Alternatives to PygmalionAI

LM-Kit.NET is an enterprise-grade toolkit designed for seamlessly integrating generative AI into your .NET applications, fully supporting Windows, Linux, and macOS. Empower your C# and VB.NET projects with a flexible platform that simplifies the creation and orchestration of dynamic AI agents. Leverage efficient Small Language Models for on‑device inference, reducing computational load, minimizing latency, and enhancing security by processing data locally. Experience the power of Retrieval‑Augmented Generation (RAG) to boost accuracy and relevance, while advanced AI agents simplify complex workflows and accelerate development. Native SDKs ensure smooth integration and high performance across diverse platforms. With robust support for custom AI agent development and multi‑agent orchestration, LM‑Kit.NET streamlines prototyping, deployment, and scalability—enabling you to build smarter, faster, and more secure solutions trusted by professionals worldwide.

GPT-J represents an advanced language model developed by EleutherAI, known for its impressive capabilities. When it comes to performance, GPT-J showcases a proficiency that rivals OpenAI's well-known GPT-3 in various zero-shot tasks. Remarkably, it has even outperformed GPT-3 in specific areas, such as code generation. The most recent version of this model, called GPT-J-6B, is constructed using a comprehensive linguistic dataset known as The Pile, which is publicly accessible and consists of an extensive 825 gibibytes of language data divided into 22 unique subsets. Although GPT-J possesses similarities to ChatGPT, it's crucial to highlight that it is primarily intended for text prediction rather than functioning as a chatbot. In a notable advancement in March 2023, Databricks unveiled Dolly, a model that is capable of following instructions and operates under an Apache license, further enriching the landscape of language models. This evolution in AI technology continues to push the boundaries of what is possible in natural language processing.

Stable LM represents a significant advancement in the field of language models by leveraging our previous experience with open-source initiatives, particularly in collaboration with EleutherAI, a nonprofit research organization. This journey includes the development of notable models such as GPT-J, GPT-NeoX, and the Pythia suite, all of which were trained on The Pile open-source dataset, while many contemporary open-source models like Cerebras-GPT and Dolly-2 have drawn inspiration from this foundational work. Unlike its predecessors, Stable LM is trained on an innovative dataset that is three times the size of The Pile, encompassing a staggering 1.5 trillion tokens. We plan to share more information about this dataset in the near future. The extensive nature of this dataset enables Stable LM to excel remarkably in both conversational and coding scenarios, despite its relatively modest size of 3 to 7 billion parameters when compared to larger models like GPT-3, which boasts 175 billion parameters. Designed for versatility, Stable LM 3B is a streamlined model that can efficiently function on portable devices such as laptops and handheld gadgets, making us enthusiastic about its practical applications and mobility. Overall, the development of Stable LM marks a pivotal step towards creating more efficient and accessible language models for a wider audience.

OpenLLaMA is an openly licensed reproduction of Meta AI's LLaMA 7B, developed using the RedPajama dataset. The model weights we offer can seamlessly replace the LLaMA 7B in current applications. Additionally, we have created a more compact 3B version of the LLaMA model for those seeking a lighter alternative. This provides users with more flexibility in choosing the right model for their specific needs.

Introducing the next iteration of our open-source large language model, this version features model weights along with initial code for the pretrained and fine-tuned Llama language models, which span from 7 billion to 70 billion parameters. The Llama 2 pretrained models have been developed using an impressive 2 trillion tokens and offer double the context length compared to their predecessor, Llama 1. Furthermore, the fine-tuned models have been enhanced through the analysis of over 1 million human annotations. Llama 2 demonstrates superior performance against various other open-source language models across multiple external benchmarks, excelling in areas such as reasoning, coding capabilities, proficiency, and knowledge assessments. For its training, Llama 2 utilized publicly accessible online data sources, while the fine-tuned variant, Llama-2-chat, incorporates publicly available instruction datasets along with the aforementioned extensive human annotations. Our initiative enjoys strong support from a diverse array of global stakeholders who are enthusiastic about our open approach to AI, including companies that have provided valuable early feedback and are eager to collaborate using Llama 2. The excitement surrounding Llama 2 signifies a pivotal shift in how AI can be developed and utilized collectively.

