Top Llama 2 Alternatives

Amazon Titan is a suite of advanced foundation models from AWS, specifically designed to enhance generative AI applications with remarkable performance and flexibility. Drawing on over 25 years of AWS's deep-rooted knowledge in artificial intelligence and machine learning, Titan models support a diverse range of functions, such as text generation, summarization, semantic search, and image creation. These models emphasize the importance of responsible AI by incorporating safety features and fine-tuning options. Moreover, they facilitate customization through Retrieval Augmented Generation (RAG), which improves accuracy and relevance, making them ideal for both general and niche AI applications. The innovative architecture and powerful functionalities of Titan models mark a noteworthy progression in the realm of artificial intelligence, paving the way for more sophisticated AI solutions. Their ability to adapt to user-specific needs further underscores their significance in various industries.

The TinyLlama project aims to pretrain a Llama model featuring 1.1 billion parameters, leveraging a vast dataset of 3 trillion tokens. With effective optimizations, this challenging endeavor can be accomplished in only 90 days, making use of 16 A100-40G GPUs for processing power. By preserving the same architecture and tokenizer as Llama 2, we ensure that TinyLlama remains compatible with a range of open-source projects built upon Llama. Moreover, the model's streamlined architecture, with its 1.1 billion parameters, renders it ideal for various applications that demand minimal computational power and memory. This adaptability allows developers to effortlessly incorporate TinyLlama into their current systems and processes, fostering innovation in resource-constrained environments. As a result, TinyLlama not only enhances accessibility but also encourages experimentation in the field of machine learning.

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.

Leverage generative language models to enhance and streamline your web services and applications effectively. You can obtain a unified summary of various textual data sources, including workplace chat conversations, customer feedback, or additional content, with the assistance of YandexGPT, which excels in synthesizing and analyzing information. Elevate the quality and presentation of your written materials to accelerate the content generation process, allowing for the creation of templates suitable for newsletters, product descriptions for e-commerce platforms, and other relevant applications. Develop a customer service chatbot capable of handling both routine inquiries and more intricate questions by training it accordingly. Utilize the API to automate workflows and seamlessly integrate this service into your existing applications, thereby enhancing operational efficiency. By implementing these strategies, you can significantly improve user engagement and satisfaction across your digital platforms.

The Mixtral 8x7B model represents a cutting-edge sparse mixture of experts (SMoE) architecture that features open weights and is made available under the Apache 2.0 license. This innovative model outperforms Llama 2 70B across a range of benchmarks, while also achieving inference speeds that are sixfold faster. As the premier open-weight model with a versatile licensing structure, Mixtral stands out for its impressive cost-effectiveness and performance metrics. Furthermore, it competes with and frequently exceeds the capabilities of GPT-3.5 in many established benchmarks, underscoring its importance in the AI landscape. Its unique blend of accessibility, rapid processing, and overall effectiveness positions it as an attractive option for developers in search of top-tier AI solutions. Consequently, the Mixtral model not only enhances the current technological landscape but also paves the way for future advancements in AI development.

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.

Gemma encompasses a series of innovative, lightweight open models inspired by the foundational research and technology that drive the Gemini models. Developed by Google DeepMind in collaboration with various teams at Google, the term "gemma" derives from Latin, meaning "precious stone." Alongside the release of our model weights, we are also providing resources designed to foster developer creativity, promote collaboration, and uphold ethical standards in the use of Gemma models. Sharing essential technical and infrastructural components with Gemini, our leading AI model available today, the 2B and 7B versions of Gemma demonstrate exceptional performance in their weight classes relative to other open models. Notably, these models are capable of running seamlessly on a developer's laptop or desktop, showcasing their adaptability. Moreover, Gemma has proven to not only surpass much larger models on key performance benchmarks but also adhere to our rigorous standards for producing safe and responsible outputs, thereby serving as an invaluable tool for developers seeking to leverage advanced AI capabilities. As such, Gemma represents a significant advancement in accessible AI technology.

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.

We have integrated Llama 3 into Meta AI, our smart assistant that transforms the way people perform tasks, innovate, and interact with technology. By leveraging Meta AI for coding and troubleshooting, users can directly experience the power of Llama 3. Whether you are developing agents or other AI-based solutions, Llama 3, which is offered in both 8B and 70B variants, delivers the essential features and adaptability needed to turn your concepts into reality. In conjunction with the launch of Llama 3, we have updated our Responsible Use Guide (RUG) to provide comprehensive recommendations on the ethical development of large language models. Our approach focuses on enhancing trust and safety measures, including the introduction of Llama Guard 2, which aligns with the newly established taxonomy from MLCommons and expands its coverage to include a broader range of safety categories, alongside code shield and Cybersec Eval 2. Moreover, these improvements are designed to promote a safer and more responsible application of AI technologies across different fields, ensuring that users can confidently harness these innovations. The commitment to ethical standards reflects our dedication to fostering a secure and trustworthy AI environment.

