Business Software for Hugging Face

Eigent is a next-generation open-source cowork desktop that enables users to build and manage an AI-powered workforce. It automates operational tasks by breaking them down into coordinated subtasks executed by multiple agents in parallel. This architecture allows Eigent to outperform traditional single-agent automation in speed, cost efficiency, and reliability. Users can fully customize their workforce by designing specialized worker nodes equipped with tailored tools. Modular MCP integration ensures agents can adapt to virtually any workflow or system. The platform is designed for local and private deployment, ensuring data never leaves the user’s environment. Developers and enterprises can run Eigent using their own API keys or local models for complete control. Eigent supports diverse automation scenarios including data scraping, reporting, ERP workflows, content generation, and system operations. Its performance has been validated by top rankings on industry benchmarks such as GAIA. By combining openness with advanced multi-agent intelligence, Eigent redefines how work gets automated on the desktop.

Axolotl is an innovative open-source tool crafted to enhance the fine-tuning process of a variety of AI models, accommodating numerous configurations and architectures. This platform empowers users to train models using diverse methods such as full fine-tuning, LoRA, QLoRA, ReLoRA, and GPTQ. Additionally, users have the flexibility to customize their configurations through straightforward YAML files or by employing command-line interface overrides, while also being able to load datasets in various formats, whether custom or pre-tokenized. Axolotl seamlessly integrates with cutting-edge technologies, including xFormers, Flash Attention, Liger kernel, RoPE scaling, and multipacking, and it is capable of operating on single or multiple GPUs using Fully Sharded Data Parallel (FSDP) or DeepSpeed. Whether run locally or in the cloud via Docker, it offers robust support for logging results and saving checkpoints to multiple platforms, ensuring users can easily track their progress. Ultimately, Axolotl aims to make the fine-tuning of AI models not only efficient but also enjoyable, all while maintaining a high level of functionality and scalability. With its user-friendly design, it invites both novices and experienced practitioners to explore the depths of AI model training.

Skott functions as an autonomous AI marketing agent that takes care of researching, writing, and posting content, which enables your team to dedicate more time to strategic planning and creative projects. It features a customizable user interface and workflow that delivers actionable insights to shape your strategy, helps you stay ahead of industry trends through real-time data, provides thorough competitive analysis, and offers audience insights to effectively customize your content. Skott shines in producing exceptional content, including impactful blog articles, captivating social media posts, and SEO-friendly writing, while ensuring a uniform brand voice across various platforms. Furthermore, it facilitates smooth publishing by allowing you to post across multiple channels with ease, maintain consistent formatting and optimization, automate scheduling tasks, and integrate seamlessly with leading blogging and social media platforms. In addition to these features, Skott presents a cost-effective solution, delivering high-quality marketing services that enhance your return on investment without the need for excessive spending or additional hires. With its robust functionality, Skott empowers your marketing efforts, ultimately driving growth and engagement for your brand.

Mistral Small 3.1 represents a cutting-edge, multimodal, and multilingual AI model that has been released under the Apache 2.0 license. This upgraded version builds on Mistral Small 3, featuring enhanced text capabilities and superior multimodal comprehension, while also accommodating an extended context window of up to 128,000 tokens. It demonstrates superior performance compared to similar models such as Gemma 3 and GPT-4o Mini, achieving impressive inference speeds of 150 tokens per second. Tailored for adaptability, Mistral Small 3.1 shines in a variety of applications, including instruction following, conversational support, image analysis, and function execution, making it ideal for both business and consumer AI needs. The model's streamlined architecture enables it to operate efficiently on hardware such as a single RTX 4090 or a Mac equipped with 32GB of RAM, thus supporting on-device implementations. Users can download it from Hugging Face and access it through Mistral AI's developer playground, while it is also integrated into platforms like Gemini Enterprise Agent Platform, with additional accessibility on NVIDIA NIM and more. This flexibility ensures that developers can leverage its capabilities across diverse environments and applications.

ML Console is an innovative web application that empowers users to develop robust machine learning models effortlessly, without the need for coding skills. It is tailored for a diverse range of users, including those in marketing, e-commerce, and large organizations, enabling them to construct AI models in under a minute. The application functions entirely in the browser, which keeps user data private and secure. Utilizing cutting-edge web technologies such as WebAssembly and WebGL, ML Console delivers training speeds that rival those of traditional Python-based approaches. Its intuitive interface streamlines the machine learning experience, making it accessible to individuals regardless of their expertise level in AI. Moreover, ML Console is available at no cost, removing obstacles for anyone interested in delving into the world of machine learning solutions. By democratizing access to powerful AI tools, it opens up new possibilities for innovation across various industries.

