Top Baichuan-13B Alternatives

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

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

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

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

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

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

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

Stable LM signifies a notable progression in the language model domain, building upon prior open-source experiences, especially through collaboration with EleutherAI, a nonprofit research group. This evolution has included the creation of prominent models like GPT-J, GPT-NeoX, and the Pythia suite, all trained on The Pile open-source dataset, with several recent models such as Cerebras-GPT and Dolly-2 taking cues from this foundational work. In contrast to earlier models, Stable LM utilizes a groundbreaking dataset that is three times as extensive as The Pile, comprising an impressive 1.5 trillion tokens. More details regarding this dataset will be disclosed soon. The vast scale of this dataset allows Stable LM to perform exceptionally well in conversational and programming tasks, even though it has a relatively compact parameter size of 3 to 7 billion compared to larger models like GPT-3, which features 175 billion parameters. Built for adaptability, Stable LM 3B is a streamlined model designed to operate efficiently on portable devices, including laptops and mobile gadgets, which excites us about its potential for practical usage and portability. This innovation has the potential to bridge the gap for users seeking advanced language capabilities in accessible formats, thus broadening the reach and impact of language technologies. Overall, the launch of Stable LM represents a crucial advancement toward developing more efficient and widely available language models for diverse users.

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

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

Recent advancements in natural language processing, understanding, and generation have largely stemmed from the evolution of large language models. This study introduces a system that utilizes Ascend 910 AI processors alongside the MindSpore framework to train a language model that surpasses one trillion parameters, achieving a total of 1.085 trillion, designated as PanGu-{\Sigma}. This model builds upon the foundation laid by PanGu-{\alpha} by transforming the traditional dense Transformer architecture into a sparse configuration via a technique called Random Routed Experts (RRE). By leveraging an extensive dataset comprising 329 billion tokens, the model was successfully trained with a method known as Expert Computation and Storage Separation (ECSS), which led to an impressive 6.3-fold increase in training throughput through the application of heterogeneous computing. Experimental results revealed that PanGu-{\Sigma} sets a new standard in zero-shot learning for various downstream tasks in Chinese NLP, highlighting its significant potential for progressing the field. This breakthrough not only represents a considerable enhancement in the capabilities of language models but also underscores the importance of creative training methodologies and structural innovations in shaping future developments. As such, this research paves the way for further exploration into improving language model efficiency and effectiveness.

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

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

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

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

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

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

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

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

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

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

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

Pre-trained language models have advanced significantly, demonstrating exceptional performance in various Natural Language Processing (NLP) tasks. The remarkable features of GPT-3 illustrate that scaling these models can lead to the discovery of their immense capabilities. Recently, the introduction of a comprehensive framework called ERNIE 3.0 has allowed for the pre-training of large-scale models infused with knowledge, resulting in a model with an impressive 10 billion parameters. This version of ERNIE 3.0 has outperformed many leading models across numerous NLP challenges. In our pursuit of exploring the impact of scaling, we have created an even larger model named ERNIE 3.0 Titan, which boasts up to 260 billion parameters and is developed on the PaddlePaddle framework. Moreover, we have incorporated a self-supervised adversarial loss coupled with a controllable language modeling loss, which empowers ERNIE 3.0 Titan to generate text that is both accurate and adaptable, thus extending the limits of what these models can achieve. This innovative methodology not only improves the model's overall performance but also paves the way for new research opportunities in the fields of text generation and fine-tuning control. As the landscape of NLP continues to evolve, the advancements in these models promise to drive further breakthroughs in understanding and generating human language.

