Alternatives to Olmo 2

Olmo 3 represents a comprehensive family of open models featuring variations with 7 billion and 32 billion parameters, offering exceptional capabilities in base performance, reasoning, instruction, and reinforcement learning, while also providing transparency throughout the model development process, which includes access to raw training datasets, intermediate checkpoints, training scripts, extended context support (with a window of 65,536 tokens), and provenance tools. The foundation of these models is built upon the Dolma 3 dataset, which comprises approximately 9 trillion tokens and utilizes a careful blend of web content, scientific papers, programming code, and lengthy documents; this thorough pre-training, mid-training, and long-context approach culminates in base models that undergo post-training enhancements through supervised fine-tuning, preference optimization, and reinforcement learning with accountable rewards, resulting in the creation of the Think and Instruct variants. Notably, the 32 billion Think model has been recognized as the most powerful fully open reasoning model to date, demonstrating performance that closely rivals that of proprietary counterparts in areas such as mathematics, programming, and intricate reasoning tasks, thereby marking a significant advancement in open model development. This innovation underscores the potential for open-source models to compete with traditional, closed systems in various complex applications.

Molmo represents a cutting-edge family of multimodal AI models crafted by the Allen Institute for AI (Ai2). These innovative models are specifically engineered to connect the divide between open-source and proprietary systems, ensuring they perform competitively across numerous academic benchmarks and assessments by humans. In contrast to many existing multimodal systems that depend on synthetic data sourced from proprietary frameworks, Molmo is exclusively trained on openly available data, which promotes transparency and reproducibility in AI research. A significant breakthrough in the development of Molmo is the incorporation of PixMo, a unique dataset filled with intricately detailed image captions gathered from human annotators who utilized speech-based descriptions, along with 2D pointing data that empowers the models to respond to inquiries with both natural language and non-verbal signals. This capability allows Molmo to engage with its surroundings in a more sophisticated manner, such as by pointing to specific objects within images, thereby broadening its potential applications in diverse fields, including robotics, augmented reality, and interactive user interfaces. Furthermore, the advancements made by Molmo set a new standard for future multimodal AI research and application development.

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.

Ai2 OLMoE is a completely open-source mixture-of-experts language model that operates entirely on-device, ensuring that you can experiment with the model in a private and secure manner. This application is designed to assist researchers in advancing on-device intelligence and to allow developers to efficiently prototype innovative AI solutions without the need for cloud connectivity. OLMoE serves as a highly efficient variant within the Ai2 OLMo model family. Discover the capabilities of state-of-the-art local models in performing real-world tasks, investigate methods to enhance smaller AI models, and conduct local tests of your own models utilizing our open-source codebase. Furthermore, you can seamlessly integrate OLMoE into various iOS applications, as the app prioritizes user privacy and security by functioning entirely on-device. Users can also easily share the outcomes of their interactions with friends or colleagues. Importantly, both the OLMoE model and the application code are fully open source, offering a transparent and collaborative approach to AI development. By leveraging this model, developers can contribute to the growing field of on-device AI while maintaining high standards of user privacy.

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.

Molmo 2 represents a cutting-edge suite of open vision-language models that come with completely accessible weights, training data, and code, thereby advancing the original Molmo series' capabilities in grounded image comprehension to encompass video and multiple image inputs. This evolution enables sophisticated video analysis, including pointing, tracking, dense captioning, and question-answering functionalities, all of which demonstrate robust spatial and temporal reasoning across frames. The suite consists of three distinct models: an 8 billion-parameter variant tailored for comprehensive video grounding and QA tasks, a 4 billion-parameter model that prioritizes efficiency, and a 7 billion-parameter model backed by Olmo, which features a fully open end-to-end architecture that includes the foundational language model. Notably, these new models surpass their predecessors on key benchmarks, setting unprecedented standards for open-model performance in image and video comprehension tasks. Furthermore, they often rival significantly larger proprietary systems while being trained on a much smaller dataset compared to similar closed models, showcasing their efficiency and effectiveness in the field. This impressive achievement marks a significant advancement in the accessibility and performance of AI-driven visual understanding technologies.