GPT4All represents a comprehensive framework designed for the training and deployment of advanced, tailored large language models that can operate efficiently on standard consumer-grade CPUs. Its primary objective is straightforward: to establish itself as the leading instruction-tuned assistant language model that individuals and businesses can access, share, and develop upon without restrictions. Each GPT4All model ranges between 3GB and 8GB in size, making it easy for users to download and integrate into the GPT4All open-source software ecosystem. Nomic AI plays a crucial role in maintaining and supporting this ecosystem, ensuring both quality and security while promoting the accessibility for anyone, whether individuals or enterprises, to train and deploy their own edge-based language models. The significance of data cannot be overstated, as it is a vital component in constructing a robust, general-purpose large language model. To facilitate this, the GPT4All community has established an open-source data lake, which serves as a collaborative platform for contributing valuable instruction and assistant tuning data, thereby enhancing future training efforts for models within the GPT4All framework. This initiative not only fosters innovation but also empowers users to engage actively in the development process.

Push the limits of individual alignment, artificial consciousness, open-source software, and decentralization through experimentation that larger corporations and governments often shy away from. Hermes 3 features sophisticated long-term context retention, the ability to engage in multi-turn conversations, and intricate roleplaying and internal monologue capabilities, alongside improved functionality for agentic function-calling. The design of this model emphasizes precise adherence to system prompts and instruction sets in a flexible way. By fine-tuning Llama 3.1 across various scales, including 8B, 70B, and 405B, and utilizing a dataset largely composed of synthetically generated inputs, Hermes 3 showcases performance that rivals and even surpasses Llama 3.1, while also unlocking greater potential in reasoning and creative tasks. This series of instructive and tool-utilizing models exhibits exceptional reasoning and imaginative skills, paving the way for innovative applications. Ultimately, Hermes 3 represents a significant advancement in the landscape of AI development.

ChatGPT by OpenAI is a versatile AI conversational platform that provides assistance in writing, learning, brainstorming, code generation, and problem-solving across a wide range of topics. Available for free with optional Plus and Pro subscription plans, it supports real-time text and voice interactions on web browsers and mobile apps. Users can leverage ChatGPT to create content, summarize meetings, debug code, analyze data, and even generate images using integrated tools like DALL·E 3. The platform is accessible via desktop and mobile devices and offers personalized workflows through custom GPTs and projects. Advanced plans unlock deeper research capabilities, extended limits, and access to cutting-edge AI models like GPT-4o and OpenAI o1 pro mode. ChatGPT integrates search capabilities for real-time information and enables collaboration through features like Canvas for project editing. It caters to students, professionals, hobbyists, and developers seeking efficient, AI-driven support. OpenAI continually updates ChatGPT with new tools and enhanced usability.

Claude Pro is a sophisticated large language model created to tackle intricate tasks while embodying a warm and approachable attitude. With a foundation built on comprehensive, high-quality information, it shines in grasping context, discerning subtle distinctions, and generating well-organized, coherent replies across various subjects. By utilizing its strong reasoning abilities and an enhanced knowledge repository, Claude Pro is capable of crafting in-depth reports, generating creative pieces, condensing extensive texts, and even aiding in programming endeavors. Its evolving algorithms consistently enhance its capacity to absorb feedback, ensuring that the information it provides remains precise, dependable, and beneficial. Whether catering to professionals seeking specialized assistance or individuals needing quick, insightful responses, Claude Pro offers a dynamic and efficient conversational encounter, making it a valuable tool for anyone in need of information or support.

Vicuna-13B is an open-source conversational agent developed through the fine-tuning of LLaMA, utilizing a dataset of user-shared dialogues gathered from ShareGPT. Initial assessments, with GPT-4 serving as an evaluator, indicate that Vicuna-13B achieves over 90% of the quality exhibited by OpenAI's ChatGPT and Google Bard, and it surpasses other models such as LLaMA and Stanford Alpaca in more than 90% of instances. The entire training process for Vicuna-13B incurs an estimated expenditure of approximately $300. Additionally, the source code and model weights, along with an interactive demonstration, are made available for public access under non-commercial terms, fostering a collaborative environment for further development and exploration. This openness encourages innovation and enables users to experiment with the model's capabilities in diverse applications.

As artificial intelligence continues to evolve, its ability to tackle more intricate and vital challenges will expand, necessitating a greater computational power to support these advancements. The ChatGPT Pro subscription, priced at $200 per month, offers extensive access to OpenAI's premier models and tools, including unrestricted use of the advanced OpenAI o1 model, o1-mini, GPT-4o, and Advanced Voice features. This subscription also grants users access to the o1 pro mode, an enhanced version of o1 that utilizes increased computational resources to deliver superior answers to more challenging inquiries. Looking ahead, we anticipate the introduction of even more robust, resource-demanding productivity tools within this subscription plan. With ChatGPT Pro, users benefit from a variant of our most sophisticated model capable of extended reasoning, yielding the most dependable responses. External expert evaluations have shown that o1 pro mode consistently generates more accurate and thorough responses, particularly excelling in fields such as data science, programming, and legal case analysis, thereby solidifying its value for professional use. In addition, the commitment to ongoing improvements ensures that subscribers will receive continual updates that enhance their experience and capabilities.