Pruna leverages generative AI technology to help businesses generate high-quality visual content swiftly and cost-effectively. It removes the conventional requirements for studios and manual editing processes, allowing brands to effortlessly create tailored and uniform images for advertising, product showcases, and online campaigns. This innovation significantly streamlines the content creation process, enhancing efficiency and creativity for various marketing needs.

Transformers is a versatile library that includes pretrained models for natural language processing, computer vision, audio, and multimodal tasks, facilitating both inference and training. With the Transformers library, you can effectively train models tailored to your specific data, create inference applications, and utilize large language models for text generation. Visit the Hugging Face Hub now to discover a suitable model and leverage Transformers to kickstart your projects immediately. This library provides a streamlined and efficient inference class that caters to various machine learning tasks, including text generation, image segmentation, automatic speech recognition, and document question answering, among others. Additionally, it features a robust trainer that incorporates advanced capabilities like mixed precision, torch.compile, and FlashAttention, making it ideal for both training and distributed training of PyTorch models. The library ensures rapid text generation through large language models and vision-language models, and each model is constructed from three fundamental classes (configuration, model, and preprocessor), allowing for quick deployment in either inference or training scenarios. Overall, Transformers empowers users with the tools needed to create sophisticated machine learning solutions with ease and efficiency.

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.

Flower is a federated learning framework that is open-source and aims to make the creation and implementation of machine learning models across distributed data sources more straightforward. By enabling the training of models on data stored on individual devices or servers without the need to transfer that data, it significantly boosts privacy and minimizes bandwidth consumption. The framework is compatible with an array of popular machine learning libraries such as PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, and XGBoost, and it works seamlessly with various cloud platforms including AWS, GCP, and Azure. Flower offers a high degree of flexibility with its customizable strategies and accommodates both horizontal and vertical federated learning configurations. Its architecture is designed for scalability, capable of managing experiments that involve tens of millions of clients effectively. Additionally, Flower incorporates features geared towards privacy preservation, such as differential privacy and secure aggregation, ensuring that sensitive data remains protected throughout the learning process. This comprehensive approach makes Flower a robust choice for organizations looking to leverage federated learning in their machine learning initiatives.

The Open Computer Agent is an AI assistant that operates within a web browser, created by Hugging Face, designed to automate tasks like web browsing, filling out forms, and retrieving information. Utilizing advanced vision-language models such as Qwen-VL, it mimics mouse and keyboard actions, allowing it to perform a variety of functions, from booking tickets to checking operating hours and navigating to locations. The agent can effectively identify and engage with various elements on web pages by analyzing their image coordinates. As part of the smolagents initiative by Hugging Face, it prioritizes both flexibility and transparency, providing an open-source framework for developers to explore, alter, and expand for specialized uses. Although still in the developmental phase and encountering certain obstacles, this agent signifies a pioneering shift toward AI functioning as a proactive digital assistant, adept at executing online tasks independently without requiring direct user involvement. Furthermore, its ongoing evolution may lead to even greater possibilities in automating complex web interactions in the future.

Devstral is a collaborative effort between Mistral AI and All Hands AI, resulting in an open-source large language model specifically tailored for software engineering. This model demonstrates remarkable proficiency in navigating intricate codebases, managing edits across numerous files, and addressing practical problems, achieving a notable score of 46.8% on the SWE-Bench Verified benchmark, which is superior to all other open-source models. Based on Mistral-Small-3.1, Devstral boasts an extensive context window supporting up to 128,000 tokens. It is designed for optimal performance on high-performance hardware setups, such as Macs equipped with 32GB of RAM or Nvidia RTX 4090 GPUs, and supports various inference frameworks including vLLM, Transformers, and Ollama. Released under the Apache 2.0 license, Devstral is freely accessible on platforms like Hugging Face, Ollama, Kaggle, Unsloth, and LM Studio, allowing developers to integrate its capabilities into their projects seamlessly. This model not only enhances productivity for software engineers but also serves as a valuable resource for anyone working with code.