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.

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

The Mixtral 8x7B model is an advanced sparse mixture of experts (SMoE) system that boasts open weights and is released under the Apache 2.0 license. This model demonstrates superior performance compared to Llama 2 70B across various benchmarks while achieving inference speeds that are six times faster. Recognized as the leading open-weight model with a flexible licensing framework, Mixtral also excels in terms of cost-efficiency and performance. Notably, it competes with and often surpasses GPT-3.5 in numerous established benchmarks, highlighting its significance in the field. Its combination of accessibility, speed, and effectiveness makes it a compelling choice for developers seeking high-performing AI solutions.

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.

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

EXAONE Deep represents a collection of advanced language models that are enhanced for reasoning, created by LG AI Research, and come in sizes of 2.4 billion, 7.8 billion, and 32 billion parameters. These models excel in a variety of reasoning challenges, particularly in areas such as mathematics and coding assessments. Significantly, the EXAONE Deep 2.4B model outshines other models of its size, while the 7.8B variant outperforms both open-weight models of similar dimensions and the proprietary reasoning model known as OpenAI o1-mini. Furthermore, the EXAONE Deep 32B model competes effectively with top-tier open-weight models in the field. The accompanying repository offers extensive documentation that includes performance assessments, quick-start guides for leveraging EXAONE Deep models with the Transformers library, detailed explanations of quantized EXAONE Deep weights formatted in AWQ and GGUF, as well as guidance on how to run these models locally through platforms like llama.cpp and Ollama. Additionally, this resource serves to enhance user understanding and accessibility to the capabilities of EXAONE Deep models.

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.

Kimi K2 represents a cutting-edge series of open-source large language models utilizing a mixture-of-experts (MoE) architecture, with a staggering 1 trillion parameters in total and 32 billion activated parameters tailored for optimized task execution. Utilizing the Muon optimizer, it has been trained on a substantial dataset of over 15.5 trillion tokens, with its performance enhanced by MuonClip’s attention-logit clamping mechanism, resulting in remarkable capabilities in areas such as advanced knowledge comprehension, logical reasoning, mathematics, programming, and various agentic operations. Moonshot AI offers two distinct versions: Kimi-K2-Base, designed for research-level fine-tuning, and Kimi-K2-Instruct, which is pre-trained for immediate applications in chat and tool interactions, facilitating both customized development and seamless integration of agentic features. Comparative benchmarks indicate that Kimi K2 surpasses other leading open-source models and competes effectively with top proprietary systems, particularly excelling in coding and intricate task analysis. Furthermore, it boasts a generous context length of 128 K tokens, compatibility with tool-calling APIs, and support for industry-standard inference engines, making it a versatile option for various applications. The innovative design and features of Kimi K2 position it as a significant advancement in the field of artificial intelligence language processing.

OpenAI's o1 series introduces a new generation of AI models specifically developed to enhance reasoning skills. Among these models are o1-preview and o1-mini, which utilize an innovative reinforcement learning technique that encourages them to dedicate more time to "thinking" through various problems before delivering solutions. This method enables the o1 models to perform exceptionally well in intricate problem-solving scenarios, particularly in fields such as coding, mathematics, and science, and they have shown to surpass earlier models like GPT-4o in specific benchmarks. The o1 series is designed to address challenges that necessitate more profound cognitive processes, representing a pivotal advancement toward AI systems capable of reasoning in a manner similar to humans. As it currently stands, the series is still undergoing enhancements and assessments, reflecting OpenAI's commitment to refining these technologies further. The continuous development of the o1 models highlights the potential for AI to evolve and meet more complex demands in the future.

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.

Large language models, often requiring extensive computational resources for training over long periods, have demonstrated impressive proficiency in zero- and few-shot learning tasks. Due to the high investment needed for their development, replicating these models poses a significant challenge for many researchers. Furthermore, access to the few models available via API is limited, as users cannot obtain the complete model weights, complicating academic exploration. In response to this, we introduce Open Pre-trained Transformers (OPT), a collection of decoder-only pre-trained transformers ranging from 125 million to 175 billion parameters, which we intend to share comprehensively and responsibly with interested scholars. Our findings indicate that OPT-175B exhibits performance on par with GPT-3, yet it is developed with only one-seventh of the carbon emissions required for GPT-3's training. Additionally, we will provide a detailed logbook that outlines the infrastructure hurdles we encountered throughout the project, as well as code to facilitate experimentation with all released models, ensuring that researchers have the tools they need to explore this technology further.