Tülu 3 is a cutting-edge language model created by the Allen Institute for AI (Ai2) that aims to improve proficiency in fields like knowledge, reasoning, mathematics, coding, and safety. It is based on the Llama 3 Base and undergoes a detailed four-stage post-training regimen: careful prompt curation and synthesis, supervised fine-tuning on a wide array of prompts and completions, preference tuning utilizing both off- and on-policy data, and a unique reinforcement learning strategy that enhances targeted skills through measurable rewards. Notably, this open-source model sets itself apart by ensuring complete transparency, offering access to its training data, code, and evaluation tools, thus bridging the performance divide between open and proprietary fine-tuning techniques. Performance assessments reveal that Tülu 3 surpasses other models with comparable sizes, like Llama 3.1-Instruct and Qwen2.5-Instruct, across an array of benchmarks, highlighting its effectiveness. The continuous development of Tülu 3 signifies the commitment to advancing AI capabilities while promoting an open and accessible approach to technology.

Instruction-following models like GPT-3.5 (text-DaVinci-003), ChatGPT, Claude, and Bing Chat have seen significant advancements in their capabilities, leading to a rise in their usage among individuals in both personal and professional contexts. Despite their growing popularity and integration into daily tasks, these models are not without their shortcomings, as they can sometimes disseminate inaccurate information, reinforce harmful stereotypes, and use inappropriate language. To effectively tackle these critical issues, it is essential for researchers and scholars to become actively involved in exploring these models further. However, conducting research on instruction-following models within academic settings has posed challenges due to the unavailability of models with comparable functionality to proprietary options like OpenAI’s text-DaVinci-003. In response to this gap, we are presenting our insights on an instruction-following language model named Alpaca, which has been fine-tuned from Meta’s LLaMA 7B model, aiming to contribute to the discourse and development in this field. This initiative represents a step towards enhancing the understanding and capabilities of instruction-following models in a more accessible manner for researchers.

Our latest flagship offering is a compact Language Model (SLM) that harnesses the capabilities and efficiency of top-tier closed-source LLMs. It excels in a variety of generalized tasks, adapts well to instructions, and aligns with human preferences. With its impressive 70B parameters, it stands out as the leading model available. SuperNova serves as a versatile tool for a wide range of generalized applications, comparable to OpenAI’s GPT-4o, Claude Sonnet 3.5, and Cohere. Utilizing cutting-edge learning and optimization methods, SuperNova produces remarkably precise responses that mimic human conversation. It is recognized as the most adaptable, secure, and budget-friendly language model in the industry, allowing clients to reduce total deployment expenses by as much as 95% compared to traditional closed-source alternatives. SuperNova can be seamlessly integrated into applications and products, used for general chat interactions, and tailored to various scenarios. Additionally, by consistently updating your models with the latest open-source advancements, you can avoid being tied to a single solution. Safeguarding your information is paramount, thanks to our top-tier privacy protocols. Ultimately, SuperNova represents a significant advancement in making powerful AI tools accessible for diverse needs.

Aya represents a cutting-edge, open-source generative language model that boasts support for 101 languages, significantly surpassing the language capabilities of current open-source counterparts. By facilitating access to advanced language processing for a diverse array of languages and cultures that are often overlooked, Aya empowers researchers to explore the full potential of generative language models. In addition to the Aya model, we are releasing the largest dataset for multilingual instruction fine-tuning ever created, which includes 513 million entries across 114 languages. This extensive dataset features unique annotations provided by native and fluent speakers worldwide, thereby enhancing the ability of AI to cater to a wide range of global communities that have historically had limited access to such technology. Furthermore, the initiative aims to bridge the gap in AI accessibility, ensuring that even the most underserved languages receive the attention they deserve in the digital landscape.

Falcon 2 11B is a versatile AI model that is open-source, supports multiple languages, and incorporates multimodal features, particularly excelling in vision-to-language tasks. It outperforms Meta’s Llama 3 8B and matches the capabilities of Google’s Gemma 7B, as validated by the Hugging Face Leaderboard. In the future, the development plan includes adopting a 'Mixture of Experts' strategy aimed at significantly improving the model's functionalities, thereby advancing the frontiers of AI technology even further. This evolution promises to deliver remarkable innovations, solidifying Falcon 2's position in the competitive landscape of artificial intelligence.

