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Open Source
562 posts about Open Source. Every summary links to the original.
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Machine Learning Experts - Sasha Luccioni
Research scientist Sasha Luccioni discusses her work on evaluating the environmental, societal, and ethical impacts of machine learning models. Her research includes measuring and reducing artificial intelligence emissions through initiatives such as CodeCarbon, regional grid selection, and the BigScience carbon footprint working group. BigScience aims to make large language models transparent by curating multilingual datasets, assessing manufacturing and compute costs, and analyzing participant workloads. Luccioni also emphasizes expanding model evaluation criteria beyond standard accuracy metrics to encompass efficiency, emissions, and subset-level fairness across demographics and data features. Overall, these efforts aim to democratize artificial intelligence and encourage deliberate trade-offs between model performance and computational resource demands.
Announcing the Hugging Face Fellowship Program
Hugging Face announced the launch of the Hugging Face Fellowship Program to support and empower prominent contributors across the open-source machine learning ecosystem. The initiative provides selected fellows with personalized resources according to their project goals, including computing power, merchandise, and official recognition from Hugging Face. Candidate selection operates on an ongoing basis through nominations made by Hugging Face team members or existing fellows, evaluated primarily on past contributions toward democratizing machine learning. Eligible open-source activities span model releases on the Hub, documentation translations, library tooling improvements for frameworks like TensorFlow and Keras, and community hackathons. Participation does not constitute employment with Hugging Face, though the program serves as a platform to scale grassroots community impact across diverse machine learning domains.
Gradio 3.0 is Out!
Gradio 3.0 introduces a ground-up redesign of the Gradio library to improve machine learning demo development and browser performance. The frontend has been rebuilt using modern technologies such as Svelte, resulting in smaller payloads, faster page load times, and cleaner visual designs suitable for embedding in blog posts. Component updates include revamping the Dataframe component to support CSV drag-and-drop, introducing new UI elements like Gallery, and adding the TabbedInterface class to group demos under distinct tabs. Developers also gain access to Gradio Blocks, a low-level Python API enabling custom layouts, multi-step interfaces, dynamic component property modifications, and complex data flows. A competition called the Gradio Blocks Party has been organized through the end of May to encourage community adoption.
Student Ambassador Program’s call for applications is open!
Hugging Face launched its inaugural Student Ambassador Program to advance its mission of democratizing machine learning globally. The initiative aligns with the organization's overarching goal to teach machine learning to 5 million people by 2023. Selected student ambassadors will receive dedicated workshops, direct team support, access to a peer network, merchandise, and updates on upcoming projects and features. Applicants must be enrolled in an accredited higher education institution, have completed at least one machine learning or data science course, and already use the Hugging Face Hub or libraries. The application window closed on June 13, 2022, with the program operating from June 30, 2022, through December 31, 2022.
Accelerated Inference with Optimum and Transformers Pipelines
Transformer-based models are moving into production for large-scale workloads, but default implementations remain slow and resource-intensive compared to traditional machine learning algorithms. Hugging Face Optimum addresses this issue by integrating performance optimization tools and accelerated runtimes like ONNX Runtime directly into standard Transformers pipelines. Developers replace standard model classes with Optimum equivalents to convert models to ONNX, run graph optimizations such as operator fusion, and apply quantization. In an evaluation on an AWS EC2 m5.xlarge CPU instance, optimizing and quantizing a RoBERTa question-answering model cut average latency from 117.61 ms to 64.94 ms while retaining 99.61 percent accuracy. Known limitations include a 2GB remote model size limit, lack of seq2seq support, omitted past key values in causal language models, and no local caching for optimized ONNX models.
We Raised $100 Million for Open & Collaborative Machine Learning 🚀
Hugging Face announced a 100 million dollar Series C funding round led by Lux Capital, alongside participants including Sequoia, Coatue, and several angel investors. The platform hosts over 100,000 pre-trained models and 10,000 datasets spanning natural language processing, computer vision, speech, and other scientific domains for more than 10,000 customer organizations. The company plans to use the capital to expand research, open-source initiatives, tooling, and efforts focused on mitigating machine learning limitations like biases, privacy risks, and energy consumption. In addition, the organization is directing the BigScience research workshop with over 1,000 participants to train a massive open-source multilingual language model. Following organizational growth from 30 to more than 120 employees in twelve months, the remote-friendly company is actively hiring across all positions and seniority levels.
Talk Nerdy to Me: 10 Tech Podcast Recommendations from Shopify Engineering
Shopify Engineering curated ten podcast recommendations from conversations with its technology employees, extending an earlier roundup of book recommendations. The list spans software engineering, front-end development, JavaScript, career growth, cybersecurity, data science, and broader technology topics, with selections ranging from quick, technical episodes to witty, conversational shows. Several entries emphasize specific formats or practices: Software Engineering Daily conducts researched interviews and focuses on open source, Soft Skills Engineering answers two listener-submitted questions in 30 minutes, and Darknet Diaries covers hacking, cybercrime, and data breaches. The recommendations also include podcasts valued for approachable explanations, collaborative software work, diverse guests, and perspectives on leadership and professional development, while some contributors identify particular episodes or hosts as reasons they keep listening.