Jamba has emerged as the leading long context model, specifically crafted for builders and tailored to meet enterprise requirements. It outperforms other prominent models of similar scale with its exceptional latency and features a groundbreaking 256k context window, the largest available. Utilizing the innovative Mamba-Transformer MoE architecture, Jamba prioritizes cost efficiency and operational effectiveness. Among its out-of-the-box features are function calls, JSON mode output, document objects, and citation mode, all aimed at improving the overall user experience. The Jamba 1.5 models excel in performance across their expansive context window and consistently achieve top-tier scores on various quality assessment metrics. Enterprises can take advantage of secure deployment options customized to their specific needs, which facilitates seamless integration with existing systems. Furthermore, Jamba is readily accessible via our robust SaaS platform, and deployment options also include collaboration with strategic partners, providing users with added flexibility. For organizations that require specialized solutions, we offer dedicated management and ongoing pre-training services, ensuring that each client can make the most of Jamba’s capabilities. This level of adaptability and support positions Jamba as a premier choice for enterprises in search of innovative and effective solutions for their needs. Additionally, Jamba's commitment to continuous improvement ensures that it remains at the forefront of technological advancements, further solidifying its reputation as a trusted partner for businesses.

The multilingual T5 (mT5) is an exceptionally adaptable pretrained text-to-text transformer model, created using a methodology similar to that of the original T5. This repository provides essential resources for reproducing the results detailed in the mT5 research publication. mT5 has undergone training on the vast mC4 corpus, which includes a remarkable 101 languages, such as 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 more. This extensive language coverage renders mT5 an invaluable asset for multilingual applications in diverse sectors, enhancing its usefulness for researchers and developers alike.

Meta’s Llama 4 Behemoth is an advanced multimodal AI model that boasts 288 billion active parameters, making it one of the most powerful models in the world. It outperforms other leading models like GPT-4.5 and Gemini 2.0 Pro on numerous STEM-focused benchmarks, showcasing exceptional skills in math, reasoning, and image understanding. As the teacher model behind Llama 4 Scout and Llama 4 Maverick, Llama 4 Behemoth drives major advancements in model distillation, improving both efficiency and performance. Currently still in training, Behemoth is expected to redefine AI intelligence and multimodal processing once fully deployed.

Meta’s Llama 4 Maverick is a state-of-the-art multimodal AI model that packs 17 billion active parameters and 128 experts into a high-performance solution. Its performance surpasses other top models, including GPT-4o and Gemini 2.0 Flash, particularly in reasoning, coding, and image processing benchmarks. Llama 4 Maverick excels at understanding and generating text while grounding its responses in visual data, making it perfect for applications that require both types of information. This model strikes a balance between power and efficiency, offering top-tier AI capabilities at a fraction of the parameter size compared to larger models, making it a versatile tool for developers and enterprises alike.

Llama 4 Scout represents a leap forward in multimodal AI, featuring 17 billion active parameters and a groundbreaking 10 million token context length. With its ability to integrate both text and image data, Llama 4 Scout excels at tasks like multi-document summarization, complex reasoning, and image grounding. It delivers superior performance across various benchmarks and is particularly effective in applications requiring both language and visual comprehension. Scout's efficiency and advanced capabilities make it an ideal solution for developers and businesses looking for a versatile and powerful model to enhance their AI-driven projects.

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

NVIDIA NeMo Megatron is a robust framework specifically crafted for the training and deployment of large language models (LLMs) that can encompass billions to trillions of parameters. Functioning as a key element of the NVIDIA AI platform, it offers an efficient, cost-effective, and containerized solution for building and deploying LLMs. Designed with enterprise application development in mind, this framework utilizes advanced technologies derived from NVIDIA's research, presenting a comprehensive workflow that automates the distributed processing of data, supports the training of extensive custom models such as GPT-3, T5, and multilingual T5 (mT5), and facilitates model deployment for large-scale inference tasks. The process of implementing LLMs is made effortless through the provision of validated recipes and predefined configurations that optimize both training and inference phases. Furthermore, the hyperparameter optimization tool greatly aids model customization by autonomously identifying the best hyperparameter settings, which boosts performance during training and inference across diverse distributed GPU cluster environments. This innovative approach not only conserves valuable time but also guarantees that users can attain exceptional outcomes with reduced effort and increased efficiency. Ultimately, NVIDIA NeMo Megatron represents a significant advancement in the field of artificial intelligence, empowering developers to harness the full potential of LLMs with unparalleled ease.

