Top Qwen2.5-1M Alternatives

Sky-T1-32B-Preview represents a groundbreaking open-source reasoning model developed by the NovaSky team at UC Berkeley's Sky Computing Lab. It achieves performance levels similar to those of proprietary models like o1-preview across a range of reasoning and coding tests, all while being created for under $450, emphasizing its potential to provide advanced reasoning skills at a lower cost. Fine-tuned from Qwen2.5-32B-Instruct, this model was trained on a carefully selected dataset of 17,000 examples that cover diverse areas, including mathematics and programming. The training was efficiently completed in a mere 19 hours with the aid of eight H100 GPUs using DeepSpeed Zero-3 offloading technology. Notably, every aspect of this project—spanning data, code, and model weights—is fully open-source, enabling both the academic and open-source communities to not only replicate but also enhance the model's functionalities. Such openness promotes a spirit of collaboration and innovation within the artificial intelligence research and development landscape, inviting contributions from various sectors. Ultimately, this initiative represents a significant step forward in making powerful AI tools more accessible to a wider audience.

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.

Qwen2 is a comprehensive array of advanced language models developed by the Qwen team at Alibaba Cloud. This collection includes various models that range from base to instruction-tuned versions, with parameters from 0.5 billion up to an impressive 72 billion, demonstrating both dense configurations and a Mixture-of-Experts architecture. The Qwen2 lineup is designed to surpass many earlier open-weight models, including its predecessor Qwen1.5, while also competing effectively against proprietary models across several benchmarks in domains such as language understanding, text generation, multilingual capabilities, programming, mathematics, and logical reasoning. Additionally, this cutting-edge series is set to significantly influence the artificial intelligence landscape, providing enhanced functionalities that cater to a wide array of applications. As such, the Qwen2 models not only represent a leap in technological advancement but also pave the way for future innovations in the field.

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.

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.

QwQ-Max-Preview represents an advanced AI model built on the Qwen2.5-Max architecture, designed to demonstrate exceptional abilities in areas such as intricate reasoning, mathematical challenges, programming tasks, and agent-based activities. This preview highlights its improved functionalities across various general-domain applications, showcasing a strong capability to handle complex workflows effectively. Set to be launched as open-source software under the Apache 2.0 license, QwQ-Max-Preview is expected to feature substantial enhancements and refinements in its final version. In addition to its technical advancements, the model plays a vital role in fostering a more inclusive AI landscape, which is further supported by the upcoming release of the Qwen Chat application and streamlined model options like QwQ-32B, aimed at developers seeking local deployment alternatives. This initiative not only enhances accessibility for a broader audience but also stimulates creativity and progress within the AI community, ensuring that diverse voices can contribute to the field's evolution. The commitment to open-source principles is likely to inspire further exploration and collaboration among developers.

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.

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.

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.

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.

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.

Smaug-72B stands out as a powerful open-source large language model (LLM) with several noteworthy characteristics: Outstanding Performance: It leads the Hugging Face Open LLM leaderboard, surpassing models like GPT-3.5 across various assessments, showcasing its adeptness in understanding, responding to, and producing text that closely mimics human language. Open Source Accessibility: Unlike many premium LLMs, Smaug-72B is available for public use and modification, fostering collaboration and innovation within the artificial intelligence community. Focus on Reasoning and Mathematics: This model is particularly effective in tackling reasoning and mathematical tasks, a strength stemming from targeted fine-tuning techniques employed by its developers at Abacus AI. Based on Qwen-72B: Essentially, it is an enhanced iteration of the robust LLM Qwen-72B, originally released by Alibaba, which contributes to its superior performance. In conclusion, Smaug-72B represents a significant progression in the field of open-source artificial intelligence, serving as a crucial asset for both developers and researchers. Its distinctive capabilities not only elevate its prominence but also play an integral role in the continual advancement of AI technology, inspiring further exploration and development in this dynamic field.

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.

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.

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.

This repository presents the research preview for LongLLaMA, an innovative large language model capable of handling extensive contexts, reaching up to 256,000 tokens or potentially even more. Built on the OpenLLaMA framework, LongLLaMA has been fine-tuned using the Focused Transformer (FoT) methodology. The foundational code for this model comes from Code Llama. We are excited to introduce a smaller 3B base version of the LongLLaMA model, which is not instruction-tuned, and it will be released under an open license (Apache 2.0). Accompanying this release is inference code that supports longer contexts, available on Hugging Face. The model's weights are designed to effortlessly integrate with existing systems tailored for shorter contexts, particularly those that accommodate up to 2048 tokens. In addition to these features, we provide evaluation results and comparisons to the original OpenLLaMA models, thus offering a thorough insight into LongLLaMA's effectiveness in managing long-context tasks. This advancement marks a significant step forward in the field of language models, enabling more sophisticated applications and research opportunities.