The MAI-1 Preview marks the debut of Microsoft AI's fully in-house developed foundation model, utilizing a mixture-of-experts architecture for streamlined performance. This model has undergone extensive training on around 15,000 NVIDIA H100 GPUs, equipping it to adeptly follow user instructions and produce relevant text responses for common inquiries, thus illustrating a prototype for future Copilot functionalities. Currently accessible for public testing on LMArena, MAI-1 Preview provides an initial look at the platform's direction, with plans to introduce select text-driven applications in Copilot over the next few weeks aimed at collecting user insights and enhancing its capabilities. Microsoft emphasizes its commitment to integrating its proprietary models, collaborations with partners, and advancements from the open-source sector to dynamically enhance user experiences through millions of distinct interactions every day. This innovative approach illustrates Microsoft's dedication to continuously evolving its AI offerings.

We are excited to unveil Codestral, our inaugural code generation model. This open-weight generative AI system is specifically crafted for tasks related to code generation, enabling developers to seamlessly write and engage with code via a unified instruction and completion API endpoint. As it becomes proficient in both programming languages and English, Codestral is poised to facilitate the creation of sophisticated AI applications tailored for software developers. With a training foundation that encompasses a wide array of over 80 programming languages—ranging from widely-used options like Python, Java, C, C++, JavaScript, and Bash to more niche languages such as Swift and Fortran—Codestral ensures a versatile support system for developers tackling various coding challenges and projects. Its extensive language capabilities empower developers to confidently navigate different coding environments, making Codestral an invaluable asset in the programming landscape.

Qwen3-Max represents Alibaba's cutting-edge large language model, featuring a staggering trillion parameters aimed at enhancing capabilities in tasks that require agency, coding, reasoning, and managing lengthy contexts. This model is an evolution of the Qwen3 series, leveraging advancements in architecture, training methods, and inference techniques; it integrates both thinker and non-thinker modes, incorporates a unique “thinking budget” system, and allows for dynamic mode adjustments based on task complexity. Capable of handling exceptionally lengthy inputs, processing hundreds of thousands of tokens, it also supports tool invocation and demonstrates impressive results across various benchmarks, including coding, multi-step reasoning, and agent evaluations like Tau2-Bench. While the initial version prioritizes instruction adherence in a non-thinking mode, Alibaba is set to introduce reasoning functionalities that will facilitate autonomous agent operations in the future. In addition to its existing multilingual capabilities and extensive training on trillions of tokens, Qwen3-Max is accessible through API interfaces that align seamlessly with OpenAI-style functionalities, ensuring broad usability across applications. This comprehensive framework positions Qwen3-Max as a formidable player in the realm of advanced artificial intelligence language models.

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

We are excited to announce the launch of our X1 large series of models. The most robust model from Giga ML is now accessible for both pre-training and fine-tuning in an on-premises environment. Thanks to our compatibility with Open AI, existing integrations with tools like long chain, llama-index, and others function effortlessly. You can also proceed with pre-training LLMs using specialized data sources such as industry-specific documents or company files. The landscape of large language models (LLMs) is rapidly evolving, creating incredible opportunities for advancements in natural language processing across multiple fields. Despite this growth, several significant challenges persist in the industry. At Giga ML, we are thrilled to introduce the X1 Large 32k model, an innovative on-premise LLM solution designed specifically to tackle these pressing challenges, ensuring that organizations can harness the full potential of LLMs effectively. With this launch, we aim to empower businesses to elevate their language processing capabilities.

