Alternatives to ChatGLM

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.

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.

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.

Yi-Lightning, a product of 01.AI and spearheaded by Kai-Fu Lee, marks a significant leap forward in the realm of large language models, emphasizing both performance excellence and cost-effectiveness. With the ability to process a context length of up to 16K tokens, it offers an attractive pricing model of $0.14 per million tokens for both inputs and outputs, making it highly competitive in the market. The model employs an improved Mixture-of-Experts (MoE) framework, featuring detailed expert segmentation and sophisticated routing techniques that enhance its training and inference efficiency. Yi-Lightning has distinguished itself across multiple fields, achieving top distinctions in areas such as Chinese language processing, mathematics, coding tasks, and challenging prompts on chatbot platforms, where it ranked 6th overall and 9th in style control. Its creation involved an extensive combination of pre-training, targeted fine-tuning, and reinforcement learning derived from human feedback, which not only enhances its performance but also prioritizes user safety. Furthermore, the model's design includes significant advancements in optimizing both memory consumption and inference speed, positioning it as a formidable contender in its field.

Qwen2.5-Max is an advanced Mixture-of-Experts (MoE) model created by the Qwen team, which has been pretrained on an extensive dataset of over 20 trillion tokens and subsequently enhanced through methods like Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). Its performance in evaluations surpasses that of models such as DeepSeek V3 across various benchmarks, including Arena-Hard, LiveBench, LiveCodeBench, and GPQA-Diamond, while also achieving strong results in other tests like MMLU-Pro. This model is available through an API on Alibaba Cloud, allowing users to easily integrate it into their applications, and it can also be interacted with on Qwen Chat for a hands-on experience. With its superior capabilities, Qwen2.5-Max represents a significant advancement in AI model technology.

Reka Flash 3 is a cutting-edge multimodal AI model with 21 billion parameters, crafted by Reka AI to perform exceptionally well in tasks such as general conversation, coding, following instructions, and executing functions. This model adeptly handles and analyzes a myriad of inputs, including text, images, video, and audio, providing a versatile and compact solution for a wide range of applications. Built from the ground up, Reka Flash 3 was trained on a rich array of datasets, encompassing both publicly available and synthetic information, and it underwent a meticulous instruction tuning process with high-quality selected data to fine-tune its capabilities. The final phase of its training involved employing reinforcement learning techniques, specifically using the REINFORCE Leave One-Out (RLOO) method, which combined both model-based and rule-based rewards to significantly improve its reasoning skills. With an impressive context length of 32,000 tokens, Reka Flash 3 competes effectively with proprietary models like OpenAI's o1-mini, making it an excellent choice for applications requiring low latency or on-device processing. The model operates at full precision with a memory requirement of 39GB (fp16), although it can be efficiently reduced to just 11GB through the use of 4-bit quantization, demonstrating its adaptability for various deployment scenarios. Overall, Reka Flash 3 represents a significant advancement in multimodal AI technology, capable of meeting diverse user needs across multiple platforms.

Microsoft’s BitNet b1.58 2B4T is a breakthrough in AI with its native 1-bit LLM architecture. This model has been optimized for computational efficiency, offering significant reductions in memory, energy, and latency while still achieving high performance on various AI benchmarks. It supports a range of natural language processing tasks, making it an ideal solution for scalable and cost-effective AI implementations in industries requiring fast, energy-efficient inference and robust language capabilities.

Canopy Labs has unveiled Orpheus, an innovative suite of advanced speech large language models (LLMs) aimed at achieving human-like speech generation capabilities. Utilizing the Llama-3 architecture, these models have been trained on an extensive dataset comprising over 100,000 hours of English speech, allowing them to generate speech that exhibits natural intonation, emotional depth, and rhythmic flow that outperforms existing high-end closed-source alternatives. Orpheus also features zero-shot voice cloning, enabling users to mimic voices without any need for prior fine-tuning, and provides easy-to-use tags for controlling emotion and intonation. The models are engineered for low latency, achieving approximately 200ms streaming latency for real-time usage, which can be further decreased to around 100ms when utilizing input streaming. Canopy Labs has made available both pre-trained and fine-tuned models with 3 billion parameters under the flexible Apache 2.0 license, with future intentions to offer smaller models with 1 billion, 400 million, and 150 million parameters to cater to devices with limited resources. This strategic move is expected to broaden accessibility and application potential across various platforms and use cases.