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.

The Qwen2.5-VL represents a significant advancement in the Qwen vision-language model series, offering substantial enhancements over the earlier version, Qwen2-VL. This sophisticated model showcases remarkable skills in visual interpretation, capable of recognizing a wide variety of elements in images, including text, charts, and numerous graphical components. Acting as an interactive visual assistant, it possesses the ability to reason and adeptly utilize tools, making it ideal for applications that require interaction on both computers and mobile devices. Additionally, Qwen2.5-VL excels in analyzing lengthy videos, being able to pinpoint relevant segments within those that exceed one hour in duration. It also specializes in precisely identifying objects in images, providing bounding boxes or point annotations, and generates well-organized JSON outputs detailing coordinates and attributes. The model is designed to output structured data for various document types, such as scanned invoices, forms, and tables, which proves especially beneficial for sectors like finance and commerce. Available in both base and instruct configurations across 3B, 7B, and 72B models, Qwen2.5-VL is accessible on platforms like Hugging Face and ModelScope, broadening its availability for developers and researchers. Furthermore, this model not only enhances the realm of vision-language processing but also establishes a new benchmark for future innovations in this area, paving the way for even more sophisticated applications.

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.

Mistral AI has introduced two advanced models tailored for on-device computing and edge applications, collectively known as "les Ministraux": Ministral 3B and Ministral 8B. These models are particularly remarkable for their abilities in knowledge retention, commonsense reasoning, function-calling, and overall operational efficiency, all while being under the 10B parameter threshold. With support for an impressive context length of up to 128k, they cater to a wide array of applications, including on-device translation, offline smart assistants, local analytics, and autonomous robotics. A standout feature of the Ministral 8B is its incorporation of an interleaved sliding-window attention mechanism, which significantly boosts both the speed and memory efficiency during inference. Both models excel in acting as intermediaries in intricate multi-step workflows, adeptly managing tasks such as input parsing, task routing, and API interactions according to user intentions while keeping latency and operational costs to a minimum. Benchmark results indicate that les Ministraux consistently outperform comparable models across numerous tasks, further cementing their competitive edge in the market. As of October 16, 2024, these innovative models are accessible to developers and businesses, with the Ministral 8B priced competitively at $0.1 per million tokens used. This pricing model promotes accessibility for users eager to incorporate sophisticated AI functionalities into their projects, potentially revolutionizing how AI is utilized in everyday applications.

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.

Qwen2-VL stands as the latest and most sophisticated version of vision-language models in the Qwen lineup, enhancing the groundwork laid by Qwen-VL. This upgraded model demonstrates exceptional abilities, including: Delivering top-tier performance in understanding images of various resolutions and aspect ratios, with Qwen2-VL particularly shining in visual comprehension challenges such as MathVista, DocVQA, RealWorldQA, and MTVQA, among others. Handling videos longer than 20 minutes, which allows for high-quality video question answering, engaging conversations, and innovative content generation. Operating as an intelligent agent that can control devices such as smartphones and robots, Qwen2-VL employs its advanced reasoning abilities and decision-making capabilities to execute automated tasks triggered by visual elements and written instructions. Offering multilingual capabilities to serve a worldwide audience, Qwen2-VL is now adept at interpreting text in several languages present in images, broadening its usability and accessibility for users from diverse linguistic backgrounds. Furthermore, this extensive functionality positions Qwen2-VL as an adaptable resource for a wide array of applications across various sectors.

InstructGPT is an accessible framework that facilitates the development of language models designed to generate natural language instructions from visual cues. Utilizing a generative pre-trained transformer (GPT) in conjunction with the sophisticated object detection features of Mask R-CNN, it effectively recognizes items within images and constructs coherent natural language narratives. This framework is crafted for flexibility across a range of industries, such as robotics, gaming, and education; for example, it can assist robots in carrying out complex tasks through spoken directions or aid learners by providing comprehensive accounts of events or processes. Moreover, InstructGPT's ability to merge visual comprehension with verbal communication significantly improves interactions across various applications, making it a valuable tool for enhancing user experiences. Its potential to innovate solutions in diverse fields continues to grow, opening up new possibilities for how we engage with technology.