Falcon Mamba 7B marks a significant milestone as the inaugural open-source State Space Language Model (SSLM), presenting a revolutionary architecture within the Falcon model family. Celebrated as the premier open-source SSLM globally by Hugging Face, it establishes a new standard for efficiency in artificial intelligence. In contrast to conventional transformers, SSLMs require significantly less memory and can produce lengthy text sequences seamlessly without extra resource demands. Falcon Mamba 7B outperforms top transformer models, such as Meta’s Llama 3.1 8B and Mistral’s 7B, demonstrating enhanced capabilities. This breakthrough not only highlights Abu Dhabi’s dedication to pushing the boundaries of AI research but also positions the region as a pivotal player in the global AI landscape. Such advancements are vital for fostering innovation and collaboration in technology.

This repository showcases the research preview of LongLLaMA, an advanced large language model that can manage extensive contexts of up to 256,000 tokens or potentially more. LongLLaMA is developed on the OpenLLaMA framework and has been fine-tuned utilizing the Focused Transformer (FoT) technique. The underlying code for LongLLaMA is derived from Code Llama. We are releasing a smaller 3B base variant of the LongLLaMA model, which is not instruction-tuned, under an open license (Apache 2.0), along with inference code that accommodates longer contexts available on Hugging Face. This model's weights can seamlessly replace LLaMA in existing systems designed for shorter contexts, specifically those handling up to 2048 tokens. Furthermore, we include evaluation results along with comparisons to the original OpenLLaMA models, thereby providing a comprehensive overview of LongLLaMA's capabilities in the realm of long-context processing.

We offer fast and precise AI models optimized for deployment in production environments. Our inference API is designed for high availability, utilizing cutting-edge NVIDIA GPUs to ensure optimal performance. We have curated a selection of top open-source natural language processing (NLP) models from the community, making them readily available for your use. You have the flexibility to fine-tune your own models, including GPT-J, or upload your proprietary models for seamless deployment in production. From your user-friendly dashboard, you can easily upload or train/fine-tune AI models, allowing you to integrate them into production immediately without the hassle of managing deployment factors such as memory usage, availability, or scalability. Moreover, you can upload an unlimited number of models and deploy them as needed, ensuring that you can continuously innovate and adapt to your evolving requirements. This provides a robust framework for leveraging AI technologies in your projects.

We have developed a model known as ChatGPT that engages users in dialogue. This conversational structure allows ChatGPT to effectively respond to follow-up inquiries, acknowledge errors, question faulty assumptions, and decline unsuitable requests. InstructGPT, a related model, focuses on adhering to specific instructions given in prompts and delivering comprehensive answers. ChatGPT Plus is a premium subscription service designed for ChatGPT, the conversational AI. The subscription costs $20 per month, offering subscribers several advantages: - Uninterrupted access to ChatGPT, even during high-demand periods - Accelerated response times - Access to GPT-4 - Integration of ChatGPT plugins - Capability for web-browsing with ChatGPT - Priority for new features and enhancements Currently, ChatGPT Plus is accessible to users in the United States, with plans to gradually invite individuals from our waitlist in the upcoming weeks. We also aim to broaden access and support to more countries and regions in the near future, ensuring that a wider audience can experience its benefits.

The latest iteration of the open-source AI model, which can be fine-tuned and deployed in various environments, is now offered in multiple versions, including 1B, 3B, 11B, and 90B, alongside the option to continue utilizing Llama 3.1. Llama 3.2 comprises a series of large language models (LLMs) that come pretrained and fine-tuned in 1B and 3B configurations for multilingual text only, while the 11B and 90B models accommodate both text and image inputs, producing text outputs. With this new release, you can create highly effective and efficient applications tailored to your needs. For on-device applications, such as summarizing phone discussions or accessing calendar tools, the 1B or 3B models are ideal choices. Meanwhile, the 11B or 90B models excel in image-related tasks, enabling you to transform existing images or extract additional information from images of your environment. Overall, this diverse range of models allows developers to explore innovative use cases across various domains.