Pre-trained language models have made significant strides, achieving top-tier performance across multiple Natural Language Processing (NLP) applications. The impressive capabilities of GPT-3 highlight how increasing the scale of these models can unlock their vast potential. Recently, a comprehensive framework known as ERNIE 3.0 was introduced to pre-train large-scale models enriched with knowledge, culminating in a model boasting 10 billion parameters. This iteration of ERNIE 3.0 has surpassed the performance of existing leading models in a variety of NLP tasks. To further assess the effects of scaling, we have developed an even larger model called ERNIE 3.0 Titan, which consists of up to 260 billion parameters and is built on the PaddlePaddle platform. Additionally, we have implemented a self-supervised adversarial loss alongside a controllable language modeling loss, enabling ERNIE 3.0 Titan to produce texts that are both reliable and modifiable, thus pushing the boundaries of what these models can achieve. This approach not only enhances the model's capabilities but also opens new avenues for research in text generation and control.

Recent breakthroughs in natural language processing, comprehension, and generation have been greatly influenced by the development of large language models. This research presents a system that employs Ascend 910 AI processors and the MindSpore framework to train a language model exceeding one trillion parameters, specifically 1.085 trillion, referred to as PanGu-{\Sigma}. This model enhances the groundwork established by PanGu-{\alpha} by converting the conventional dense Transformer model into a sparse format through a method known as Random Routed Experts (RRE). Utilizing a substantial dataset of 329 billion tokens, the model was effectively trained using a strategy called Expert Computation and Storage Separation (ECSS), which resulted in a remarkable 6.3-fold improvement in training throughput through the use of heterogeneous computing. Through various experiments, it was found that PanGu-{\Sigma} achieves a new benchmark in zero-shot learning across multiple downstream tasks in Chinese NLP, showcasing its potential in advancing the field. This advancement signifies a major leap forward in the capabilities of language models, illustrating the impact of innovative training techniques and architectural modifications.

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.

The Megatron-Turing Natural Language Generation model (MT-NLG) stands out as the largest and most advanced monolithic transformer model for the English language, boasting an impressive 530 billion parameters. This 105-layer transformer architecture significantly enhances the capabilities of previous leading models, particularly in zero-shot, one-shot, and few-shot scenarios. It exhibits exceptional precision across a wide range of natural language processing tasks, including completion prediction, reading comprehension, commonsense reasoning, natural language inference, and word sense disambiguation. To foster further research on this groundbreaking English language model and to allow users to explore and utilize its potential in various language applications, NVIDIA has introduced an Early Access program for its managed API service dedicated to the MT-NLG model. This initiative aims to facilitate experimentation and innovation in the field of natural language processing.

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.

StarCoder and StarCoderBase represent advanced Large Language Models specifically designed for code, developed using openly licensed data from GitHub, which encompasses over 80 programming languages, Git commits, GitHub issues, and Jupyter notebooks. In a manner akin to LLaMA, we constructed a model with approximately 15 billion parameters trained on a staggering 1 trillion tokens. Furthermore, we tailored the StarCoderBase model with 35 billion Python tokens, leading to the creation of what we now refer to as StarCoder. Our evaluations indicated that StarCoderBase surpasses other existing open Code LLMs when tested against popular programming benchmarks and performs on par with or even exceeds proprietary models like code-cushman-001 from OpenAI, the original Codex model that fueled early iterations of GitHub Copilot. With an impressive context length exceeding 8,000 tokens, the StarCoder models possess the capability to handle more information than any other open LLM, thus paving the way for a variety of innovative applications. This versatility is highlighted by our ability to prompt the StarCoder models through a sequence of dialogues, effectively transforming them into dynamic technical assistants that can provide support in diverse programming tasks.

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.

GPT-4o, with the "o" denoting "omni," represents a significant advancement in the realm of human-computer interaction by accommodating various input types such as text, audio, images, and video, while also producing outputs across these same formats. Its capability to process audio inputs allows for responses in as little as 232 milliseconds, averaging 320 milliseconds, which closely resembles the response times seen in human conversations. In terms of performance, it maintains the efficiency of GPT-4 Turbo for English text and coding while showing marked enhancements in handling text in other languages, all while operating at a much faster pace and at a cost that is 50% lower via the API. Furthermore, GPT-4o excels in its ability to comprehend vision and audio, surpassing the capabilities of its predecessors, making it a powerful tool for multi-modal interactions. This innovative model not only streamlines communication but also broadens the possibilities for applications in diverse fields.