BGE (BAAI General Embedding) serves as a versatile retrieval toolkit aimed at enhancing search capabilities and Retrieval-Augmented Generation (RAG) applications. It encompasses functionalities for inference, evaluation, and fine-tuning of embedding models and rerankers, aiding in the creation of sophisticated information retrieval systems. This toolkit features essential elements such as embedders and rerankers, which are designed to be incorporated into RAG pipelines, significantly improving the relevance and precision of search results. BGE accommodates a variety of retrieval techniques, including dense retrieval, multi-vector retrieval, and sparse retrieval, allowing it to adapt to diverse data types and retrieval contexts. Users can access the models via platforms like Hugging Face, and the toolkit offers a range of tutorials and APIs to help implement and customize their retrieval systems efficiently. By utilizing BGE, developers are empowered to construct robust, high-performing search solutions that meet their unique requirements, ultimately enhancing user experience and satisfaction. Furthermore, the adaptability of BGE ensures it can evolve alongside emerging technologies and methodologies in the data retrieval landscape.

Pinecone Rerank V0 is a cross-encoder model specifically designed to enhance precision in reranking tasks, thereby improving enterprise search and retrieval-augmented generation (RAG) systems. This model processes both queries and documents simultaneously, enabling it to assess fine-grained relevance and assign a relevance score ranging from 0 to 1 for each query-document pair. With a maximum context length of 512 tokens, it ensures that the quality of ranking is maintained. In evaluations based on the BEIR benchmark, Pinecone Rerank V0 stood out by achieving the highest average NDCG@10, surpassing other competing models in 6 out of 12 datasets. Notably, it achieved an impressive 60% increase in performance on the Fever dataset when compared to Google Semantic Ranker, along with over 40% improvement on the Climate-Fever dataset against alternatives like cohere-v3-multilingual and voyageai-rerank-2. Accessible via Pinecone Inference, this model is currently available to all users in a public preview, allowing for broader experimentation and feedback. Its design reflects an ongoing commitment to innovation in search technology, making it a valuable tool for organizations seeking to enhance their information retrieval capabilities.

RankGPT is a Python toolkit specifically crafted to delve into the application of generative Large Language Models (LLMs), such as ChatGPT and GPT-4, for the purpose of relevance ranking within Information Retrieval (IR). It presents innovative techniques, including instructional permutation generation and a sliding window strategy, which help LLMs to efficiently rerank documents. Supporting a diverse array of LLMs—including GPT-3.5, GPT-4, Claude, Cohere, and Llama2 through LiteLLM—RankGPT offers comprehensive modules for retrieval, reranking, evaluation, and response analysis, thereby streamlining end-to-end processes. Additionally, the toolkit features a module dedicated to the in-depth analysis of input prompts and LLM outputs, effectively tackling reliability issues associated with LLM APIs and the non-deterministic nature of Mixture-of-Experts (MoE) models. Furthermore, it is designed to work with multiple backends, such as SGLang and TensorRT-LLM, making it compatible with a broad spectrum of LLMs. Among its resources, RankGPT's Model Zoo showcases various models, including LiT5 and MonoT5, which are conveniently hosted on Hugging Face, allowing users to easily access and implement them in their projects. Overall, RankGPT serves as a versatile and powerful toolkit for researchers and developers aiming to enhance the effectiveness of information retrieval systems through advanced LLM techniques.

HumanSignal's Label Studio Enterprise is a versatile platform crafted to produce high-quality labeled datasets and assess model outputs with oversight from human evaluators. This platform accommodates the labeling and evaluation of diverse data types, including images, videos, audio, text, and time series, all within a single interface. Users can customize their labeling environments through pre-existing templates and robust plugins, which allows for the adaptation of user interfaces and workflows to meet specific requirements. Moreover, Label Studio Enterprise integrates effortlessly with major cloud storage services and various ML/AI models, thus streamlining processes such as pre-annotation, AI-assisted labeling, and generating predictions for model assessment. The innovative Prompts feature allows users to utilize large language models to quickly create precise predictions, facilitating the rapid labeling of thousands of tasks. Its capabilities extend to multiple labeling applications, encompassing text classification, named entity recognition, sentiment analysis, summarization, and image captioning, making it an essential tool for various industries. Additionally, the platform's user-friendly design ensures that teams can efficiently manage their data labeling projects while maintaining high standards of accuracy.