Experience unparalleled security and privacy along with the most advanced iteration of ChatGPT to date. 1. Customer data and prompts are excluded from model training processes. 2. Data is securely encrypted both at rest using AES-256 and during transit with TLS 1.2 or higher. 3. Compliance with SOC 2 standards is ensured. 4. A dedicated admin console simplifies bulk management of members. 5. Features like SSO and Domain Verification enhance security. 6. An analytics dashboard provides insights into usage patterns. 7. Users enjoy unlimited, high-speed access to GPT-4 alongside Advanced Data Analysis capabilities*. 8. With 32k token context windows, you can input four times longer texts and retain memory. 9. Easily shareable chat templates facilitate collaboration within your organization. 10. This comprehensive suite of features ensures that your team operates seamlessly and securely.

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

Falcon-40B is a causal decoder-only model consisting of 40 billion parameters, developed by TII and trained on 1 trillion tokens from RefinedWeb, supplemented with carefully selected datasets. It is distributed under the Apache 2.0 license. Why should you consider using Falcon-40B? This model stands out as the leading open-source option available, surpassing competitors like LLaMA, StableLM, RedPajama, and MPT, as evidenced by its ranking on the OpenLLM Leaderboard. Its design is specifically tailored for efficient inference, incorporating features such as FlashAttention and multiquery capabilities. Moreover, it is offered under a flexible Apache 2.0 license, permitting commercial applications without incurring royalties or facing restrictions. It's important to note that this is a raw, pretrained model and is generally recommended to be fine-tuned for optimal performance in most applications. If you need a version that is more adept at handling general instructions in a conversational format, you might want to explore Falcon-40B-Instruct as a potential alternative.

Claude represents a sophisticated artificial intelligence language model capable of understanding and producing text that resembles human communication. Anthropic is an organization dedicated to AI safety and research, aiming to develop AI systems that are not only dependable and understandable but also controllable. While contemporary large-scale AI systems offer considerable advantages, they also present challenges such as unpredictability and lack of transparency; thus, our mission is to address these concerns. Currently, our primary emphasis lies in advancing research to tackle these issues effectively; however, we anticipate numerous opportunities in the future where our efforts could yield both commercial value and societal benefits. As we continue our journey, we remain committed to enhancing the safety and usability of AI technologies.

Sky-T1-32B-Preview is an innovative open-source reasoning model crafted by the NovaSky team at UC Berkeley's Sky Computing Lab. It delivers performance comparable to proprietary models such as o1-preview on various reasoning and coding assessments, while being developed at a cost of less than $450, highlighting the potential for budget-friendly, advanced reasoning abilities. Fine-tuned from Qwen2.5-32B-Instruct, the model utilized a meticulously curated dataset comprising 17,000 examples spanning multiple fields, such as mathematics and programming. The entire training process was completed in just 19 hours using eight H100 GPUs with DeepSpeed Zero-3 offloading technology. Every component of this initiative—including the data, code, and model weights—is entirely open-source, allowing both academic and open-source communities to not only replicate but also improve upon the model's capabilities. This accessibility fosters collaboration and innovation in the realm of artificial intelligence research and development.

OLMo 2 represents a collection of completely open language models created by the Allen Institute for AI (AI2), aimed at giving researchers and developers clear access to training datasets, open-source code, reproducible training methodologies, and thorough assessments. These models are trained on an impressive volume of up to 5 trillion tokens and compete effectively with top open-weight models like Llama 3.1, particularly in English academic evaluations. A key focus of OLMo 2 is on ensuring training stability, employing strategies to mitigate loss spikes during extended training periods, and applying staged training interventions in the later stages of pretraining to mitigate weaknesses in capabilities. Additionally, the models leverage cutting-edge post-training techniques derived from AI2's Tülu 3, leading to the development of OLMo 2-Instruct models. To facilitate ongoing enhancements throughout the development process, an actionable evaluation framework known as the Open Language Modeling Evaluation System (OLMES) was created, which includes 20 benchmarks that evaluate essential capabilities. This comprehensive approach not only fosters transparency but also encourages continuous improvement in language model performance.

ChatGLM-6B is a bilingual dialogue model that supports both Chinese and English, built on the General Language Model (GLM) framework and features 6.2 billion parameters. Thanks to model quantization techniques, it can be easily run on standard consumer graphics cards, requiring only 6GB of video memory at the INT4 quantization level. This model employs methodologies akin to those found in ChatGPT but is specifically tailored to enhance Chinese question-and-answer interactions and dialogue. Following extensive training with approximately 1 trillion identifiers in both languages, along with additional supervision, fine-tuning, self-assistance through feedback, and reinforcement learning from human input, ChatGLM-6B has demonstrated an impressive capability to produce responses that resonate well with human users. Its adaptability and performance make it a valuable tool for bilingual communication.