CodeQwen serves as the coding counterpart to Qwen, which is a series of large language models created by the Qwen team at Alibaba Cloud. Built on a transformer architecture that functions solely as a decoder, this model has undergone extensive pre-training using a vast dataset of code. It showcases robust code generation abilities and demonstrates impressive results across various benchmarking tests. With the capacity to comprehend and generate long contexts of up to 64,000 tokens, CodeQwen accommodates 92 programming languages and excels in tasks such as text-to-SQL queries and debugging. Engaging with CodeQwen is straightforward—you can initiate a conversation with just a few lines of code utilizing transformers. The foundation of this interaction relies on constructing the tokenizer and model using pre-existing methods, employing the generate function to facilitate dialogue guided by the chat template provided by the tokenizer. In alignment with our established practices, we implement the ChatML template tailored for chat models. This model adeptly completes code snippets based on the prompts it receives, delivering responses without the need for any further formatting adjustments, thereby enhancing the user experience. The seamless integration of these elements underscores the efficiency and versatility of CodeQwen in handling diverse coding tasks.

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

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.

DeepSeek V3.1 stands as a revolutionary open-weight large language model, boasting an impressive 685-billion parameters and an expansive 128,000-token context window, which allows it to analyze extensive documents akin to 400-page books in a single invocation. This model offers integrated functionalities for chatting, reasoning, and code creation, all within a cohesive hybrid architecture that harmonizes these diverse capabilities. Furthermore, V3.1 accommodates multiple tensor formats, granting developers the versatility to enhance performance across various hardware setups. Preliminary benchmark evaluations reveal strong results, including a remarkable 71.6% on the Aider coding benchmark, positioning it competitively with or even superior to systems such as Claude Opus 4, while achieving this at a significantly reduced cost. Released under an open-source license on Hugging Face with little publicity, DeepSeek V3.1 is set to revolutionize access to advanced AI technologies, potentially disrupting the landscape dominated by conventional proprietary models. Its innovative features and cost-effectiveness may attract a wide range of developers eager to leverage cutting-edge AI in their projects.

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

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.

Teuken-7B is a multilingual language model that has been developed as part of the OpenGPT-X initiative, specifically tailored to meet the needs of Europe's varied linguistic environment. This model has been trained on a dataset where over half consists of non-English texts, covering all 24 official languages of the European Union, which ensures it performs well across these languages. A significant advancement in Teuken-7B is its unique multilingual tokenizer, which has been fine-tuned for European languages, leading to enhanced training efficiency and lower inference costs when compared to conventional monolingual tokenizers. Users can access two versions of the model: Teuken-7B-Base, which serves as the basic pre-trained version, and Teuken-7B-Instruct, which has received instruction tuning aimed at boosting its ability to respond to user requests. Both models are readily available on Hugging Face, fostering an environment of transparency and collaboration within the artificial intelligence community while also encouraging further innovation. The creation of Teuken-7B highlights a dedication to developing AI solutions that embrace and represent the rich diversity found across Europe.

DeepSeek-V3.2 is a highly optimized large language model engineered to balance top-tier reasoning performance with significant computational efficiency. It builds on DeepSeek's innovations by introducing DeepSeek Sparse Attention (DSA), a custom attention algorithm that reduces complexity and excels in long-context environments. The model is trained using a sophisticated reinforcement learning approach that scales post-training compute, enabling it to perform on par with GPT-5 and match the reasoning skill of Gemini-3.0-Pro. Its Speciale variant overachieves in demanding reasoning benchmarks and does not include tool-calling capabilities, making it ideal for deep problem-solving tasks. DeepSeek-V3.2 is also trained using an agentic synthesis pipeline that creates high-quality, multi-step interactive data to improve decision-making, compliance, and tool-integration skills. It introduces a new chat template design featuring explicit thinking sections, improved tool-calling syntax, and a dedicated developer role used strictly for search-agent workflows. Users can encode messages using provided Python utilities that convert OpenAI-style chat messages into the expected DeepSeek format. Fully open-source under the MIT license, DeepSeek-V3.2 is a flexible, cutting-edge model for researchers, developers, and enterprise AI teams.

