Hugging Face Integrations

Accelerate your internal tools development. A low-code platform for developers to build internal tools in minutes. With the SQL Generate feature, you can input natural language and get SQL statements that can be run directly. This works in databases like MySQL, Postgre, Supabase, etc. We offer developers a high degree of freedom to implement richer functionality through JavaScript, wrapping JS statements with {{Template Syntax}} to place at any string type variable spaces for more variety in the output. ILLA can connect to the mainstream databases or any data connected through APIs and add actions to chain to user events. It tightly integrates the data obtained from the back-end with the front-end components, allowing developers to write simple data calls to achieve massive information interaction.

Langfuse is an open source LLM engineering platform to help teams collaboratively debug, analyze and iterate on their LLM Applications. Observability: Instrument your app and start ingesting traces to Langfuse Langfuse UI: Inspect and debug complex logs and user sessions Prompts: Manage, version and deploy prompts from within Langfuse Analytics: Track metrics (LLM cost, latency, quality) and gain insights from dashboards & data exports Evals: Collect and calculate scores for your LLM completions Experiments: Track and test app behavior before deploying a new version Why Langfuse? - Open source - Model and framework agnostic - Built for production - Incrementally adoptable - start with a single LLM call or integration, then expand to full tracing of complex chains/agents - Use GET API to build downstream use cases and export data

Zilliz Cloud is a fully managed vector database based on the popular open-source Milvus. Zilliz Cloud helps to unlock high-performance similarity searches with no previous experience or extra effort needed for infrastructure management. It is ultra-fast and enables 10x faster vector retrieval, a feat unparalleled by any other vector database management system. Zilliz includes support for multiple vector search indexes, built-in filtering, and complete data encryption in transit, a requirement for enterprise-grade applications. Zilliz is a cost-effective way to build similarity search, recommender systems, and anomaly detection into applications to keep that competitive edge.

Union.ai is an award-winning, Flyte-based data and ML orchestrator for scalable, reproducible ML pipelines. With Union.ai, you can write your code locally and easily deploy pipelines to remote Kubernetes clusters. “Flyte’s scalability, data lineage, and caching capabilities enable us to train hundreds of models on petabytes of geospatial data, giving us an edge in our business.” — Arno, CTO at Blackshark.ai “With Flyte, we want to give the power back to biologists. We want to stand up something that they can play around with different parameters for their models because not every … parameter is fixed. We want to make sure we are giving them the power to run the analyses.” — Krishna Yeramsetty, Principal Data Scientist at Infinome “Flyte plays a vital role as a key component of Gojek's ML Platform by providing exactly that." — Pradithya Aria Pura, Principal Engineer at Goj

The workflow automation platform for complex, mission-critical data and ML processes at scale. Flyte makes it easy to create concurrent, scalable, and maintainable workflows for machine learning and data processing. Flyte is used in production at Lyft, Spotify, Freenome, and others. At Lyft, Flyte has been serving production model training and data processing for over four years, becoming the de-facto platform for teams like pricing, locations, ETA, mapping, autonomous, and more. In fact, Flyte manages over 10,000 unique workflows at Lyft, totaling over 1,000,000 executions every month, 20 million tasks, and 40 million containers. Flyte has been battle-tested at Lyft, Spotify, Freenome, and others. It is entirely open-source with an Apache 2.0 license under the Linux Foundation with a cross-industry overseeing committee. Configuring machine learning and data workflows can get complex and error-prone with YAML.

TrueFoundry is a Cloud-native Machine Learning Training and Deployment PaaS on top of Kubernetes that enables Machine learning teams to train and Deploy models at the speed of Big Tech with 100% reliability and scalability - allowing them to save cost and release Models to production faster. We abstract out the Kubernetes for Data Scientists and enable them to operate in a way they are comfortable. It also allows teams to deploy and fine-tune large language models seamlessly with full security and cost optimization. TrueFoundry is open-ended, API Driven and integrates with the internal systems, deploys on a company's internal infrastructure and ensures complete Data Privacy and DevSecOps practices.

Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects. Whether you bring your own vectors or use one of the vectorization modules, you can index billions of data objects to search through. Combine multiple search techniques, such as keyword-based and vector search, to provide state-of-the-art search experiences. Improve your search results by piping them through LLM models like GPT-3 to create next-gen search experiences. Beyond search, Weaviate's next-gen vector database can power a wide range of innovative apps. Perform lightning-fast pure vector similarity search over raw vectors or data objects, even with filters. Combine keyword-based search with vector search techniques for state-of-the-art results. Use any generative model in combination with your data, for example to do Q&A over your dataset.

Simplify your MLOps pipelines. Manage, deploy, and scale on any infrastructure with ZenML. ZenML is completely free and open-source. See the magic with just two simple commands. Set up ZenML in a matter of minutes, and start with all the tools you already use. ZenML standard interfaces ensure that your tools work together seamlessly. Gradually scale up your MLOps stack by switching out components whenever your training or deployment requirements change. Keep up with the latest changes in the MLOps world and easily integrate any new developments. Define simple and clear ML workflows without wasting time on boilerplate tooling or infrastructure code. Write portable ML code and switch from experimentation to production in seconds. Manage all your favorite MLOps tools in one place with ZenML's plug-and-play integrations. Prevent vendor lock-in by writing extensible, tooling-agnostic, and infrastructure-agnostic code.

AI agents that interact with users, answer questions, and complete tasks, using your internal data and APIs. AI that answers questions, summarize, and extract insights from any document, no matter how long. Generate tags, summaries, and transfer styles or formats between documents and data sources. Developer teams use Stack AI to automate customer support, process documents, qualify sales leads, and search through libraries of data. Try multiple prompts and LLM architectures with the ease of a button. Collect data and run fine-tuning jobs to build the optimal LLM for your product. We host all your workflows as APIs so that your users can access AI instantly. Select from the different LLM providers to compare fine-tuning jobs that satisfy your accuracy, price, and latency needs.

Just use plain English to describe your workflow steps. If the step can be automated, Nekton AI will write automation code for you and run it in the cloud. It's easy to get started, and you don't need to learn any complicated tools. Nekton integrates with thousands of services and can automate different business and personal workflows. Collect input from users and use it in automation. Send a link to your workflow to others, so they can run it. No sign-ups are required. Nekton AI can automate highly-customized tasks, and you don't need to learn complex platforms or hire developers. Mix manual and automated steps in a workflow, and automate things gradually as needed. Automation runs in the cloud, so you don’t need to set up and maintain any infrastructure. Run automation locally on your computer or use services that are not accessible from the Internet. Process small-to-medium amounts of data.

Open source is the core of Flowise, and it will always be free for commercial and personal usage. Build LLMs apps easily with Flowise, an open source UI visual tool to build your customized LLM flow using LangchainJS, written in Node Typescript/Javascript. Open source MIT license, see your LLM apps running live, and manage custom component integrations. GitHub repo Q&A using conversational retrieval QA chain. Language translation using LLM chain with a chat prompt template and chat model. Conversational agent for a chat model which utilizes chat-specific prompts and buffer memory.

Marqo is more than a vector database, it's an end-to-end vector search engine. Vector generation, storage, and retrieval are handled out of the box through a single API. No need to bring your own embeddings. Accelerate your development cycle with Marqo. Index documents and begin searching in just a few lines of code. Create multimodal indexes and search combinations of images and text with ease. Choose from a range of open source models or bring your own. Build interesting and complex queries with ease. With Marqo you can compose queries with multiple weighted components. With Marqo, input pre-processing, machine learning inference, and storage are all included out of the box. Run Marqo in a Docker image on your laptop or scale it up to dozens of GPU inference nodes in the cloud. Marqo can be scaled to provide low-latency searches against multi-terabyte indexes. Marqo helps you configure deep-learning models like CLIP to pull semantic meaning from images.