CodeGemma represents an impressive suite of efficient and versatile models capable of tackling numerous coding challenges, including middle code completion, code generation, natural language processing, mathematical reasoning, and following instructions. It features three distinct model types: a 7B pre-trained version designed for code completion and generation based on existing code snippets, a 7B variant fine-tuned for translating natural language queries into code and adhering to instructions, and an advanced 2B pre-trained model that offers code completion speeds up to twice as fast. Whether you're completing lines, developing functions, or crafting entire segments of code, CodeGemma supports your efforts, whether you're working in a local environment or leveraging Google Cloud capabilities. With training on an extensive dataset comprising 500 billion tokens predominantly in English, sourced from web content, mathematics, and programming languages, CodeGemma not only enhances the syntactical accuracy of generated code but also ensures its semantic relevance, thereby minimizing mistakes and streamlining the debugging process. This powerful tool continues to evolve, making coding more accessible and efficient for developers everywhere.

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.

Hume AI has unveiled Octave, an innovative text-to-speech platform that utilizes advanced language model technology to deeply understand and interpret word context, allowing it to produce speech infused with the right emotions, rhythm, and cadence. Unlike conventional TTS systems that simply vocalize text, Octave mimics the performance of a human actor, delivering lines with rich expression tailored to the content being spoken. Users are empowered to create a variety of unique AI voices by submitting descriptive prompts, such as "a skeptical medieval peasant," facilitating personalized voice generation that reflects distinct character traits or situational contexts. Moreover, Octave supports the adjustment of emotional tone and speaking style through straightforward natural language commands, enabling users to request changes like "speak with more enthusiasm" or "whisper in fear" for precise output customization. This level of interactivity enhances user experience by allowing for a more engaging and immersive auditory experience.

Qwen-7B is the 7-billion parameter iteration of Alibaba Cloud's Qwen language model series, also known as Tongyi Qianwen. This large language model utilizes a Transformer architecture and has been pretrained on an extensive dataset comprising web texts, books, code, and more. Furthermore, we introduced Qwen-7B-Chat, an AI assistant that builds upon the pretrained Qwen-7B model and incorporates advanced alignment techniques. The Qwen-7B series boasts several notable features: It has been trained on a premium dataset, with over 2.2 trillion tokens sourced from a self-assembled collection of high-quality texts and codes across various domains, encompassing both general and specialized knowledge. Additionally, our model demonstrates exceptional performance, surpassing competitors of similar size on numerous benchmark datasets that assess capabilities in natural language understanding, mathematics, and coding tasks. This positions Qwen-7B as a leading choice in the realm of AI language models. Overall, its sophisticated training and robust design contribute to its impressive versatility and effectiveness.

Samsung Gauss is an innovative AI model crafted by Samsung Electronics, designed to serve as a large language model that has been trained on an extensive array of text and code. This advanced model is capable of producing coherent text, translating various languages, creating diverse forms of artistic content, and providing informative answers to a wide range of inquiries. Although Samsung Gauss is still being refined, it has already demonstrated proficiency in a variety of tasks, such as: Following directives and fulfilling requests with careful consideration. Offering thorough and insightful responses to questions, regardless of their complexity or peculiarity. Crafting different types of creative outputs, which include poems, programming code, scripts, musical compositions, emails, and letters. To illustrate its capabilities, Samsung Gauss can translate text among numerous languages, including English, French, German, Spanish, Chinese, Japanese, and Korean, while also generating functional code tailored to specific programming needs. Ultimately, as development continues, the potential applications of Samsung Gauss are bound to expand even further.

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.

DeepSeek R2 is the highly awaited successor to DeepSeek R1, an innovative AI reasoning model that made waves when it was introduced in January 2025 by the Chinese startup DeepSeek. This new version builds on the remarkable achievements of R1, which significantly altered the AI landscape by providing cost-effective performance comparable to leading models like OpenAI’s o1. R2 is set to offer a substantial upgrade in capabilities, promising impressive speed and reasoning abilities akin to that of a human, particularly in challenging areas such as complex coding and advanced mathematics. By utilizing DeepSeek’s cutting-edge Mixture-of-Experts architecture along with optimized training techniques, R2 is designed to surpass the performance of its predecessor while keeping computational demands low. Additionally, there are expectations that this model may broaden its reasoning skills to accommodate languages beyond just English, potentially increasing its global usability. The anticipation surrounding R2 highlights the ongoing evolution of AI technology and its implications for various industries.