GigaChat 3 Ultra redefines open-source scale by delivering a 702B-parameter frontier model purpose-built for Russian and multilingual understanding. Designed with a modern MoE architecture, it achieves the reasoning strength of giant dense models while using only a fraction of active parameters per generation step. Its massive 14T-token training corpus includes natural human text, curated multilingual sources, extensive STEM materials, and billions of high-quality synthetic examples crafted to boost logic, math, and programming skills. This model is not a derivative or retrained foreign LLM—it is a ground-up build engineered to capture cultural nuance, linguistic accuracy, and reliable long-context performance. GigaChat 3 Ultra integrates seamlessly with open-source tooling like vLLM, sglang, DeepSeek-class architectures, and HuggingFace-based training stacks. It supports advanced capabilities including a code interpreter, improved chat template, memory system, contextual search reformulation, and 128K context windows. Benchmarking shows clear improvements over previous GigaChat generations and competitive results against global leaders in coding, reasoning, and cross-domain tasks. Overall, GigaChat 3 Ultra empowers teams to explore frontier-scale AI without sacrificing transparency, customizability, or ecosystem compatibility.

GPT-4, or Generative Pre-trained Transformer 4, is a highly advanced unsupervised language model that is anticipated for release by OpenAI. As the successor to GPT-3, it belongs to the GPT-n series of natural language processing models and was developed using an extensive dataset comprising 45TB of text, enabling it to generate and comprehend text in a manner akin to human communication. Distinct from many conventional NLP models, GPT-4 operates without the need for additional training data tailored to specific tasks. It is capable of generating text or responding to inquiries by utilizing only the context it creates internally. Demonstrating remarkable versatility, GPT-4 can adeptly tackle a diverse array of tasks such as translation, summarization, question answering, sentiment analysis, and more, all without any dedicated task-specific training. This ability to perform such varied functions further highlights its potential impact on the field of artificial intelligence and natural language processing.

BERT is a significant language model that utilizes a technique for pre-training language representations. This pre-training process involves initially training BERT on an extensive dataset, including resources like Wikipedia. Once this foundation is established, the model can be utilized for diverse Natural Language Processing (NLP) applications, including tasks such as question answering and sentiment analysis. Additionally, by leveraging BERT alongside AI Platform Training, it becomes possible to train various NLP models in approximately half an hour, streamlining the development process for practitioners in the field. This efficiency makes it an appealing choice for developers looking to enhance their NLP capabilities.

DeepSeek-V3.2-Speciale is the most advanced reasoning-focused version of the DeepSeek-V3.2 family, designed to excel in mathematical, algorithmic, and logic-intensive tasks. It incorporates DeepSeek Sparse Attention (DSA), an efficient attention mechanism tailored for very long contexts, enabling scalable reasoning with minimal compute costs. The model undergoes a robust reinforcement learning pipeline that scales post-training compute to frontier levels, enabling performance that exceeds GPT-5 on internal evaluations. Its achievements include gold-medal-level solutions in IMO 2025, IOI 2025, ICPC World Finals, and CMO 2025, with final submissions publicly released for verification. Unlike the standard V3.2 model, the Speciale variant removes tool-calling capabilities to maximize focused reasoning output without external interactions. DeepSeek-V3.2-Speciale uses a revised chat template with explicit thinking blocks and system-level reasoning formatting. The repository includes encoding tools showing how to convert OpenAI-style chat messages into DeepSeek’s specialized input format. With its MIT license and 685B-parameter architecture, DeepSeek-V3.2-Speciale offers cutting-edge performance for academic research, competitive programming, and enterprise-level reasoning applications.

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

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

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.

ERNIE X1.1 is Baidu’s latest reasoning AI model, designed to raise the bar for accuracy, reliability, and action-oriented intelligence. Compared to ERNIE X1, it delivers a 34.8% boost in factual accuracy, a 12.5% improvement in instruction compliance, and a 9.6% gain in agentic behavior. Benchmarks show that it outperforms DeepSeek R1-0528 and matches the capabilities of advanced models such as GPT-5 and Gemini 2.5 Pro. The model builds upon ERNIE 4.5 with additional mid-training and post-training phases, reinforced by end-to-end reinforcement learning. This approach helps minimize hallucinations while ensuring closer alignment to user intent. The agentic upgrades allow it to plan, make decisions, and execute tasks more effectively than before. Users can access ERNIE X1.1 through ERNIE Bot, Wenxiaoyan, or via API on Baidu’s Qianfan platform. Altogether, the model delivers stronger reasoning capabilities for developers and enterprises that demand high-performance AI.