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

Pixtral Large is a comprehensive multimodal model developed by Mistral AI, boasting an impressive 124 billion parameters that build upon their earlier Mistral Large 2 framework. The architecture consists of a 123-billion-parameter multimodal decoder paired with a 1-billion-parameter vision encoder, which empowers the model to adeptly interpret diverse content such as documents, graphs, and natural images while maintaining excellent text understanding. Furthermore, Pixtral Large can accommodate a substantial context window of 128,000 tokens, enabling it to process at least 30 high-definition images simultaneously with impressive efficiency. Its performance has been validated through exceptional results in benchmarks like MathVista, DocVQA, and VQAv2, surpassing competitors like GPT-4o and Gemini-1.5 Pro. The model is made available for research and educational use under the Mistral Research License, while also offering a separate Mistral Commercial License for businesses. This dual licensing approach enhances its appeal, making Pixtral Large not only a powerful asset for academic research but also a significant contributor to advancements in commercial applications. As a result, the model stands out as a multifaceted tool capable of driving innovation across various fields.

CodeGemma is an impressive collection of efficient and adaptable models that can handle a variety of coding tasks, such as middle code completion, code generation, natural language processing, mathematical reasoning, and instruction following. It includes three unique model variants: a 7B pre-trained model intended for code completion and generation using existing code snippets, a fine-tuned 7B version for converting natural language queries into code while following instructions, and a high-performing 2B pre-trained model that completes code at speeds up to twice as fast as its counterparts. Whether you are filling in lines, creating functions, or assembling complete code segments, CodeGemma is designed to assist you in any environment, whether local or utilizing Google Cloud services. With its training grounded in a vast dataset of 500 billion tokens, primarily in English and taken from web sources, mathematics, and programming languages, CodeGemma not only improves the syntactical precision of the code it generates but also guarantees its semantic accuracy, resulting in fewer errors and a more efficient debugging process. Beyond just functionality, this powerful tool consistently adapts and improves, making coding more accessible and streamlined for developers across the globe, thereby fostering a more innovative programming landscape. As the technology advances, users can expect even more enhancements in terms of speed and accuracy.

CodeQwen acts as the programming equivalent of Qwen, a collection of large language models developed by the Qwen team at Alibaba Cloud. This model, which is based on a transformer architecture that operates purely as a decoder, has been rigorously pre-trained on an extensive dataset of code. It is known for its strong capabilities in code generation and has achieved remarkable results on various benchmarking assessments. CodeQwen can understand and generate long contexts of up to 64,000 tokens and supports 92 programming languages, excelling in tasks such as text-to-SQL queries and debugging operations. Interacting with CodeQwen is uncomplicated; users can start a dialogue with just a few lines of code leveraging transformers. The interaction is rooted in creating the tokenizer and model using pre-existing methods, utilizing the generate function to foster communication through the chat template specified by the tokenizer. Adhering to our established guidelines, we adopt the ChatML template specifically designed for chat models. This model efficiently completes code snippets according to the prompts it receives, providing responses that require no additional formatting changes, thereby significantly enhancing the user experience. The smooth integration of these components highlights the adaptability and effectiveness of CodeQwen in addressing a wide range of programming challenges, making it an invaluable tool for developers.

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

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

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

OpenELM is a series of open-source language models developed by Apple. Utilizing a layer-wise scaling method, it successfully allocates parameters throughout the layers of the transformer model, leading to enhanced accuracy compared to other open language models of a comparable scale. The model is trained on publicly available datasets and is recognized for delivering exceptional performance given its size. Moreover, OpenELM signifies a major step forward in the quest for efficient language models within the open-source community, showcasing Apple's commitment to innovation in this field. Its development not only highlights technical advancements but also emphasizes the importance of accessibility in AI research.