Yi-Large is a cutting-edge proprietary large language model developed by 01.AI, boasting an impressive context length of 32,000 tokens and a pricing model set at $2 per million tokens for both input and output. Celebrated for its exceptional capabilities in natural language processing, common-sense reasoning, and multilingual support, it stands out in competition with leading models like GPT-4 and Claude3 in diverse assessments. The model excels in complex tasks that demand deep inference, precise prediction, and thorough language understanding, making it particularly suitable for applications such as knowledge retrieval, data classification, and the creation of conversational chatbots that closely resemble human communication. Utilizing a decoder-only transformer architecture, Yi-Large integrates advanced features such as pre-normalization and Group Query Attention, having been trained on a vast, high-quality multilingual dataset to optimize its effectiveness. Its versatility and cost-effective pricing make it a powerful contender in the realm of artificial intelligence, particularly for organizations aiming to adopt AI technologies on a worldwide scale. Furthermore, its adaptability across various applications highlights its potential to transform how businesses utilize language models for an array of requirements, paving the way for innovative solutions in the industry. Thus, Yi-Large not only meets but also exceeds expectations, solidifying its role as a pivotal tool in the advancements of AI-driven communication.

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

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.

The Claude 3.5 Sonnet introduces a remarkable benchmark in the realm of graduate-level reasoning (GPQA), undergraduate knowledge (MMLU), and coding abilities (HumanEval). This model showcases impressive improvements in grasping nuances, wit, and complex instructions, thriving in generating top-notch content that remains both authentic and engaging. Significantly, Claude 3.5 Sonnet operates at twice the speed of its earlier version, Claude 3 Opus, leading to superior efficiency and performance. This boost in operational speed, combined with its cost-effective pricing, makes Claude 3.5 Sonnet an outstanding choice for tackling intricate tasks, including context-sensitive customer support and orchestrating multi-step processes. It is freely available on Claude.ai and the Claude iOS app, with additional perks for subscribers of the Claude Pro and Team plans, such as elevated rate limits. Additionally, users can access the model through the Anthropic API, Amazon Bedrock, and Google Cloud's Vertex AI, which come with a pricing structure of $3 per million input tokens and $15 per million output tokens. With a generous context window of 200K tokens, the extensive capabilities of Claude 3.5 Sonnet render it an invaluable resource for businesses and developers, ensuring they can leverage advanced AI for a variety of applications. Its versatility and robust performance make it an essential tool in the competitive landscape of AI technology.

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.

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.

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.

In tribute to Cleopatra, whose dramatic story ended with the fateful encounter with a snake, we proudly present Codestral Mamba, a Mamba2 language model tailored for code generation and made available under an Apache 2.0 license. Codestral Mamba marks a pivotal step forward in our commitment to pioneering and refining innovative architectures. This model is available for free use, modification, and distribution, and we hope it will pave the way for new discoveries in architectural research. The Mamba models stand out due to their linear time inference capabilities, coupled with a theoretical ability to manage sequences of infinite length. This unique characteristic allows users to engage with the model seamlessly, delivering quick responses irrespective of the input size. Such remarkable efficiency is especially beneficial for boosting coding productivity; hence, we have integrated advanced coding and reasoning abilities into this model, ensuring it can compete with top-tier transformer-based models. As we push the boundaries of innovation, we are confident that Codestral Mamba will not only advance coding practices but also inspire new generations of developers. This exciting release underscores our dedication to fostering creativity and productivity within the tech community.

Fullmoon stands out as a groundbreaking, open-source app that empowers users to interact directly with large language models right on their personal devices, emphasizing user privacy and offline capabilities. Specifically optimized for Apple silicon, it operates efficiently across a range of platforms, including iOS, iPadOS, macOS, and visionOS, ensuring a cohesive user experience. Users can tailor their interactions by adjusting themes, fonts, and system prompts, and the app’s integration with Apple’s Shortcuts further boosts productivity. Importantly, Fullmoon supports models like Llama-3.2-1B-Instruct-4bit and Llama-3.2-3B-Instruct-4bit, facilitating robust AI engagements without the need for an internet connection. This unique combination of features positions Fullmoon as a highly adaptable tool for individuals seeking to leverage AI technology conveniently and securely. Additionally, the app's emphasis on customization allows users to create an environment that perfectly suits their preferences and needs.