Prototype and productionize generative AI apps, built for engineers, not just ML practitioners. No more switching between platforms or wrestling with different APIs, focus on creating, not configuring. Use a familiar interface to prompt engineer and work with AI. Use parameters to easily streamline your workbooks into reusable templates. Create workflows by chaining model outputs from LLMs, image, and audio models. Create organizations to manage workbooks amongst your teammates. Share your workbook to the public or specific organizations you define with your team. Comment on workbooks and easily review and compare workbooks with your team. Develop templates for yourself, your team, or the broader developer community, get started quickly with templates to see what people are building.

PostgresML is a complete platform in a PostgreSQL extension. Build simpler, faster, and more scalable models right inside your database. Explore the SDK and test open source models in our hosted database. Combine and automate the entire workflow from embedding generation to indexing and querying for the simplest (and fastest) knowledge-based chatbot implementation. Leverage multiple types of natural language processing and machine learning models such as vector search and personalization with embeddings to improve search results. Leverage your data with time series forecasting to garner key business insights. Build statistical and predictive models with the full power of SQL and dozens of regression algorithms. Return results and detect fraud faster with ML at the database layer. PostgresML abstracts the data management overhead from the ML/AI lifecycle by enabling users to run ML/LLM models directly on a Postgres database.

garak checks if an LLM can be made to fail in a way we don't want. garak probes for hallucination, data leakage, prompt injection, misinformation, toxicity generation, jailbreaks, and many other weaknesses. garak's a free tool, we love developing it and are always interested in adding functionality to support applications. garak is a command-line tool, it's developed in Linux and OSX. Just grab it from PyPI and you should be good to go. The standard pip version of garak is updated periodically. garak has its own dependencies, you can to install garak in its own Conda environment. garak needs to know what model to scan, and by default, it'll try all the probes it knows on that model, using the vulnerability detectors recommended by each probe. For each probe loaded, garak will print a progress bar as it generates. Once the generation is complete, a row evaluating that probe's results on each detector is given.

Experience enhanced web security testing with BurpGPT our Burp Suite extension which integrates OpenAI's LLMs for advanced vulnerability scanning and traffic-based analysis. It also supports local LLMs, including custom-trained models, ensuring greater data privacy and more accurate results according to your needs. Effortlessly integrate Burp GPT into your security testing workflows with user-friendly documentation. Developed by application security experts, Burp GPT represents the cutting-edge of web security testing. Burp GPT continuously improves based on user feedback, ensuring it meets evolving security testing needs. Burp GPT is a robust tool developed to enhance the precision and efficiency of application security testing. Extended with advanced language processing capabilities and an intuitive interface, it enhances security testing for both beginners and seasoned testers alike. With BurpGPT, you can perform sophisticated technical tasks.

Depending on the use case, pick an appropriate app type or template. Provide input and configuration required for the app, and choose how the output is rendered. Add your existing data from sources like files, website URLs, sitemaps, YouTube links, Google Drive, and Notion exports. Attach these data sources to your app in the app builder. Save and publish your app. Access it from the dedicated app page or embed it in your website using the embed code. Use our APIs to run the app from your applications. Promptly provides embeddable widgets that you can easily integrate into your website. Use these widgets to build conversational AI applications or to add a chatbot to your website. Customize the chatbot look and feel to match your website, including a logo for the bot.

Lilac is an open source tool that enables data and AI practitioners to improve their products by improving their data. Understand your data with powerful search and filtering. Collaborate with your team on a single, centralized dataset. Apply best practices for data curation, like removing duplicates and PII to reduce dataset size and lower training cost and time. See how your pipeline impacts your data using our diff viewer. Clustering is a technique that automatically assigns categories to each document by analyzing the text content and putting similar documents in the same category. This reveals the overarching structure of your dataset. Lilac uses state-of-the-art algorithms and LLMs to cluster the dataset and assign informative, descriptive titles. Before we do advanced searching, like concept or semantic search, we can immediately use keyword search by typing a keyword in the search box.