Sparrow serves as a research prototype and a demonstration project aimed at enhancing the training of dialogue agents to be more effective, accurate, and safe. By instilling these attributes within a generalized dialogue framework, Sparrow improves our insights into creating agents that are not only safer but also more beneficial, with the long-term ambition of contributing to the development of safer and more effective artificial general intelligence (AGI). Currently, Sparrow is not available for public access. The task of training conversational AI presents unique challenges, particularly due to the complexities involved in defining what constitutes a successful dialogue. To tackle this issue, we utilize a method of reinforcement learning (RL) that incorporates feedback from individuals, which helps us understand their preferences regarding the usefulness of different responses. By presenting participants with various model-generated answers to identical questions, we gather their opinions on which responses they find most appealing, thus refining our training process. This feedback loop is crucial for enhancing the performance and reliability of dialogue agents.

The multilingual T5 (mT5) is a highly versatile pretrained text-to-text transformer model, developed using a methodology akin to that of T5. This repository serves as a resource for replicating the findings outlined in the mT5 research paper. mT5 has been trained on the extensive mC4 corpus, which encompasses 101 different languages, including but not limited to Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish, Kyrgyz, Lao, Latin, Latvian, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Samoan, Scottish Gaelic, Serbian, Shona, Sindhi, and many others. This impressive range of languages makes mT5 a valuable tool for multilingual applications across various fields.

We are excited to announce the launch of Phi-2, a language model featuring 2.7 billion parameters that excels in reasoning and language comprehension, achieving top-tier results compared to other base models with fewer than 13 billion parameters. In challenging benchmarks, Phi-2 competes with and often surpasses models that are up to 25 times its size, a feat made possible by advancements in model scaling and meticulous curation of training data. Due to its efficient design, Phi-2 serves as an excellent resource for researchers interested in areas such as mechanistic interpretability, enhancing safety measures, or conducting fine-tuning experiments across a broad spectrum of tasks. To promote further exploration and innovation in language modeling, Phi-2 has been integrated into the Azure AI Studio model catalog, encouraging collaboration and development within the research community. Researchers can leverage this model to unlock new insights and push the boundaries of language technology.

The QwQ-32B model, created by Alibaba Cloud's Qwen team, represents a significant advancement in AI reasoning, aimed at improving problem-solving skills. Boasting 32 billion parameters, it rivals leading models such as DeepSeek's R1, which contains 671 billion parameters. This remarkable efficiency stems from its optimized use of parameters, enabling QwQ-32B to tackle complex tasks like mathematical reasoning, programming, and other problem-solving scenarios while consuming fewer resources. It can handle a context length of up to 32,000 tokens, making it adept at managing large volumes of input data. Notably, QwQ-32B is available through Alibaba's Qwen Chat service and is released under the Apache 2.0 license, which fosters collaboration and innovation among AI developers. With its cutting-edge features, QwQ-32B is poised to make a substantial impact in the field of artificial intelligence.

Qwen2 represents a collection of extensive language models crafted by the Qwen team at Alibaba Cloud. This series encompasses a variety of models, including base and instruction-tuned versions, with parameters varying from 0.5 billion to an impressive 72 billion, showcasing both dense configurations and a Mixture-of-Experts approach. The Qwen2 series aims to outperform many earlier open-weight models, including its predecessor Qwen1.5, while also striving to hold its own against proprietary models across numerous benchmarks in areas such as language comprehension, generation, multilingual functionality, programming, mathematics, and logical reasoning. Furthermore, this innovative series is poised to make a significant impact in the field of artificial intelligence, offering enhanced capabilities for a diverse range of applications.

ALBERT is a self-supervised Transformer architecture that undergoes pretraining on a vast dataset of English text, eliminating the need for manual annotations by employing an automated method to create inputs and corresponding labels from unprocessed text. This model is designed with two primary training objectives in mind. The first objective, known as Masked Language Modeling (MLM), involves randomly obscuring 15% of the words in a given sentence and challenging the model to accurately predict those masked words. This approach sets it apart from recurrent neural networks (RNNs) and autoregressive models such as GPT, as it enables ALBERT to capture bidirectional representations of sentences. The second training objective is Sentence Ordering Prediction (SOP), which focuses on the task of determining the correct sequence of two adjacent text segments during the pretraining phase. By incorporating these dual objectives, ALBERT enhances its understanding of language structure and contextual relationships. This innovative design contributes to its effectiveness in various natural language processing tasks.