Grok is an innovative artificial intelligence that draws inspiration from the Hitchhiker’s Guide to the Galaxy, designed to handle a diverse range of questions while also encouraging users to think critically through stimulating inquiries. Its talent for providing responses that incorporate humor and a touch of irreverence makes Grok unsuitable for individuals who prefer a more serious tone in their interactions. A notable characteristic of Grok is its ability to access live data via the 𝕏 platform, enabling it to address daring and unconventional queries that other AI systems may avoid. This feature not only broadens its adaptability but also guarantees that users receive answers that are both immediate and captivating. As a result, Grok stands out as a unique option for those seeking a blend of entertainment and information in their AI interactions.

DeepSeek emerges as a cutting-edge AI assistant, utilizing the advanced DeepSeek-V3 model, which features a remarkable 600 billion parameters for enhanced performance. Designed to compete with the top AI systems worldwide, it provides quick responses and a wide range of functionalities that streamline everyday tasks. Available across multiple platforms such as iOS, Android, and the web, DeepSeek ensures that users can access its services from nearly any location. The application supports various languages and is regularly updated to improve its features, add new language options, and resolve any issues. Celebrated for its seamless performance and versatility, DeepSeek has garnered positive feedback from a varied global audience. Moreover, its dedication to user satisfaction and ongoing enhancements positions it as a leader in the AI technology landscape, making it a trusted tool for many. With a focus on innovation, DeepSeek continually strives to refine its offerings to meet evolving user needs.

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.

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.

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.

Qwen-7B represents the seventh iteration in Alibaba Cloud's Qwen language model lineup, also referred to as Tongyi Qianwen, featuring 7 billion parameters. This advanced language model employs a Transformer architecture and has undergone pretraining on a vast array of data, including web content, literature, programming code, and more. In addition, we have launched Qwen-7B-Chat, an AI assistant that enhances the pretrained Qwen-7B model by integrating sophisticated alignment techniques. The Qwen-7B series includes several remarkable attributes: Its training was conducted on a premium dataset encompassing over 2.2 trillion tokens collected from a custom assembly of high-quality texts and codes across diverse fields, covering both general and specialized areas of knowledge. Moreover, the model excels in performance, outshining similarly-sized competitors on various benchmark datasets that evaluate skills in natural language comprehension, mathematical reasoning, and programming challenges. This establishes Qwen-7B as a prominent contender in the AI language model landscape. In summary, its intricate training regimen and solid architecture contribute significantly to its outstanding adaptability and efficiency in a wide range of applications.

The Qwen LLM, developed by Alibaba Cloud's Damo Academy, is an innovative suite of large language models that utilize a vast array of text and code to generate text that closely mimics human language, assist in language translation, create diverse types of creative content, and deliver informative responses to a variety of questions. Notable features of the Qwen LLMs are: A diverse range of model sizes: The Qwen series includes models with parameter counts ranging from 1.8 billion to 72 billion, which allows for a variety of performance levels and applications to be addressed. Open source options: Some versions of Qwen are available as open source, which provides users the opportunity to access and modify the source code to suit their needs. Multilingual proficiency: Qwen models are capable of understanding and translating multiple languages, such as English, Chinese, and French. Wide-ranging functionalities: Beyond generating text and translating languages, Qwen models are adept at answering questions, summarizing information, and even generating programming code, making them versatile tools for many different scenarios. In summary, the Qwen LLM family is distinguished by its broad capabilities and adaptability, making it an invaluable resource for users with varying needs. As technology continues to advance, the potential applications for Qwen LLMs are likely to expand even further, enhancing their utility in numerous fields.

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.

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.

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.

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.

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.

Code Llama is a sophisticated language model engineered to produce code from text prompts, setting itself apart as a premier choice among publicly available models for coding applications. This groundbreaking model not only enhances productivity for seasoned developers but also supports newcomers in tackling the complexities of learning programming. Its adaptability allows Code Llama to serve as both an effective productivity tool and a pedagogical resource, enabling programmers to develop more efficient and well-documented software. Furthermore, users can generate code alongside natural language explanations by inputting either format, which contributes to its flexibility for various programming tasks. Offered for free for both research and commercial use, Code Llama is based on the Llama 2 architecture and is available in three specific versions: the core Code Llama model, Code Llama - Python designed exclusively for Python development, and Code Llama - Instruct, which is fine-tuned to understand and execute natural language commands accurately. As a result, Code Llama stands out not just for its technical capabilities but also for its accessibility and relevance to diverse coding scenarios.

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.

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.

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.