Defense Unicorns provides continuous delivery for national security systems, spanning cloud, on-prem, and edge environments in land, sea, air, and space domains. UDS removes the complexity and inefficiencies of government secure technology stacks. Seamlessly integrating mission capabilities with an open source 800-53 compliant platform (70% NIST compliant) and Infrastructure as Code (IaC) for your target environment, UDS creates a secure, declarative software bundle repeatedly deployable to all your environments without vendor locking your mission. The Defense Unicorns platform is open source, accessible, avoids vendor lock-in, and provides data independence. Defense Unicorns accelerates ATO timelines by leveraging a declarative cybersecurity stack that can be deployed in any cloud, on-prem, or edge environment. Defense Unicorns specializes in platform technologies that enable rapid and reliable delivery of capabilities across a wide range of mission and technology areas.

Create amazing apps with AI without knowing how to code. Export code to multiple platforms by sketching your ideas to life. Glowbom allows you to build games and apps for the most popular platforms out there. We are actively developing support for all the most commonly used platforms. With Glowbom, you can access and modify your app's source code, and write code with AI assistance to make your app truly unique. The desktop app guides you through the code and answers any coding questions. Glowbom can also turn everyday language or other programming languages into the one that Glowbom supports, making coding easier by providing multiplatform code generation.

Lamini makes it possible for enterprises to turn proprietary data into the next generation of LLM capabilities, by offering a platform for in-house software teams to uplevel to OpenAI-level AI teams and to build within the security of their existing infrastructure. Guaranteed structured output with optimized JSON decoding. Photographic memory through retrieval-augmented fine-tuning. Improve accuracy, and dramatically reduce hallucinations. Highly parallelized inference for large batch inference. Parameter-efficient finetuning that scales to millions of production adapters. Lamini is the only company that enables enterprise companies to safely and quickly develop and control their own LLMs anywhere. It brings several of the latest technologies and research to bear that was able to make ChatGPT from GPT-3, as well as Github Copilot from Codex. These include, among others, fine-tuning, RLHF, retrieval-augmented training, data augmentation, and GPU optimization.

CodeQwen is the code version of Qwen, the large language model series developed by the Qwen team, Alibaba Cloud. It is a transformer-based decoder-only language model pre-trained on a large amount of data of codes. Strong code generation capabilities and competitive performance across a series of benchmarks. Supporting long context understanding and generation with the context length of 64K tokens. CodeQwen supports 92 coding languages and provides excellent performance in text-to-SQL, bug fixes, etc. You can just write several lines of code with transformers to chat with CodeQwen. Essentially, we build the tokenizer and the model from pre-trained methods, and we use the generate method to perform chatting with the help of the chat template provided by the tokenizer. We apply the ChatML template for chat models following our previous practice. The model completes the code snippets according to the given prompts, without any additional formatting.

CometLLM is a tool to log and visualize your LLM prompts and chains. Use CometLLM to identify effective prompt strategies, streamline your troubleshooting, and ensure reproducible workflows. Log your prompts and responses, including prompt template, variables, timestamps and duration, and any metadata that you need. Visualize your prompts and responses in the UI. Log your chain execution down to the level of granularity that you need. Visualize your chain execution in the UI. Automatically tracks your prompts when using the OpenAI chat models. Track and analyze user feedback. Diff your prompts and chain execution in the UI. Comet LLM Projects have been designed to support you in performing smart analysis of your logged prompt engineering workflows. Each column header corresponds to a metadata attribute logged in the LLM project, so the exact list of the displayed default headers can vary across projects.

Collaborate on prompts, evaluate, and monitor LLM apps with confidence. Agenta is a comprehensive platform that enables teams to quickly build robust LLM apps. Create a playground connected to your code where the whole team can experiment and collaborate. Systematically compare different prompts, models, and embeddings before going to production. Share a link to gather human feedback from the rest of the team. Agenta works out of the box with all frameworks (Langchain, Lama Index, etc.) and model providers (OpenAI, Cohere, Huggingface, self-hosted models, etc.). Gain visibility into your LLM app's costs, latency, and chain of calls. You have the option to create simple LLM apps directly from the UI. However, if you would like to write customized applications, you need to write code with Python. Agenta is model agnostic and works with all model providers and frameworks. The only limitation at present is that our SDK is available only in Python.