We are excited to unveil an open-source AI model that offers the ability to be fine-tuned, distilled, and deployed across a wide range of platforms. Our latest instruction-tuned model is available in three different sizes: 8B, 70B, and 405B, allowing you to select an option that best fits your unique needs. The open ecosystem we provide accelerates your development journey with a variety of customized product offerings tailored to meet your specific project requirements. You can choose between real-time inference and batch inference services, depending on what your project requires, giving you added flexibility to optimize performance. Furthermore, downloading model weights can significantly enhance cost efficiency per token while you fine-tune the model for your application. To further improve performance, you can leverage synthetic data and seamlessly deploy your solutions either on-premises or in the cloud. By taking advantage of Llama system components, you can also expand the model's capabilities through the use of zero-shot tools and retrieval-augmented generation (RAG), promoting more agentic behaviors in your applications. Utilizing the extensive 405B high-quality data enables you to fine-tune specialized models that cater specifically to various use cases, ensuring that your applications function at their best. In conclusion, this empowers developers to craft innovative solutions that not only meet efficiency standards but also drive effectiveness in their respective domains, leading to a significant impact on the technology landscape.

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.

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.

Aya stands as a pioneering open-source generative large language model that supports a remarkable 101 languages, far exceeding the offerings of other open-source alternatives. This expansive language support allows researchers to harness the powerful capabilities of LLMs for numerous languages and cultures that have frequently been neglected by dominant models in the industry. Alongside the launch of the Aya model, we are also unveiling the largest multilingual instruction fine-tuning dataset, which contains 513 million entries spanning 114 languages. This extensive dataset is enriched with distinctive annotations from native and fluent speakers around the globe, ensuring that AI technology can address the needs of a diverse international community that has often encountered obstacles to access. Therefore, Aya not only broadens the horizons of multilingual AI but also fosters inclusivity among various linguistic groups, paving the way for future advancements in the field. By creating an environment where linguistic diversity is celebrated, Aya stands to inspire further innovations that can bridge gaps in communication and understanding.

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

Mistral Small 3.1 is an advanced, multimodal, and multilingual AI model that has been made available under the Apache 2.0 license. Building upon the previous Mistral Small 3, this updated version showcases improved text processing abilities and enhanced multimodal understanding, with the capacity to handle an extensive context window of up to 128,000 tokens. It outperforms comparable models like Gemma 3 and GPT-4o Mini, reaching remarkable inference rates of 150 tokens per second. Designed for versatility, Mistral Small 3.1 excels in various applications, including instruction adherence, conversational interaction, visual data interpretation, and executing functions, making it suitable for both commercial and individual AI uses. Its efficient architecture allows it to run smoothly on hardware configurations such as a single RTX 4090 or a Mac with 32GB of RAM, enabling on-device operations. Users have the option to download the model from Hugging Face and explore its features via Mistral AI's developer playground, while it is also embedded in services like Google Cloud Vertex AI and accessible on platforms like NVIDIA NIM. This extensive flexibility empowers developers to utilize its advanced capabilities across a wide range of environments and applications, thereby maximizing its potential impact in the AI landscape. Furthermore, Mistral Small 3.1's innovative design ensures that it remains adaptable to future technological advancements.

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.

We provide rapid and accurate AI models tailored for effective use in production settings. Our inference API is engineered for maximum uptime, harnessing the latest NVIDIA GPUs to deliver peak performance. Additionally, we have compiled a diverse array of high-quality open-source natural language processing (NLP) models sourced from the community, making them easily accessible for your projects. You can also customize your own models, including GPT-J, or upload your proprietary models for smooth integration into production. Through a user-friendly dashboard, you can swiftly upload or fine-tune AI models, enabling immediate deployment without the complexities of managing factors like memory constraints, uptime, or scalability. You have the freedom to upload an unlimited number of models and deploy them as necessary, fostering a culture of continuous innovation and adaptability to meet your dynamic needs. This comprehensive approach provides a solid foundation for utilizing AI technologies effectively in your initiatives, promoting growth and efficiency in your workflows.

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.

Falcon 3 is an open-source large language model introduced by the Technology Innovation Institute (TII), with the goal of expanding access to cutting-edge AI technologies. It is engineered for optimal efficiency, making it suitable for use on lightweight devices such as laptops while still delivering impressive performance. The Falcon 3 collection consists of four scalable models, each tailored for specific uses and capable of supporting a variety of languages while keeping resource use to a minimum. This latest edition in TII's lineup of language models establishes a new standard for reasoning, language understanding, following instructions, coding, and solving mathematical problems. By combining strong performance with resource efficiency, Falcon 3 aims to make advanced AI more accessible, enabling users from diverse fields to take advantage of sophisticated technology without the need for significant computational resources. Additionally, this initiative not only enhances the skills of individual users but also promotes innovation across various industries by providing easy access to advanced AI tools, ultimately transforming how technology is utilized in everyday practices.