FriendliAI serves as an advanced generative AI infrastructure platform that delivers rapid, efficient, and dependable inference solutions tailored for production settings. The platform is equipped with an array of tools and services aimed at refining the deployment and operation of large language models (LLMs) alongside various generative AI tasks on a large scale. Among its key features is Friendli Endpoints, which empowers users to create and implement custom generative AI models, thereby reducing GPU expenses and hastening AI inference processes. Additionally, it facilitates smooth integration with well-known open-source models available on the Hugging Face Hub, ensuring exceptionally fast and high-performance inference capabilities. FriendliAI incorporates state-of-the-art technologies, including Iteration Batching, the Friendli DNN Library, Friendli TCache, and Native Quantization, all of which lead to impressive cost reductions (ranging from 50% to 90%), a significant decrease in GPU demands (up to 6 times fewer GPUs), enhanced throughput (up to 10.7 times), and a marked decrease in latency (up to 6.2 times). With its innovative approach, FriendliAI positions itself as a key player in the evolving landscape of generative AI solutions.

ZenCtrl is an innovative, open-source AI image generation toolkit created by Fotographer AI, aimed at generating high-quality, multi-perspective visuals from a single image without requiring any form of training. This tool allows for precise regeneration of objects and subjects viewed from various angles and backgrounds, offering real-time element regeneration which enhances both stability and flexibility in creative workflows. Users can easily regenerate subjects from different perspectives, swap backgrounds or outfits with a simple click, and start producing results instantly without the need for prior training. By utilizing cutting-edge image processing methods, ZenCtrl guarantees high accuracy while minimizing the need for large training datasets. The architecture consists of streamlined sub-models, each specifically fine-tuned to excel at distinct tasks, resulting in a lightweight system that produces sharper and more controllable outcomes. The latest update to ZenCtrl significantly improves the generation of both subjects and backgrounds, ensuring that the final images are not only coherent but also visually appealing. This continual enhancement reflects the commitment to providing users with the most efficient and effective tools for their creative endeavors.

Thunder Compute delivers cheap cloud GPUs for companies, researchers, and developers running demanding AI and machine learning workloads. The platform gives users fast access to H100, A100, and RTX A6000 GPUs for LLM training, inference, fine-tuning, image generation, ComfyUI workflows, PyTorch jobs, CUDA applications, deep learning pipelines, model serving, and other GPU-intensive compute tasks. Thunder Compute is designed for teams that want affordable GPU cloud infrastructure with a strong developer experience, clear pricing, and minimal operational friction. Instead of dealing with the cost and complexity of legacy cloud vendors, users can deploy on-demand GPU instances with persistent storage, rapid provisioning, straightforward management, and scalable compute capacity. Thunder Compute is a strong fit for startups building AI products, engineering teams that need cloud GPUs for inference, and organizations looking for GPU hosting that is both economical and reliable. If you are searching for cheap H100s, A100 cloud instances, affordable GPUs for AI, or a RunPod alternative with transparent pricing and a simple interface, Thunder Compute provides a modern option for high-performance cloud GPU rental and AI infrastructure. Thunder Compute supports teams building and deploying modern AI applications that need dependable access to cheap cloud GPUs for both experimentation and production. From prototype training runs to large-scale inference and batch processing, the platform is designed to reduce infrastructure friction and accelerate iteration. For users comparing GPU cloud providers, Thunder Compute stands out with affordable pricing, fast access to top-tier GPUs, and a developer-friendly experience built around real AI workflows.

Bitext specializes in creating multilingual hybrid synthetic training datasets tailored for intent recognition and the fine-tuning of language models. These datasets combine extensive synthetic text generation with careful expert curation and detailed linguistic annotation, which encompasses various aspects like lexical, syntactic, semantic, register, and stylistic diversity, all aimed at improving the understanding, precision, and adaptability of conversational models. For instance, their open-source customer support dataset includes approximately 27,000 question-and-answer pairs, totaling around 3.57 million tokens, 27 distinct intents across 10 categories, 30 types of entities, and 12 tags for language generation, all meticulously anonymized to meet privacy, bias reduction, and anti-hallucination criteria. Additionally, Bitext provides industry-specific datasets, such as those for travel and banking, and caters to over 20 sectors in various languages while achieving an impressive accuracy rate exceeding 95%. Their innovative hybrid methodology guarantees that the training data is not only scalable and multilingual but also compliant with privacy standards, effectively reduces bias, and is well-prepared for the enhancement and deployment of language models. This comprehensive approach positions Bitext as a leader in delivering high-quality training resources for advanced conversational AI systems.