Crawl and convert any website into clean markdown or structured data, it's also open source. We crawl all accessible subpages and give you a clean markdown for each, no sitemap is required. Enhance your applications with top-tier web scraping and crawling capabilities. Extract markdown or structured data from websites quickly and efficiently. Navigate and retrieve data from all accessible subpages, even without a sitemap. Already fully integrated with the greatest existing tools and workflows. Kick off your journey for free and scale seamlessly as your project expands. Developed transparently and collaboratively. Join our community of contributors. Firecrawl crawls all accessible subpages, even without a sitemap. Firecrawl gathers data even if a website uses JavaScript to render content. Firecrawl returns clean, well-formatted markdown, ready for use in LLM applications. Firecrawl orchestrates the crawling process in parallel for the fastest results.

OpenLIT is an OpenTelemetry-native application observability tool. It's designed to make the integration process of observability into AI projects with just a single line of code. Whether you're working with popular LLM libraries such as OpenAI and HuggingFace. OpenLIT's native support makes adding it to your projects feel effortless and intuitive. Analyze LLM and GPU performance, and costs to achieve maximum efficiency and scalability. Streams data to let you visualize your data and make quick decisions and modifications. Ensures that data is processed quickly without affecting the performance of your application. OpenLIT UI helps you explore LLM costs, token consumption, performance indicators, and user interactions in a straightforward interface. Connect to popular observability systems with ease, including Datadog and Grafana Cloud, to export data automatically. OpenLIT ensures your applications are monitored seamlessly.

Automate grading tasks and provide inline feedback for students. Collaborate effectively with your learners using discussion boards, mentions, and inline comments. real-time collaboration is coming soon. IllumiDesk's AI will help you create, maintain, and structure your content into interactive lessons. IllumiDesk's flexible integration options using the RESTful API and/or LTI v1.3 help leverage existing content and flows. Crafting remarkable courses can now be achievable in just minutes, not months. Mobilize your audiences with captivating content without devoting endless hours of work. Create a course effortlessly with AI in just a few clicks, tailoring it to your specific context, or start from scratch and build a course from the ground up. IllumiDesk's flexible integration options using the RESTful API and/or LTI v1.3 help leverage existing content and flows. IllumiDesk is the AI platform for instructors and content developers that helps teams create and monetize content.

The most flexible data annotation tool. Quickly installable. Build custom UIs or use pre-built labeling templates. Configurable layouts and templates adapt to your dataset and workflow. Detect objects on images, boxes, polygons, circular, and key points supported. Partition the image into multiple segments. Use ML models to pre-label and optimize the process. Webhooks, Python SDK, and API allow you to authenticate, create projects, import tasks, manage model predictions, and more. Save time by using predictions to assist your labeling process with ML backend integration. Connect to cloud object storage and label data there directly with S3 and GCP. Prepare and manage your dataset in our Data Manager using advanced filters. Support multiple projects, use cases, and data types in one platform. Start typing in the config, and you can quickly preview the labeling interface. At the bottom of the page, you have live serialization updates of what Label Studio expects as an input.

your team can develop AI applications based on models such as GPT-4 and operate them visually. Whether for internal team use or external release, you can deploy your application in as fast as 5 minutes. Using documents/webpages/Notion content as the context for AI, automatically complete text preprocessing, vectorization and segmentation. You don't have to learn embedding techniques anymore, saving you weeks of development time. Dify provides a smooth experience for model access, context embedding, cost control and data annotation. Whether for internal team use or product development, you can easily create AI applications. Starting from a prompt, but transcending the limitations of the prompt. Dify provides rich functionality for many scenarios, all through graphical user interface operations.

Build and manage AI applications easily without needing to move your data to complex pipelines and specialized vector databases. Integrate AI and vector search directly with your database including real-time inference and model training. A single scalable deployment of all your AI models and APIs which is automatically kept up-to-date as new data is processed immediately. No need to introduce an additional database and duplicate your data to use vector search and build on top of it. SuperDuperDB enables vector search in your existing database. Integrate and combine models from Sklearn, PyTorch, and HuggingFace with AI APIs such as OpenAI to build even the most complex AI applications and workflows. Deploy all your AI models to automatically compute outputs (inference) in your datastore in a single environment with simple Python commands.