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.

A community-driven marketplace empowers developers to design specialized expert LLMs that excel in technical domains, outperforming conventional AI systems in accuracy and efficiency. These Lunes continuously enhance their performance by pulling in data from a diverse array of technical knowledge resources, such as GitHub repositories and official documentation, which significantly minimizes errors in technical questions. Users benefit from reference materials similar to those available through Perplexity, while also gaining access to a variety of Lunes crafted by other contributors, spanning from those based on open-source tools to well-organized compilations of tech blog content. Additionally, individuals have the opportunity to create their own Lune by curating resources, including their own projects, to boost their visibility within the community. Our API integrates effortlessly with OpenAI’s framework, ensuring compatibility with applications like Cursor, Continue, and other tools that leverage OpenAI-compatible models. The transition of conversations from your IDE to Lune Web is seamless, greatly enhancing user interaction. Furthermore, you can earn rewards for contributions made during discussions, with compensation for every piece of feedback that receives approval. Alternatively, you might choose to launch a public Lune, allowing you to monetize it based on its popularity and the level of user engagement it garners. This groundbreaking model not only encourages collaboration among users but also incentivizes them for their knowledge and innovative contributions, fostering a dynamic ecosystem of shared expertise. Ultimately, this approach redefines how technical knowledge is shared and developed within the community.

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.

GPT-4.1 mini is a more lightweight version of the GPT-4.1 model, designed to offer faster response times and reduced latency, making it an excellent choice for applications that require real-time AI interaction. Despite its smaller size, GPT-4.1 mini retains the core capabilities of the full GPT-4.1 model, including handling up to 1 million tokens of context and excelling at tasks like coding and instruction following. With significant improvements in efficiency and cost-effectiveness, GPT-4.1 mini is ideal for developers and businesses looking for powerful, low-latency AI solutions.

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.

GPT-4.1 is a cutting-edge AI model from OpenAI, offering major advancements in performance, especially for tasks requiring complex reasoning and large context comprehension. With the ability to process up to 1 million tokens, GPT-4.1 delivers more accurate and reliable results for tasks like software coding, multi-document analysis, and real-time problem-solving. Compared to its predecessors, GPT-4.1 excels in instruction following and coding tasks, offering higher efficiency and improved performance at a reduced cost.

Models designed to follow instructions, such as GPT-3.5 (text-DaVinci-003), ChatGPT, Claude, and Bing Chat, have experienced remarkable improvements in their functionalities, resulting in a notable increase in their utilization by users in various personal and professional environments. While their rising popularity and integration into everyday activities is evident, these models still face significant challenges, including the potential to spread misleading information, perpetuate detrimental stereotypes, and utilize offensive language. Addressing these pressing concerns necessitates active engagement from researchers and academics to further investigate these models. However, the pursuit of research on instruction-following models in academic circles has been complicated by the lack of accessible alternatives to proprietary systems like OpenAI’s text-DaVinci-003. To bridge this divide, we are excited to share our findings on Alpaca, an instruction-following language model that has been fine-tuned from Meta’s LLaMA 7B model, as we aim to enhance the dialogue and advancements in this domain. By shedding light on Alpaca, we hope to foster a deeper understanding of instruction-following models while providing researchers with a more attainable resource for their studies and explorations. This initiative marks a significant stride toward improving the overall landscape of instruction-following technologies.

Mistral AI has introduced two state-of-the-art models aimed at on-device computing and edge applications, collectively known as "les Ministraux": Ministral 3B and Ministral 8B. These advanced models set new benchmarks for knowledge, commonsense reasoning, function-calling, and efficiency in the sub-10B category. They offer remarkable flexibility for a variety of applications, from overseeing complex workflows to creating specialized task-oriented agents. With the capability to manage an impressive context length of up to 128k (currently supporting 32k on vLLM), Ministral 8B features a distinctive interleaved sliding-window attention mechanism that boosts both speed and memory efficiency during inference. Crafted for low-latency and compute-efficient applications, these models thrive in environments such as offline translation, internet-independent smart assistants, local data processing, and autonomous robotics. Additionally, when integrated with larger language models like Mistral Large, les Ministraux can serve as effective intermediaries, enhancing function-calling within detailed multi-step workflows. This synergy not only amplifies performance but also extends the potential of AI in edge computing, paving the way for innovative solutions in various fields. The introduction of these models marks a significant step forward in making advanced AI more accessible and efficient for real-world applications.