Baichuan-13B is a powerful language model featuring 13 billion parameters, created by Baichuan Intelligent as both an open-source and commercially accessible option, and it builds on the previous Baichuan-7B model. This new iteration has excelled in key benchmarks for both Chinese and English, surpassing other similarly sized models in performance. It offers two different pre-training configurations: Baichuan-13B-Base and Baichuan-13B-Chat. Significantly, Baichuan-13B increases its parameter count to 13 billion, utilizing the groundwork established by Baichuan-7B, and has been trained on an impressive 1.4 trillion tokens sourced from high-quality datasets, achieving a 40% increase in training data compared to LLaMA-13B. It stands out as the most comprehensively trained open-source model within the 13B parameter range. Furthermore, it is designed to be bilingual, supporting both Chinese and English, employs ALiBi positional encoding, and features a context window size of 4096 tokens, which provides it with the flexibility needed for a wide range of natural language processing tasks. This model's advancements mark a significant step forward in the capabilities of large language models.

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.

OLMo 2 is a suite of fully open language models developed by the Allen Institute for AI (AI2), designed to provide researchers and developers with straightforward access to training datasets, open-source code, reproducible training methods, and extensive evaluations. These models are trained on a remarkable dataset consisting of up to 5 trillion tokens and are competitive with leading open-weight models such as Llama 3.1, especially in English academic assessments. A significant emphasis of OLMo 2 lies in maintaining training stability, utilizing techniques to reduce loss spikes during prolonged training sessions, and implementing staged training interventions to address capability weaknesses in the later phases of pretraining. Furthermore, the models incorporate advanced post-training methodologies inspired by AI2's Tülu 3, resulting in the creation of OLMo 2-Instruct models. To support continuous enhancements during the development lifecycle, an actionable evaluation framework called the Open Language Modeling Evaluation System (OLMES) has been established, featuring 20 benchmarks that assess vital capabilities. This thorough methodology not only promotes transparency but also actively encourages improvements in the performance of language models, ensuring they remain at the forefront of AI advancements. Ultimately, OLMo 2 aims to empower the research community by providing resources that foster innovation and collaboration in language modeling.

Llama, a leading-edge foundational large language model developed by Meta AI, is designed to assist researchers in expanding the frontiers of artificial intelligence research. By offering streamlined yet powerful models like Llama, even those with limited resources can access advanced tools, thereby enhancing inclusivity in this fast-paced and ever-evolving field. The development of more compact foundational models, such as Llama, proves beneficial in the realm of large language models since they require considerably less computational power and resources, which allows for the exploration of novel approaches, validation of existing studies, and examination of potential new applications. These models harness vast amounts of unlabeled data, rendering them particularly effective for fine-tuning across diverse tasks. We are introducing Llama in various sizes, including 7B, 13B, 33B, and 65B parameters, each supported by a comprehensive model card that details our development methodology while maintaining our dedication to Responsible AI practices. By providing these resources, we seek to empower a wider array of researchers to actively participate in and drive forward the developments in the field of AI. Ultimately, our goal is to foster an environment where innovation thrives and collaboration flourishes.

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.

We are thrilled to introduce MPT-7B, the latest model in the MosaicML Foundation Series. This transformer model has been carefully developed from scratch, utilizing 1 trillion tokens of varied text and code during its training. It is accessible as open-source software, making it suitable for commercial use and achieving performance levels comparable to LLaMA-7B. The entire training process was completed in just 9.5 days on the MosaicML platform, with no human intervention, and incurred an estimated cost of $200,000. With MPT-7B, users can train, customize, and deploy their own versions of MPT models, whether they opt to start from one of our existing checkpoints or initiate a new project. Additionally, we are excited to unveil three specialized variants alongside the core MPT-7B: MPT-7B-Instruct, MPT-7B-Chat, and MPT-7B-StoryWriter-65k+, with the latter featuring an exceptional context length of 65,000 tokens for generating extensive content. These new offerings greatly expand the horizons for developers and researchers eager to harness the capabilities of transformer models in their innovative initiatives. Furthermore, the flexibility and scalability of MPT-7B are designed to cater to a wide range of application needs, fostering creativity and efficiency in developing advanced AI solutions.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Large language models, which often demand significant computational power and prolonged training periods, have shown remarkable abilities in performing zero- and few-shot learning tasks. The substantial resources required for their creation make it quite difficult for many researchers to replicate these models. Moreover, access to the limited number of models available through APIs is restricted, as users are unable to acquire the full model weights, which hinders academic research. To address these issues, we present Open Pre-trained Transformers (OPT), a series of decoder-only pre-trained transformers that vary in size from 125 million to 175 billion parameters, which we aim to share fully and responsibly with interested researchers. Our research reveals that OPT-175B achieves performance levels comparable to GPT-3, while consuming only one-seventh of the carbon emissions needed for GPT-3's training process. In addition to this, we plan to offer a comprehensive logbook detailing the infrastructural challenges we faced during the project, along with code to aid experimentation with all released models, ensuring that scholars have the necessary resources to further investigate this technology. This initiative not only democratizes access to advanced models but also encourages sustainable practices in the field of artificial intelligence.