We are an AI-automated turnkey IT workforce solution in a box that is indefinitely scalable and payments-enabled. We are democratizing how U.S. companies go nearshore without the high vendor costs and security risks. Use our system to predict talent costs to bring new business objectives to market and the amount of aligned talent across the LATAM region. AccessInstantly access a dedicated and influential senior recruitment staff team that understands the talent market and your business technologies. Your dedicated engineering managers validate and score technical capabilities among video-recorded specialized tests for best alignment. Automate your personalized onboarding process for all roles across multiple LATAM countries. We procure and prepare dedicated devices and ensure all staff have access to all the tools and documentation from day one to hit the ground running. Quickly address top performers and those who yearn to expand their capabilities.

Take the guesswork out of prompt engineering with suggested edits, prompt rewrites, and automatically generated test cases. We run dozens of analyses over your prompts and data to identify limitations and apply fixes. Detect prompt issues and potential improvements. Automatically rewrite prompts with AI-generated fixes. Don't waste time writing test cases for your prompts. We generate high-quality examples to test your prompts and guide your updates. Identify ways in which you can improve your prompts. Have AI automatically rewrite your prompts to fix limitations. Generate diverse test cases to validate changes and guide updates. Use your optimized prompts across models and tools.

Featherless is an AI model provider that offers our subscribers access to a continually expanding library of Hugging Face models. With hundreds of new models daily, you need dedicated tools to keep up with the hype. No matter your use case, find and use the state-of-the-art AI model with Featherless. At present, we support LLaMA-3-based models, including LLaMA-3 and QWEN-2. Note that QWEN-2 models are only supported up to 16,000 context length. We plan to add more architectures to our supported list soon. We continuously onboard new models as they become available on Hugging Face. As we grow, we aim to automate this process to encompass all publicly available Hugging Face models with compatible architecture. To ensure fair individual account use, concurrent requests are limited according to the plan you've selected. Output is delivered at a speed of 10-40 tokens per second, depending on the model and prompt size.

Local and secure AI on your desktop, ensuring comprehensive insights with complete data security and privacy. Experience unparalleled efficiency, privacy, and intelligence with our cutting-edge macOS-native app and advanced AI features. RAG can utilize data from a local knowledge base to supplement the large language model (LLM). This means you can keep sensitive data on-premises while leveraging it to enhance the model‘s response capabilities. To implement RAG locally, you first need to segment documents into smaller chunks and then encode these chunks into vectors, storing them in a vector database. These vectorized data will be used for subsequent retrieval processes. When a user query is received, the system retrieves the most relevant chunks from the local knowledge base and inputs these chunks along with the original query into the LLM to generate the final response. We promise lifetime free access for individual users.

Connect AI models to build AI apps in minutes, with no-code. The next generation of AI-powered apps will be built by you. Create AI-powered apps in minutes. There has never been a faster way to create AI-powered apps. Monetize & share your creations with the world. Tap into the earning potential of your unique creations. From a spark of inspiration to a quick start for a new app, brancher.ai shares over 100 templates to help you boost your creativity and productivity.

Long-term memory for AI. The Pinecone vector database makes it easy to build high-performance vector search applications. Developer-friendly, fully managed, and easily scalable without infrastructure hassles. Once you have vector embeddings, manage and search through them in Pinecone to power semantic search, recommenders, and other applications that rely on relevant information retrieval. Ultra-low query latency, even with billions of items. Give users a great experience. Live index updates when you add, edit, or delete data. Your data is ready right away. Combine vector search with metadata filters for more relevant and faster results. Launch, use, and scale your vector search service with our easy API, without worrying about infrastructure or algorithms. We'll keep it running smoothly and securely.