Baichuan-13B is an advanced large-scale language model developed by Baichuan Intelligent, featuring 13 billion parameters and available for open-source and commercial use, building upon its predecessor Baichuan-7B. This model has set new records for performance among similarly sized models on esteemed Chinese and English evaluation metrics. The release includes two distinct pre-training variations: Baichuan-13B-Base and Baichuan-13B-Chat. By significantly increasing the parameter count to 13 billion, Baichuan-13B enhances its capabilities, training on 1.4 trillion tokens from a high-quality dataset, which surpasses LLaMA-13B's training data by 40%. It currently holds the distinction of being the model with the most extensive training data in the 13B category, providing robust support for both Chinese and English languages, utilizing ALiBi positional encoding, and accommodating a context window of 4096 tokens for improved comprehension and generation. This makes it a powerful tool for a variety of applications in natural language processing.

Qwen LLM represents a collection of advanced large language models created by Alibaba Cloud's Damo Academy. These models leverage an extensive dataset comprising text and code, enabling them to produce human-like text, facilitate language translation, craft various forms of creative content, and provide informative answers to queries. Key attributes of Qwen LLMs include: A range of sizes: The Qwen series features models with parameters varying from 1.8 billion to 72 billion, catering to diverse performance requirements and applications. Open source availability: Certain versions of Qwen are open-source, allowing users to access and modify the underlying code as needed. Multilingual capabilities: Qwen is equipped to comprehend and translate several languages, including English, Chinese, and French. Versatile functionalities: In addition to language generation and translation, Qwen models excel in tasks such as answering questions, summarizing texts, and generating code, making them highly adaptable tools for various applications. Overall, the Qwen LLM family stands out for its extensive capabilities and flexibility in meeting user needs.

OpenEuroLLM represents a collaborative effort between prominent AI firms and research organizations across Europe, aimed at creating a suite of open-source foundational models to promote transparency in artificial intelligence within the continent. This initiative prioritizes openness by making data, documentation, training and testing code, and evaluation metrics readily available, thereby encouraging community participation. It is designed to comply with European Union regulations, with the goal of delivering efficient large language models that meet the specific standards of Europe. A significant aspect of the project is its commitment to linguistic and cultural diversity, ensuring that multilingual capabilities cover all official EU languages and potentially more. The initiative aspires to broaden access to foundational models that can be fine-tuned for a range of applications, enhance evaluation outcomes across different languages, and boost the availability of training datasets and benchmarks for researchers and developers alike. By sharing tools, methodologies, and intermediate results, transparency is upheld during the entire training process, fostering trust and collaboration within the AI community. Ultimately, OpenEuroLLM aims to pave the way for more inclusive and adaptable AI solutions that reflect the rich diversity of European languages and cultures.

Scale AI is excited to introduce Defense Llama, a specialized Large Language Model (LLM) developed from Meta’s Llama 3, tailored specifically to enhance American national security initiatives. Designed for exclusive use within controlled U.S. government settings through Scale Donovan, Defense Llama equips our military personnel and national security experts with the generative AI tools needed for various applications, including the planning of military operations and the analysis of adversary weaknesses. With its training grounded in a comprehensive array of materials, including military doctrines and international humanitarian laws, Defense Llama adheres to the Department of Defense (DoD) guidelines on armed conflict and aligns with the DoD’s Ethical Principles for Artificial Intelligence. This structured foundation allows the model to deliver precise, relevant, and insightful responses tailored to the needs of its users. By providing a secure and efficient generative AI platform, Scale is committed to enhancing the capabilities of U.S. defense personnel in their critical missions. The integration of such technology marks a significant advancement in how national security objectives can be achieved.

Dolly is an economical large language model that surprisingly demonstrates a notable level of instruction-following abilities similar to those seen in ChatGPT. While the Alpaca team's research revealed that cutting-edge models could be encouraged to excel in high-quality instruction adherence, our findings indicate that even older open-source models with earlier architectures can display remarkable behaviors when fine-tuned on a modest set of instructional training data. By utilizing an existing open-source model with 6 billion parameters from EleutherAI, Dolly has been slightly adjusted to enhance its ability to follow instructions, showcasing skills like brainstorming and generating text that were absent in its original form. This approach not only highlights the potential of older models but also opens new avenues for leveraging existing technologies in innovative ways.