Llama 4 Maverick is a cutting-edge multimodal AI model with 17 billion active parameters and 128 experts, setting a new standard for efficiency and performance. It excels in diverse domains, outperforming other models such as GPT-4o and Gemini 2.0 Flash in coding, reasoning, and image-related tasks. Llama 4 Maverick integrates both text and image processing seamlessly, offering enhanced capabilities for complex tasks such as visual question answering, content generation, and problem-solving. The model’s performance-to-cost ratio makes it an ideal choice for businesses looking to integrate powerful AI into their operations without the hefty resource demands.

Our advanced multimodal assistant is meticulously crafted with a focus on privacy, security, and operational efficiency. Yasa is trained to interpret various forms of content, including text, images, videos, and tabular data, with plans to expand to additional modalities in the future. It can assist you in brainstorming for creative projects, answering fundamental questions, or extracting valuable insights from your internal datasets. With just a few straightforward commands, you can generate, train, compress, or deploy it on your own servers. Our proprietary algorithms enable you to customize the model according to your specific data and requirements. We utilize innovative techniques that encompass retrieval, fine-tuning, self-supervised instruction tuning, and reinforcement learning to optimize our model based on your unique datasets, ensuring that it meets your operational needs effectively. In doing so, we aim to enhance user experience and deliver tailored solutions that drive productivity and innovation.

DeepSeek stands out as a state-of-the-art AI assistant, leveraging the sophisticated DeepSeek-V3 model that boasts an impressive 600 billion parameters for superior performance. Created to rival leading AI systems globally, it delivers rapid responses alongside an extensive array of features aimed at enhancing daily tasks' efficiency and simplicity. Accessible on various platforms, including iOS, Android, and web, DeepSeek guarantees that users can connect from virtually anywhere. The application offers support for numerous languages and is consistently updated to enhance its capabilities, introduce new language options, and fix any issues. Praised for its smooth functionality and adaptability, DeepSeek has received enthusiastic reviews from a diverse user base around the globe. Furthermore, its commitment to user satisfaction and continuous improvement ensures that it remains at the forefront of AI technology.

Alpa is designed to simplify the process of automating extensive distributed training and serving with minimal coding effort. Originally created by a team at Sky Lab, UC Berkeley, it employs several advanced techniques documented in a paper presented at OSDI'2022. The Alpa community continues to expand, welcoming new contributors from Google. A language model serves as a probability distribution over sequences of words, allowing it to foresee the next word based on the context of preceding words. This capability proves valuable for various AI applications, including email auto-completion and chatbot functionalities. For further insights, one can visit the Wikipedia page dedicated to language models. Among these models, GPT-3 stands out as a remarkably large language model, boasting 175 billion parameters and utilizing deep learning to generate text that closely resembles human writing. Many researchers and media outlets have characterized GPT-3 as "one of the most interesting and significant AI systems ever developed," and its influence continues to grow as it becomes integral to cutting-edge NLP research and applications. Additionally, its implementation has sparked discussions about the future of AI-driven communication tools.

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.

Opus, recognized as our most advanced model, surpasses its competitors in numerous widely-used evaluation benchmarks for artificial intelligence, including assessments of undergraduate expert knowledge (MMLU), graduate-level reasoning (GPQA), fundamental mathematics (GSM8K), and others. Its performance approaches human-like comprehension and fluency in handling intricate tasks, positioning it at the forefront of general intelligence advancements. Furthermore, all Claude 3 models demonstrate enhanced abilities in analysis and prediction, sophisticated content creation, programming code generation, and engaging in conversations in various non-English languages such as Spanish, Japanese, and French, showcasing their versatility in communication.

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.

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.

OpenAI aims to guarantee that artificial general intelligence (AGI)—defined as highly autonomous systems excelling beyond human capabilities in most economically significant tasks—serves the interests of all humanity. While we intend to develop safe and advantageous AGI directly, we consider our mission successful if our efforts support others in achieving this goal. You can utilize our API for a variety of language-related tasks, including semantic search, summarization, sentiment analysis, content creation, translation, and beyond, all with just a few examples or by clearly stating your task in English. A straightforward integration provides you with access to our continuously advancing AI technology, allowing you to explore the API’s capabilities through these illustrative completions and discover numerous potential applications.