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

Mistral Saba is a sophisticated model featuring 24 billion parameters, developed from meticulously curated datasets originating from the Middle East and South Asia. It surpasses the performance of larger models—those exceeding five times its parameter count—by providing accurate and relevant responses while being remarkably faster and more economical. Moreover, it acts as a solid foundation for the development of highly tailored regional applications. Users can access this model via an API, and it can also be deployed locally, addressing specific security needs of customers. Like the newly launched Mistral Small 3, it is designed to be lightweight enough for operation on single-GPU systems, achieving impressive response rates of over 150 tokens per second. Mistral Saba embodies the rich cultural interconnections between the Middle East and South Asia, offering support for Arabic as well as a variety of Indian languages, with particular expertise in South Indian dialects such as Tamil. This broad linguistic capability enhances its flexibility for multinational use in these interconnected regions. Furthermore, the architecture of the model promotes seamless integration into a wide array of platforms, significantly improving its applicability across various sectors and ensuring that it meets the diverse needs of its users.

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

The Mixtral 8x22B is our latest open model, setting a new standard in performance and efficiency within the realm of AI. By utilizing a sparse Mixture-of-Experts (SMoE) architecture, it activates only 39 billion parameters out of a total of 141 billion, leading to remarkable cost efficiency relative to its size. Moreover, it exhibits proficiency in several languages, such as English, French, Italian, German, and Spanish, alongside strong capabilities in mathematics and programming. Its native function calling feature, paired with the constrained output mode used on la Plateforme, greatly aids in application development and the large-scale modernization of technology infrastructures. The model boasts a context window of up to 64,000 tokens, allowing for precise information extraction from extensive documents. We are committed to designing models that optimize cost efficiency, thus providing exceptional performance-to-cost ratios compared to alternatives available in the market. As a continuation of our open model lineage, the Mixtral 8x22B's sparse activation patterns enhance its speed, making it faster than any similarly sized dense 70 billion model available. Additionally, its pioneering design and performance metrics make it an outstanding option for developers in search of high-performance AI solutions, further solidifying its position as a vital asset in the fast-evolving tech landscape.

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

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

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

ALBERT is a groundbreaking Transformer model that employs self-supervised learning and has been pretrained on a vast array of English text. Its automated mechanisms remove the necessity for manual data labeling, allowing the model to generate both inputs and labels straight from raw text. The training of ALBERT revolves around two main objectives. The first is Masked Language Modeling (MLM), which randomly masks 15% of the words in a sentence, prompting the model to predict the missing words. This approach stands in contrast to RNNs and autoregressive models like GPT, as it allows for the capture of bidirectional representations in sentences. The second objective, Sentence Ordering Prediction (SOP), aims to ascertain the proper order of two adjacent segments of text during the pretraining process. By implementing these strategies, ALBERT significantly improves its comprehension of linguistic context and structure. This innovative architecture positions ALBERT as a strong contender in the realm of natural language processing, pushing the boundaries of what language models can achieve.

Samsung Gauss is a groundbreaking AI model developed by Samsung Electronics, intended to function as a large language model trained on a vast selection of text and code. This sophisticated model possesses the ability to generate coherent text, translate multiple languages, create a variety of artistic works, and offer informative answers to a broad spectrum of questions. While Samsung Gauss is still undergoing enhancements, it has already proven its skill in numerous tasks, including: Adhering to directives and satisfying requests with thoughtful attention. Providing comprehensive and insightful answers to inquiries, no matter how intricate or unique they may be. Generating an array of creative outputs, such as poems, programming code, scripts, musical pieces, emails, and letters. For example, Samsung Gauss is capable of translating text between many languages, including English, French, German, Spanish, Chinese, Japanese, and Korean, and can also produce functional code tailored to specific programming requirements. Moreover, as its development progresses, the potential uses of Samsung Gauss are expected to grow extensively, promising exciting new possibilities for users in various fields.

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

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

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