The newest version in the Llama series, Llama 3.3, represents a significant advancement in language models aimed at enhancing AI's capabilities in understanding and communication. It boasts improved contextual reasoning, superior language generation, and advanced fine-tuning features aimed at producing exceptionally accurate, human-like responses across a variety of uses. This iteration incorporates a more extensive training dataset, refined algorithms for deeper comprehension, and mitigated biases compared to earlier versions. Llama 3.3 stands out in applications including natural language understanding, creative writing, technical explanations, and multilingual interactions, making it a crucial asset for businesses, developers, and researchers alike. Additionally, its modular architecture facilitates customizable deployment in specific fields, ensuring it remains versatile and high-performing even in large-scale applications. With these enhancements, Llama 3.3 is poised to redefine the standards of AI language models.

Stability AI, along with its CarperAI lab, is excited to unveil Stable Beluga 1 and its advanced successor, Stable Beluga 2, previously known as FreeWilly, both of which are robust new Large Language Models (LLMs) available for public use. These models exhibit remarkable reasoning capabilities across a wide range of benchmarks, showcasing their versatility and strength. Stable Beluga 1 is built on the original LLaMA 65B foundation model and has undergone meticulous fine-tuning with a novel synthetically-generated dataset utilizing Supervised Fine-Tune (SFT) in the conventional Alpaca format. In a similar vein, Stable Beluga 2 utilizes the LLaMA 2 70B foundation model, pushing the boundaries of performance in the industry. Their development marks a significant step forward in the evolution of open access AI technologies.

Foundation models, including GPT-4, have significantly accelerated advancements in artificial intelligence, yet the most advanced models remain either proprietary or only partially accessible. In response to this challenge, the RedPajama initiative aims to develop a collection of top-tier, fully open-source models. We are thrilled to announce that we have successfully completed the initial phase of this endeavor: recreating the LLaMA training dataset, which contains over 1.2 trillion tokens. Currently, many of the leading foundation models are locked behind commercial APIs, restricting opportunities for research, customization, and application with sensitive information. The development of fully open-source models represents a potential solution to these limitations, provided that the open-source community can bridge the gap in quality between open and closed models. Recent advancements have shown promising progress in this area, suggesting that the AI field is experiencing a transformative period akin to the emergence of Linux. The success of Stable Diffusion serves as a testament to the fact that open-source alternatives can not only match the quality of commercial products like DALL-E but also inspire remarkable creativity through the collaborative efforts of diverse communities. By fostering an open-source ecosystem, we can unlock new possibilities for innovation and ensure broader access to cutting-edge AI technology.

Mistral 7B is a language model with 7.3 billion parameters that demonstrates superior performance compared to larger models such as Llama 2 13B on a variety of benchmarks. It utilizes innovative techniques like Grouped-Query Attention (GQA) for improved inference speed and Sliding Window Attention (SWA) to manage lengthy sequences efficiently. Released under the Apache 2.0 license, Mistral 7B is readily available for deployment on different platforms, including both local setups and prominent cloud services. Furthermore, a specialized variant known as Mistral 7B Instruct has shown remarkable capabilities in following instructions, outperforming competitors like Llama 2 13B Chat in specific tasks. This versatility makes Mistral 7B an attractive option for developers and researchers alike.

Cohere is a robust enterprise AI platform that empowers developers and organizations to create advanced applications leveraging language technologies. With a focus on large language models (LLMs), Cohere offers innovative solutions for tasks such as text generation, summarization, and semantic search capabilities. The platform features the Command family designed for superior performance in language tasks, alongside Aya Expanse, which supports multilingual functionalities across 23 different languages. Emphasizing security and adaptability, Cohere facilitates deployment options that span major cloud providers, private cloud infrastructures, or on-premises configurations to cater to a wide array of enterprise requirements. The company partners with influential industry players like Oracle and Salesforce, striving to weave generative AI into business applications, thus enhancing automation processes and customer interactions. Furthermore, Cohere For AI, its dedicated research lab, is committed to pushing the boundaries of machine learning via open-source initiatives and fostering a collaborative global research ecosystem. This commitment to innovation not only strengthens their technology but also contributes to the broader AI landscape.

Introducing an open-source AI model that can be fine-tuned, distilled, and deployed across various platforms. Our newest instruction-tuned model comes in three sizes: 8B, 70B, and 405B, giving you options to suit different needs. With our open ecosystem, you can expedite your development process using a diverse array of tailored product offerings designed to meet your specific requirements. You have the flexibility to select between real-time inference and batch inference services according to your project's demands. Additionally, you can download model weights to enhance cost efficiency per token while fine-tuning for your application. Improve performance further by utilizing synthetic data and seamlessly deploy your solutions on-premises or in the cloud. Take advantage of Llama system components and expand the model's capabilities through zero-shot tool usage and retrieval-augmented generation (RAG) to foster agentic behaviors. By utilizing 405B high-quality data, you can refine specialized models tailored to distinct use cases, ensuring optimal functionality for your applications. Ultimately, this empowers developers to create innovative solutions that are both efficient and effective.

