning itself as an essential tool for boosting productivity and encouraging innovation. Additionally, its capacity to learn from user interactions significantly improves its effectiveness, rendering it even more efficient in practical applications. This continuous enhancement ensures that the model remains relevant and useful in an ever-evolving technological landscape.