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.

OpenLLaMA is a freely available version of Meta AI's LLaMA 7B, crafted using the RedPajama dataset. The model weights provided can easily substitute the LLaMA 7B in existing applications. Furthermore, we have also developed a streamlined 3B variant of the LLaMA model, catering to users who prefer a more compact option. This initiative enhances user flexibility by allowing them to select the most suitable model according to their particular requirements, thus accommodating a wider range of applications and use cases.

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.

Developing advanced language models poses considerable hurdles, requiring immense computational power, sophisticated distributed computing methods, and a deep understanding of machine learning. As a result, only a select few organizations undertake the complex endeavor of creating large language models (LLMs) independently. Additionally, many entities equipped with the requisite expertise and resources have started to limit the accessibility of their discoveries, reflecting a significant change from the more open practices observed in recent months. At Cerebras, we prioritize the importance of open access to leading-edge models, which is why we proudly introduce Cerebras-GPT to the open-source community. This initiative features a lineup of seven GPT models, with parameter sizes varying from 111 million to 13 billion. By employing the Chinchilla training formula, these models achieve remarkable accuracy while maintaining computational efficiency. Importantly, Cerebras-GPT is designed to offer faster training times, lower costs, and reduced energy use compared to any other model currently available to the public. Through the release of these models, we aspire to encourage further innovation and foster collaborative efforts within the machine learning community, ultimately pushing the boundaries of what is possible in this rapidly evolving field.

Foundation models, such as GPT-4, have propelled the field of artificial intelligence forward at an unprecedented pace; however, the most sophisticated models continue to be either restricted or only partially available to the public. To counteract this issue, the RedPajama initiative is focused on creating a suite of high-quality, completely open-source models. We are excited to share that we have successfully finished the first stage of this project: the recreation of the LLaMA training dataset, which encompasses over 1.2 trillion tokens. At present, a significant portion of leading foundation models is confined within commercial APIs, which limits opportunities for research and customization, especially when dealing with sensitive data. The pursuit of fully open-source models may offer a viable remedy to these constraints, on the condition that the open-source community can enhance the quality of these models to compete with their closed counterparts. Recent developments have indicated that there is encouraging progress in this domain, hinting that the AI sector may be on the brink of a revolutionary shift similar to what was seen with the introduction of Linux. The success of Stable Diffusion highlights that open-source alternatives can not only compete with high-end commercial products like DALL-E but also foster extraordinary creativity through the collaborative input of various communities. By nurturing a thriving open-source ecosystem, we can pave the way for new avenues of innovation and ensure that access to state-of-the-art AI technology is more widely available, ultimately democratizing the capabilities of artificial intelligence for all users.

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.

We are excited to introduce DBRX, a highly adaptable open LLM created by Databricks. This cutting-edge model sets a new standard for open LLMs by achieving remarkable performance across a wide range of established benchmarks. It offers both open-source developers and businesses the advanced features that were traditionally limited to proprietary model APIs; our assessments show that it surpasses GPT-3.5 and stands strong against Gemini 1.0 Pro. Furthermore, DBRX shines as a coding model, outperforming dedicated systems like CodeLLaMA-70B in various programming tasks, while also proving its capability as a general-purpose LLM. The exceptional quality of DBRX is further enhanced by notable improvements in training and inference efficiency. With its sophisticated fine-grained mixture-of-experts (MoE) architecture, DBRX pushes the efficiency of open models to unprecedented levels. In terms of inference speed, it can achieve performance that is twice as fast as LLaMA2-70B, and its total and active parameter counts are around 40% of those found in Grok-1, illustrating its compact structure without sacrificing performance. This unique blend of velocity and size positions DBRX as a transformative force in the realm of open AI models, promising to reshape expectations in the industry. As it continues to evolve, the potential applications for DBRX in various sectors are vast and exciting.

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.

Open R1 is a community-driven, open-source project aimed at replicating the advanced AI capabilities of DeepSeek-R1 through transparent and accessible methodologies. Participants can delve into the Open R1 AI model or engage in a complimentary online conversation with DeepSeek R1 through the Open R1 platform. This project provides a meticulous implementation of DeepSeek-R1's reasoning-optimized training framework, including tools for GRPO training, SFT fine-tuning, and synthetic data generation, all released under the MIT license. While the foundational training dataset remains proprietary, Open R1 empowers users with an extensive array of resources to build and refine their own AI models, fostering increased customization and exploration within the realm of artificial intelligence. Furthermore, this collaborative environment encourages innovation and shared knowledge, paving the way for advancements in AI technology.