OpenELM is a family of open-source language models created by Apple. By employing a layer-wise scaling approach, it effectively distributes parameters across the transformer model's layers, resulting in improved accuracy when compared to other open language models of a similar scale. This model is trained using datasets that are publicly accessible and is noted for achieving top-notch performance relative to its size. Furthermore, OpenELM represents a significant advancement in the pursuit of high-performing language models in the open-source community.

We are excited to present MPT-7B, the newest addition to the MosaicML Foundation Series. This transformer model has been meticulously trained from the ground up using 1 trillion tokens of diverse text and code. It is open-source and ready for commercial applications, delivering performance on par with LLaMA-7B. The training process took 9.5 days on the MosaicML platform, requiring no human input and incurring an approximate cost of $200,000. With MPT-7B, you can now train, fine-tune, and launch your own customized MPT models, whether you choose to begin with one of our provided checkpoints or start anew. To provide additional options, we are also introducing three fine-tuned variants alongside the base MPT-7B: MPT-7B-Instruct, MPT-7B-Chat, and MPT-7B-StoryWriter-65k+, the latter boasting an impressive context length of 65,000 tokens, allowing for extensive content generation. These advancements open up new possibilities for developers and researchers looking to leverage the power of transformer models in their projects.

Qwen2.5-1M, an open-source language model from the Qwen team, has been meticulously crafted to manage context lengths reaching as high as one million tokens. This version introduces two distinct model variants, namely Qwen2.5-7B-Instruct-1M and Qwen2.5-14B-Instruct-1M, representing a significant advancement as it is the first instance of Qwen models being enhanced to accommodate such large context lengths. In addition to this, the team has released an inference framework that is based on vLLM and incorporates sparse attention mechanisms, which greatly enhance the processing speed for 1M-token inputs, achieving improvements between three to seven times. A detailed technical report accompanies this release, providing in-depth insights into the design choices and the results from various ablation studies. This transparency allows users to fully understand the capabilities and underlying technology of the models.

Open R1 is a collaborative, open-source effort focused on mimicking the sophisticated AI functionalities of DeepSeek-R1 using clear and open methods. Users have the opportunity to explore the Open R1 AI model or engage in a free online chat with DeepSeek R1 via the Open R1 platform. This initiative presents a thorough execution of DeepSeek-R1's reasoning-optimized training framework, featuring resources for GRPO training, SFT fine-tuning, and the creation of synthetic data, all available under the MIT license. Although the original training dataset is still proprietary, Open R1 equips users with a complete suite of tools to create and enhance their own AI models, allowing for greater customization and experimentation in the field of artificial intelligence.

We are creating advanced large language models tailored to India's rich linguistic diversity while also facilitating innovative GenAI applications through custom enterprise solutions. Our focus is on building a robust platform that empowers businesses to create and assess their own GenAI applications seamlessly. Believing in the transformative potential of open-source, we are dedicated to contributing to community-driven models and datasets, and we will take a leading role in curating large-scale data aimed at the public good. Our team consists of dynamic AI innovators who combine their expertise in research, engineering, product design, and business operations to drive progress. United by a common dedication to scientific excellence and making a positive societal impact, we cultivate a workplace where addressing intricate technological challenges is embraced as a true passion. In this collaborative environment, we strive to push the boundaries of AI and its applications for the betterment of society.

IBM® Granite™ comprises a suite of AI models specifically designed for business applications, built from the ground up to prioritize trust and scalability in AI implementations. Currently, the open-source Granite models can be accessed. Our goal is to make AI widely available to as many developers as possible, which is why we have released the essential Granite Code, as well as Time Series, Language, and GeoSpatial models as open-source on Hugging Face, under the permissive Apache 2.0 license, allowing extensive commercial use without restrictions. Every Granite model is developed using meticulously selected data, ensuring exceptional transparency regarding the sources of the training data. Additionally, we have made the tools that validate and maintain the quality of this data accessible to the public, meeting the rigorous standards required for enterprise-level applications. This commitment to openness and quality reflects our dedication to fostering innovation in the AI landscape.