InstructGPT is a publicly available framework that enables the training of language models capable of producing natural language instructions based on visual stimuli. By leveraging a generative pre-trained transformer (GPT) model alongside the advanced object detection capabilities of Mask R-CNN, it identifies objects within images and formulates coherent natural language descriptions. This framework is tailored for versatility across various sectors, including robotics, gaming, and education; for instance, it can guide robots in executing intricate tasks through spoken commands or support students by offering detailed narratives of events or procedures. Furthermore, InstructGPT's adaptability allows it to bridge the gap between visual understanding and linguistic expression, enhancing interaction in numerous applications.

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.

We are thrilled to present DBRX, a versatile open LLM developed by Databricks. This innovative model achieves unprecedented performance on a variety of standard benchmarks, setting a new benchmark for existing open LLMs. Additionally, it equips both the open-source community and enterprises crafting their own LLMs with features that were once exclusive to proprietary model APIs; our evaluations indicate that it outperforms GPT-3.5 and competes effectively with Gemini 1.0 Pro. Notably, it excels as a code model, outperforming specialized counterparts like CodeLLaMA-70B in programming tasks, while also demonstrating its prowess as a general-purpose LLM. The remarkable quality of DBRX is complemented by significant enhancements in both training and inference efficiency. Thanks to its advanced fine-grained mixture-of-experts (MoE) architecture, DBRX elevates the efficiency of open models to new heights. In terms of inference speed, it can be twice as fast as LLaMA2-70B, and its total and active parameter counts are approximately 40% of those in Grok-1, showcasing its compact design without compromising capability. This combination of speed and size makes DBRX a game-changer in the landscape of open AI models.

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

Llama 4 Behemoth, with 288 billion active parameters, is Meta's flagship AI model, setting new standards for multimodal performance. Outpacing its predecessors like GPT-4.5 and Claude Sonnet 3.7, it leads the field in STEM benchmarks, offering cutting-edge results in tasks such as problem-solving and reasoning. Designed as the teacher model for the Llama 4 series, Behemoth drives significant improvements in model quality and efficiency through distillation. Although still in development, Llama 4 Behemoth is shaping the future of AI with its unparalleled intelligence, particularly in math, image, and multilingual tasks.

Qwen3 is a state-of-the-art large language model designed to revolutionize the way we interact with AI. Featuring both thinking and non-thinking modes, Qwen3 allows users to customize its response style, ensuring optimal performance for both complex reasoning tasks and quick inquiries. With the ability to support 119 languages, the model is suitable for international projects. The model's hybrid training approach, which involves over 36 trillion tokens, ensures accuracy across a variety of disciplines, from coding to STEM problems. Its integration with platforms such as Hugging Face, ModelScope, and Kaggle allows for easy adoption in both research and production environments. By enhancing multilingual support and incorporating advanced AI techniques, Qwen3 is designed to push the boundaries of AI-driven applications.

Pixtral Large is an expansive multimodal model featuring 124 billion parameters, crafted by Mistral AI and enhancing their previous Mistral Large 2 framework. This model combines a 123-billion-parameter multimodal decoder with a 1-billion-parameter vision encoder, allowing it to excel in the interpretation of various content types, including documents, charts, and natural images, all while retaining superior text comprehension abilities. With the capability to manage a context window of 128,000 tokens, Pixtral Large can efficiently analyze at least 30 high-resolution images at once. It has achieved remarkable results on benchmarks like MathVista, DocVQA, and VQAv2, outpacing competitors such as GPT-4o and Gemini-1.5 Pro. Available for research and educational purposes under the Mistral Research License, it also has a Mistral Commercial License for business applications. This versatility makes Pixtral Large a valuable tool for both academic research and commercial innovations.