Welcome fastai to the Hugging Face Hub
Hugging Face Hub has integrated with the fastai deep learning library, allowing practitioners to upload and download models directly through the huggingface_hub Python client. Fastai Learner objects, which bundle model architectures, data loaders, and loss functions, can be published to a designated repository namespace using push_to_hub_fastai. Uploaded models automatically receive Git-based version control, Git LFS support for large files, and generated model cards. Practitioners can retrieve and instantiate published models locally for inference or transfer learning via from_pretrained_fastai. The integration also supports Blurr, an ecosystem library that combines fastai with Hugging Face Transformers for sequence classification and related text modeling tasks.
React Native Skia—For Us, For You, and For Fun
React Native Skia brings Skia’s cross-platform 2D drawing primitives to React Native, with the stated goals of improving its limited graphical capabilities and aligning designers and developers around capabilities comparable to Figma. The integration uses React Native’s JavaScript Interface (JSI), enabling direct JavaScript-to-native communication through C++ rather than asynchronous messages, and exposes an API described as virtually 100% compatible with Flutter’s. On top of the imperative API, a declarative layer uses the React Reconciler to diff internal representation states and send changes to the Skia engine, supporting composable drawings and declarative optimizations. The library includes image filters, shaders, SVG, path operations, vertices, text layouts, mesh gradients, shadows, backdrop blur, and animations, with a direct connector to Reanimated 2; the authors expect it to address a major React Native pain point and describe the work as just beginning.
Introducing Hugging Face for Education 🤗
Hugging Face established an initiative to teach machine learning to 5 million people by the end of 2023. As machine learning expands across software development and impacts non-technical users, adapting workforce skills and assisting educators with ethical considerations become critical challenges. The educational framework provides browser-based model testing widgets, documentation on model biases, and free courses covering Natural Language Processing, Deep Reinforcement Learning, and interactive demo creation. Instructors receive free infrastructure to create Hub classrooms, alongside a multilingual toolkit spanning topics like Transformers and Gradio. Through ongoing global tours and free compute events, the organization supports collaborative machine learning education across diverse experience levels.
Supercharged Customer Service with Machine Learning
Customer support teams often receive high volumes of messages that cannot all be answered manually. To prioritize urgent inquiries, support workflows can be modeled as a text classification task to identify the most unsatisfied customers. Using the Hugging Face ecosystem, an NLP pipeline is established by selecting the Amazon reviews multi dataset and fine-tuning a DeBERTa model for sentiment classification across five granular categories. Evaluation on test data shows that the model identifies roughly 95% of unsatisfied messages with an 11.7% false-positive rate on satisfied messages, potentially reducing human triage workload by 83%. For production deployment, performance can be optimized through hardware acceleration, lower precision arithmetic, open-source libraries like Optimum and ONNX Runtime, and inference servers.
CO2 Emissions and the 🤗 Hub: Leading the Charge
Training and deploying machine learning models produces greenhouse gas emissions determined by infrastructure runtime, hardware type, and energy grid carbon intensity. Hugging Face introduced mechanisms to measure, report, and filter models based on their carbon footprint to improve ecological transparency. Using the huggingface_hub library, practitioners can query HfApi.list_models with an emissions_thresholds parameter to filter models by maximum or minimum carbon emissions measured in grams. In addition, installing the codecarbon package enables the Transformers Trainer to automatically attach CodeCarbonCallback and log training emissions to an emissions.csv file. Users can then publish these measured values directly into model card metadata under co2_eq_emissions for public tracking.
Works With Supabase - announcing our Partner Gallery
Supabase launched its Partner Gallery to showcase integration partners and certified expert agencies assisting developers on the platform. The open-source gallery is built using the Supabase stack, featuring Postgres Full Text Search, next-image with Supabase Storage, and email triggers via Supabase Edge Functions. Initial agency partners include Morrow, a UK app development agency specializing in React Native and Supabase, alongside Quinence, a Singapore-based product development studio. Integrated tooling covers authentication systems like Clerk and Stytch, database and developer utilities like Prisma, Snaplet, Vercel, and pgMustard, and low-code builders including Appsmith, Draftbit, Plasmic, and Teta. These ecosystem integrations allow developers to manage Postgres workflows, secure user authorization, and build applications across web and mobile platforms.