Portia AI is an open-source developer framework that includes optional cloud services, enabling teams to quickly create, deploy, and oversee stateful, authenticated AI agents while maintaining full visibility and control over the process. Developers initiate the process by using the SDK to generate clear, organized multi-step "plans" that integrate LLM reasoning with various tool calls, executing these plans incrementally and enhancing the plan state at each step, while also allowing for pauses to seek clarifications, whether from human users or machine inputs, when authentication or additional information is necessary. With its cohesive authentication framework and an easily customizable tool catalog, Portia automatically manages the credentials and permissions needed for remote API and MCP tool calls. Furthermore, the accompanying cloud solution provides persistent storage for plan execution states, historical log tracking, telemetry dashboards, and managed scaling, ensuring that production deployments remain dependable, traceable, and compliant with regulatory standards. This comprehensive approach not only simplifies the development process but also enhances the overall efficiency and effectiveness of AI agent deployments.

NuExtract is an advanced tool designed for extracting structured data from various document formats, such as text files, scanned images, PDFs, PowerPoints, spreadsheets, among others, while accommodating multiple languages and mixed-language inputs. It generates output in JSON format that adheres to user-specified templates, incorporating verification and handling of null values to reduce inaccuracies. Users can initiate extraction tasks by crafting a template through either specifying the fields they want or importing existing formats; they can enhance precision by including example documents and expected outputs in the example set. The NuExtract Platform boasts a user-friendly interface for template creation, extraction testing in a sandbox environment, managing teaching examples, and adjusting parameters like model temperature and document rasterization DPI. After completion of validation, projects can be executed through a RESTful API endpoint, enabling real-time processing of documents. This seamless integration allows users to efficiently manage their data extraction needs, enhancing both productivity and accuracy in their workflows.

The Vercel AI SDK is a complimentary, open source toolkit based on TypeScript, developed by the team behind Next.js, which empowers developers with cohesive, high-level tools for swiftly implementing AI-driven features across various model providers with just a single line of code modification. It simplifies intricate tasks such as managing streaming responses, executing multi-turn tools, handling errors, recovering from issues, and switching between models while being adaptable to any framework, allowing creators to transition from concept to operational application in mere minutes. Featuring a unified provider API, the toolkit enables developers to produce typed objects, design generative user interfaces, and provide immediate, streamed AI replies without the need to redo foundational work, complemented by comprehensive documentation, practical guides, an interactive playground, and community-driven enhancements to speed up the development process. By taking care of the complex elements behind the scenes while still allowing sufficient control for deeper customization, this SDK ensures a smooth integration experience with multiple large language models. Overall, it stands as an essential resource for developers seeking to innovate rapidly and effectively in the realm of AI applications.

Qwen-Image is a cutting-edge multimodal diffusion transformer (MMDiT) foundation model that delivers exceptional capabilities in image generation, text rendering, editing, and comprehension. It stands out for its proficiency in integrating complex text, effortlessly incorporating both alphabetic and logographic scripts into visuals while maintaining high typographic accuracy. The model caters to a wide range of artistic styles, from photorealism to impressionism, anime, and minimalist design. In addition to creation, it offers advanced image editing functionalities such as style transfer, object insertion or removal, detail enhancement, in-image text editing, and manipulation of human poses through simple prompts. Furthermore, its built-in vision understanding tasks, which include object detection, semantic segmentation, depth and edge estimation, novel view synthesis, and super-resolution, enhance its ability to perform intelligent visual analysis. Qwen-Image can be accessed through popular libraries like Hugging Face Diffusers and is equipped with prompt-enhancement tools to support multiple languages, making it a versatile tool for creators across various fields. Its comprehensive features position Qwen-Image as a valuable asset for both artists and developers looking to explore the intersection of visual art and technology.

NVIDIA Cosmos serves as a cutting-edge platform tailored for developers, featuring advanced generative World Foundation Models (WFMs), sophisticated video tokenizers, safety protocols, and a streamlined data processing and curation system aimed at enhancing the development of physical AI. This platform empowers developers who are focused on areas such as autonomous vehicles, robotics, and video analytics AI agents to create highly realistic, physics-informed synthetic video data, leveraging an extensive dataset that encompasses 20 million hours of both actual and simulated footage, facilitating the rapid simulation of future scenarios, the training of world models, and the customization of specific behaviors. The platform comprises three primary types of WFMs: Cosmos Predict, which can produce up to 30 seconds of continuous video from various input modalities; Cosmos Transfer, which modifies simulations to work across different environments and lighting conditions for improved domain augmentation; and Cosmos Reason, a vision-language model that implements structured reasoning to analyze spatial-temporal information for effective planning and decision-making. With these capabilities, NVIDIA Cosmos significantly accelerates the innovation cycle in physical AI applications, fostering breakthroughs across various industries.

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.