Ship AI faster with managed, cloud-hosted AI packages. Full, built-in support for GPT-4. No API tokens are necessary. Build with our low code framework. Integrations with all major models are built-in. Deploy for an instant API. Scale and share without managing infrastructure. Turn prompts, prompt chains, and basic Python into a managed API. Turn a clever prompt into a published API you can share. Add logic and routing smarts with Python. Steamship connects to your favorite models and services so that you don't have to learn a new API for every provider. Steamship persists in model output in a standardized format. Consolidate training, inference, vector search, and endpoint hosting. Import, transcribe, or generate text. Run all the models you want on it. Query across the results with ShipQL. Packages are full-stack, cloud-hosted AI apps. Each instance you create provides an API and private data workspace.

Build, train and deploy your models in the cloud, using the latest IPU AI systems and the frameworks you love, with our cloud partners. Allowing you to save on compute costs and seamlessly scale to massive IPU compute when you need it. Get started with IPUs today with on-demand pricing and free tier offerings with our cloud partners. We believe our Intelligence Processing Unit (IPU) technology will become the worldwide standard for machine intelligence compute. The Graphcore IPU is going to be transformative across all industries and sectors with a real potential for positive societal impact from drug discovery and disaster recovery to decarbonization. The IPU is a completely new processor, specifically designed for AI compute. The IPU’s unique architecture lets AI researchers undertake entirely new types of work, not possible using current technologies, to drive the next advances in machine intelligence.

Amazon SageMaker Model Training reduces the time and cost to train and tune machine learning (ML) models at scale without the need to manage infrastructure. You can take advantage of the highest-performing ML compute infrastructure currently available, and SageMaker can automatically scale infrastructure up or down, from one to thousands of GPUs. Since you pay only for what you use, you can manage your training costs more effectively. To train deep learning models faster, SageMaker distributed training libraries can automatically split large models and training datasets across AWS GPU instances, or you can use third-party libraries, such as DeepSpeed, Horovod, or Megatron. Efficiently manage system resources with a wide choice of GPUs and CPUs including P4d.24xl instances, which are the fastest training instances currently available in the cloud. Specify the location of data, indicate the type of SageMaker instances, and get started with a single click.

Build & Share Delightful Machine Learning Apps. Gradio is the fastest way to demo your machine learning model with a friendly web interface so that anyone can use it, anywhere! Gradio can be installed with pip. Creating a Gradio interface only requires adding a couple lines of code to your project. You can choose from a variety of interface types to interface your function. Gradio can be embedded in Python notebooks or presented as a webpage. A Gradio interface can automatically generate a public link you can share with colleagues that lets them interact with the model on your computer remotely from their own devices. Once you've created an interface, you can permanently host it on Hugging Face. Hugging Face Spaces will host the interface on its servers and provide you with a link you can share.

Accelerate your GPU workflows. Make AI models, video processing, and more up to 10x faster while cutting costs by up to 90%.

Apply the latest NLP technology to your own data with the use of Haystack's pipeline architecture. Implement production-ready semantic search, question answering, summarization and document ranking for a wide range of NLP applications. Evaluate components and fine-tune models. Ask questions in natural language and find granular answers in your documents using the latest QA models with the help of Haystack pipelines. Perform semantic search and retrieve ranked documents according to meaning, not just keywords! Make use of and compare the latest pre-trained transformer-based languages models like OpenAI’s GPT-3, BERT, RoBERTa, DPR, and more. Build semantic search and question-answering applications that can scale to millions of documents. Building blocks for the entire product development cycle such as file converters, indexing functions, models, labeling tools, domain adaptation modules, and REST API.

Cleanlab Studio handles the entire data quality and data-centric AI pipeline in a single framework for analytics and machine learning tasks. Automated pipeline does all ML for you: data preprocessing, foundation model fine-tuning, hyperparameter tuning, and model selection. ML models are used to diagnose data issues, and then can be re-trained on your corrected dataset with one click. Explore the entire heatmap of suggested corrections for all classes in your dataset. Cleanlab Studio provides all of this information and more for free as soon as you upload your dataset. Cleanlab Studio comes pre-loaded with several demo datasets and projects, so you can check those out in your account after signing in.