Bring the Func Hackathon Winners 2022
Supabase announced the winners and runners-up of the 2022 Bring the Func Hackathon following their recent launch week. The event featured multiple project categories, showcasing applications built by community developers with Supabase tools. In the main showcase, mintbaseXsupabase won for an Instagram-like NFT browsing platform, while Pgm Dictionary served as runner-up with an UrbanDictionary clone built on Next.js, Mantine, and Supabase. The Edge Functions category highlighted Wen NFT for managing NFT community access and runner-up DocuPool for token-unlocked document sharing. Finally, AbileneX won the Most Fun or Visually Pleasing category for gathering feedback on ideas, alongside project management tool Kardow as runner-up.
Supabase Beta March 2022
Supabase announced several major platform updates for March 2022, introducing global edge deployments for JavaScript and TypeScript functions developed and tested locally. Database querying expanded to support GraphQL through an open-source, portable Postgres extension. Supabase Realtime received support for multiplayer and presence to enable collaborative applications, alongside enterprise and scale capabilities including Point-In-Time-Recovery, SLAs, premium support, a Prometheus endpoint, and an observability dashboard. To enhance security, an integration with GitHub secret scanning automatically notifies developers and rolls exposed service_role keys found in public repositories. Additional updates include the PostgREST 10 pre-release, Next-JS auth helpers, new OAuth providers, and full remote hiring across engineering, marketing, and human resources.
Machine Learning Experts - Lewis Tunstall
In this interview with Britney Muller, Hugging Face machine learning engineer Lewis Tunstall discusses his background, his book Natural Language Processing with Transformers, and tooling optimizations for deploying transformer models. Tunstall notes that large model parameter sizes often lead to high latency, making off-the-shelf transformers impractical for responsive applications like chatbots. To resolve this issue, he developed single-line export tooling within the transformers library to convert models into the ONNX format. This conversion enables engineers to run PyTorch models in TensorFlow or on specialized hardware, achieving faster latency and higher throughput. Tunstall also recounts co-authoring the O'Reilly book with Leandro von Werra and Thom Wolf, collaborating on the Hugging Face Course, and training a GPT-2 model named code parrot on Python code.
Habana Labs and Hugging Face Partner to Accelerate Transformer Model Training
Training transformer models across computer vision, speech, and natural language processing tasks at scale often demands heavy compute resources, incurring significant time and financial expense. To address these bottlenecks, Habana Labs and Hugging Face partnered to integrate the SynapseAI software suite into the Hugging Face Optimum open-source library. This integration allows machine learning practitioners to accelerate transformer training workflows on Habana Gaudi processors using minimal code adjustments. Habana Gaudi hardware, featured in Amazon EC2 DL1 instances and Supermicro X12 servers, incorporates ten 100 Gigabit Ethernet ports per processor to scale from single units to thousands of chips. The combined hardware and software architecture supports TensorFlow and PyTorch while delivering price and performance metrics up to 40% lower than comparable training alternatives.
Leveraging Shopify’s API to Build the Latest Marketplace Kit
Marketplace Kit version 2 provides boilerplate app code and documentation for third-party developers integrating with Shopify, implementing selected commerce features, and launching marketplaces across channels. The team moved away from using the Shopify CLI-generated Node app because CLI changes could affect their code and documentation, while its dependencies limited control over development practices. The new kit separates a full-stack merchant-facing admin app from a buyer-facing client app, using Express, React, NextJS, GraphQL, Apollo Client, and Apollo Server; open-source versions are publicly available. Its examples demonstrate JWT authentication through App Bridge and Shopify’s Node API, serving REST and GraphQL together with Express, and choosing REST for a product-listing count where a query offered no advantage.
~Don't~ Repeat Yourself
Hugging Face's Transformers library intentionally departs from the traditional "Don't repeat yourself" (DRY) software design principle by adopting a single model file policy. This architecture places all code required for a model's forward pass into a dedicated file, avoiding centralized shared modules for components like attention mechanisms. Because machine learning research evolves rapidly and published model architectures remain static, isolating code protects individual models from breaking changes and lowers contribution barriers for the open-source community. To maintain consistency without manually syncing duplicated code across successor models, the library uses automated tooling driven by Copied from comments. While this approach complicates unified API enforcement and component-wide research integration, Transformers validates API consistency by running approximately 20,000 automated tests daily.
Hackathon: Bring the Func(🕺)
Following the conclusion of its fourth Launch Week, Supabase announced the ten-day community hackathon titled Bring the Func, running through April 10th. The competition highlights newly released platform capabilities, establishing primary prize tracks dedicated to Supabase Edge Functions and GraphQL projects powered by pg_graphql. Participants can compete individually or in teams of up to five people, provided all design and code assets are built during the event and published as open-source repositories. Submissions require linking a repository detailing architecture, hosted demos, and Supabase feature usage across storage, authentication, realtime, or database access layers. Alongside category prizes and community swag, Supabase partnered with the Future Forest Company to plant a tree and issue a certificate for every completed project entry.
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