DeepSeek-V2 is a cutting-edge Mixture-of-Experts (MoE) language model developed by DeepSeek-AI, noted for its cost-effective training and high-efficiency inference features. It boasts an impressive total of 236 billion parameters, with only 21 billion active for each token, and is capable of handling a context length of up to 128K tokens. The model utilizes advanced architectures such as Multi-head Latent Attention (MLA) to optimize inference by minimizing the Key-Value (KV) cache and DeepSeekMoE to enable economical training through sparse computations. Compared to its predecessor, DeepSeek 67B, this model shows remarkable improvements, achieving a 42.5% reduction in training expenses, a 93.3% decrease in KV cache size, and a 5.76-fold increase in generation throughput. Trained on an extensive corpus of 8.1 trillion tokens, DeepSeek-V2 demonstrates exceptional capabilities in language comprehension, programming, and reasoning tasks, positioning it as one of the leading open-source models available today. Its innovative approach not only elevates its performance but also sets new benchmarks within the field of artificial intelligence.

Hippocratic AI represents a cutting-edge advancement in artificial intelligence, surpassing GPT-4 on 105 out of 114 healthcare-related exams and certifications. Notably, it exceeded GPT-4's performance by at least five percent on 74 of these certifications, and on 43 of them, the margin was ten percent or greater. Unlike most language models that rely on a broad range of internet sources—which can sometimes include inaccurate information—Hippocratic AI is committed to sourcing evidence-based healthcare content through legal means. To ensure the model's effectiveness and safety, we are implementing a specialized Reinforcement Learning with Human Feedback process, involving healthcare professionals in training and validating the model before its release. This meticulous approach, dubbed RLHF-HP, guarantees that Hippocratic AI will only be launched after it receives the approval of a significant number of licensed healthcare experts, prioritizing patient safety and accuracy in its applications. The dedication to rigorous validation sets Hippocratic AI apart in the landscape of AI healthcare solutions.

PygmalionAI is a vibrant community focused on the development of open-source initiatives utilizing EleutherAI's GPT-J 6B and Meta's LLaMA models. Essentially, Pygmalion specializes in crafting AI tailored for engaging conversations and roleplaying. The actively maintained Pygmalion AI model currently features the 7B variant, derived from Meta AI's LLaMA model. Requiring a mere 18GB (or even less) of VRAM, Pygmalion demonstrates superior chat functionality compared to significantly larger language models, all while utilizing relatively limited resources. Our meticulously assembled dataset, rich in high-quality roleplaying content, guarantees that your AI companion will be the perfect partner for roleplaying scenarios. Both the model weights and the training code are entirely open-source, allowing you the freedom to modify and redistribute them for any purpose you desire. Generally, language models, such as Pygmalion, operate on GPUs, as they require swift memory access and substantial processing power to generate coherent text efficiently. As a result, users can expect a smooth and responsive interaction experience when employing Pygmalion's capabilities.

LLaVA, or Large Language-and-Vision Assistant, represents a groundbreaking multimodal model that combines a vision encoder with the Vicuna language model, enabling enhanced understanding of both visual and textual information. By employing end-to-end training, LLaVA showcases remarkable conversational abilities, mirroring the multimodal features found in models such as GPT-4. Significantly, LLaVA-1.5 has reached cutting-edge performance on 11 different benchmarks, leveraging publicly accessible data and achieving completion of its training in about one day on a single 8-A100 node, outperforming approaches that depend on massive datasets. The model's development included the construction of a multimodal instruction-following dataset, which was produced using a language-only variant of GPT-4. This dataset consists of 158,000 distinct language-image instruction-following examples, featuring dialogues, intricate descriptions, and advanced reasoning challenges. Such a comprehensive dataset has played a crucial role in equipping LLaVA to handle a diverse range of tasks related to vision and language with great efficiency. In essence, LLaVA not only enhances the interaction between visual and textual modalities but also sets a new benchmark in the field of multimodal AI.

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.

Jurassic-1 offers two model sizes, with the Jumbo variant being the largest at 178 billion parameters, representing the pinnacle of complexity in language models released for developers. Currently, AI21 Studio is in an open beta phase, inviting users to register and begin exploring Jurassic-1 through an accessible API and an interactive web platform. At AI21 Labs, our goal is to revolutionize how people engage with reading and writing by integrating machines as cognitive collaborators, a vision that requires collective effort to realize. Our exploration of language models dates back to what we refer to as our Mesozoic Era (2017 😉). Building upon this foundational research, Jurassic-1 marks the inaugural series of models we are now offering for broad public application. As we move forward, we are excited to see how users will leverage these advancements in their own creative processes.

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.