Lakera Guard empowers organizations to build GenAI applications without worrying about prompt injections, data loss, harmful content, and other LLM risks. Powered by the world's most advanced AI threat intelligence. Lakera’s threat intelligence database contains tens of millions of attack data points and is growing by 100k+ entries every day. With Lakera guard, your defense continuously strengthens. Lakera guard embeds industry-leading security intelligence at the heart of your LLM applications so that you can build and deploy secure AI systems at scale. We observe tens of millions of attacks to detect and protect you from undesired behavior and data loss caused by prompt injection. Continuously assess, track, report, and responsibly manage your AI systems across the organization to ensure they are secure at all times.

Prompt Security enables enterprises to benefit from the adoption of Generative AI while protecting from the full range of risks to their applications, employees and customers. At every touchpoint of Generative AI in an organization — from AI tools used by employees to GenAI integrations in customer-facing products — Prompt inspects each prompt and model response to prevent the exposure of sensitive data, block harmful content, and secure against GenAI-specific attacks. The solution also provides leadership of enterprises with complete visibility and governance over the AI tools used within their organization.

The only auto-pilot agent that works with your unique software development workflow. Anycode AI converts your whole Legacy codebase to modern tech stacks up to 8X faster. Boost your coding speed tenfold with Anycode AI. Utilize AI for rapid, compliant coding and testing. Modernize swiftly with Anycode AI. Effortlessly handle legacy code and embrace updates for efficient applications. Upgrade seamlessly from outdated systems. Our platform refines old logic for a smooth transition to advanced tech.

Use models through the in-app Chat UI or an OpenAI-compatible local server. Minimum requirements: M1/M2/M3 Mac, or a Windows PC with a processor that supports AVX2. Linux is available in beta. One of the main reasons for using a local LLM is privacy, and LM Studio is designed for that. Your data remains private and local to your machine. You can use LLMs you load within LM Studio via an API server running on localhost.

Light up the black box and pip install 3LC to gain the clarity you need to make meaningful changes to your models in moments. Remove the guesswork from your model training and iterate fast. Collect per-sample metrics and visualize them in your browser. Analyze your training and eliminate issues in your dataset. Model-guided, interactive data debugging and enhancements. Find important or inefficient samples. Understand what samples work and where your model struggles. Improve your model in different ways by weighting your data. Make sparse, non-destructive edits to individual samples or in a batch. Maintain a lineage of all changes and restore any previous revisions. Dive deeper than standard experiment trackers with per-sample per epoch metrics and data tracking. Aggregate metrics by sample features, rather than just epoch, to spot hidden trends. Tie each training run to a specific dataset revision for full reproducibility.

An intuitive yet comprehensive evaluation platform to iteratively optimize your AI-driven products. Streamline LLMOps workflow, build confidence, and gain a competitive edge. EvalsOne is your all-in-one toolbox for optimizing your application evaluation process. Imagine a Swiss Army knife for AI, equipped to tackle any evaluation scenario you throw its way. Suitable for crafting LLM prompts, fine-tuning RAG processes, and evaluating AI agents. Choose from rule-based or LLM-based approaches to automate the evaluation process. Integrate human evaluation seamlessly, leveraging the power of expert judgment. Applicable to all LLMOps stages from development to production environments. EvalsOne provides an intuitive process and interface, that empowers teams across the AI lifecycle, from developers to researchers and domain experts. Easily create evaluation runs and organize them in levels. Quickly iterate and perform in-depth analysis through forked runs.

A family of state-of-the-art, light-open models created from the same research and technology that were used to create Gemini models. These models incorporate comprehensive security measures and help ensure responsible and reliable AI solutions through selected data sets and rigorous adjustments. Gemma models achieve exceptional comparative results in their 2B, 7B, 9B, and 27B sizes, even outperforming some larger open models. With Keras 3.0, enjoy seamless compatibility with JAX, TensorFlow, and PyTorch, allowing you to effortlessly choose and change frameworks based on task. Redesigned to deliver outstanding performance and unmatched efficiency, Gemma 2 is optimized for incredibly fast inference on various hardware. The Gemma family of models offers different models that are optimized for specific use cases and adapt to your needs. Gemma models are large text-to-text lightweight language models with a decoder, trained in a huge set of text data, code, and mathematical content.