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Open Source
561 posts about Open Source. Every summary links to the original.
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Fine-Tune ViT for Image Classification with 🤗 Transformers
Vision Transformer models bring transformer architectures to computer vision by splitting images into grids of sub-image patches and projecting them into token sequences. To classify healthy and diseased leaves using the beans dataset, the Hugging Face datasets and transformers libraries enable streamlined data ingestion and model fine-tuning. Preprocessing relies on ViTImageProcessor paired with lazy on-the-fly dataset transforms to dynamically generate normalized pixel tensors. Training leverages ViTForImageClassification with the Trainer API, requiring remove_unused_columns set to False so raw image data is preserved for batch collation. Over four training epochs, fine-tuning the google/vit-base-patch16-224-in21k checkpoint achieves an evaluation accuracy of 98.5% alongside an evaluation loss of 0.0637.
Getting Started with Sentiment Analysis using Python
Modern natural language processing tools allow developers to perform sentiment analysis without deep machine learning expertise. Sentiment analysis categorizes text polarity into positive, negative, or neutral labels to extract insights from large volumes of social posts, reviews, and support tickets. Using the Hugging Face Transformers library and the Hub, practitioners can quickly run pre-trained transformer pipelines or target specific multilingual and emotion-detection models with minimal Python code. For custom requirements, developers can fine-tune models like DistilBERT using the IMDB dataset via the Trainer API or train models automatically with AutoNLP. Applying these workflows to social media data enables automated sentiment distribution analysis and visualization using pandas, matplotlib, and word clouds.
Making automatic speech recognition work on large files with Wav2Vec2 in 🤗 Transformers
Transformer-based automatic speech recognition models like Wav2Vec2 crash on long audio inputs because the O(n²) attention mechanism rapidly exhausts GPU memory. Simple audio chunking avoids out-of-memory errors but causes poor transcription accuracy at chunk boundaries due to a lack of surrounding context. To resolve this, Hugging Face Transformers leverages the Connectionist Temporal Classification architecture to process overlapping audio chunks with configurable strides. The pipeline discards low-quality logits at chunk edges and chains the remaining central predictions together to reconstruct a seamless transcript. This striding technique operates out of the box with language model-augmented pipelines and adapts directly to low-latency live audio streaming.
Fixing the Dependency Confusion Vulnerability in 600+ Ruby Apps - Shopify
Shopify identified a dependency confusion vulnerability through its bug bounty program; an attacker could exploit ambiguous package sources to install a malicious dependency and gain access to local, CI/CD, and production environments. To assess exposure, the Ruby Conventions team collected Bundler versions from repositories in a data lake and found around 600 susceptible Ruby applications. An experimental Bundler plugin converted Gemfile.lock files to the explicit-source format while restoring existing gem versions, and a CI-backed CLI later provided per-repository environments, parallel execution, pull requests, and easier failure tracking. After incremental migrations exposed configuration and tooling issues, Shopify updated supporting systems, set Bundler requirements, changed local and CI defaults, and worked with Bundler maintainers; the company reports migrating all its applications in less than a day and contributing to Bundler 2.2.21 and 2.2.22.
Welcome Stable-baselines3 to the Hugging Face Hub 🤗
Hugging Face announced an official integration with Stable-Baselines3, a popular PyTorch library for training and testing Deep Reinforcement Learning agents across diverse environments like Gym, Atari, MuJoco, and Procgen. The integration enables researchers and developers to host their saved reinforcement learning checkpoints on the Hugging Face Hub and download pre-trained community models. Interacting with the Hub requires installing the huggingface_hub and huggingface_sb3 packages, which supply helper methods for authentication, downloading, and uploading. Practitioners can retrieve checkpoint zip files using the load_from_hub function by providing the target repository identifier and filename before loading them into Stable-Baselines3 algorithms. Furthermore, users authenticated via CLI or notebook login can train policies such as PPO and publish their saved zip files to the Hub using push_to_hub.
Supabase Beta December 2021
Supabase announced a series of platform updates, educational content, and community milestones for December 2021. PostgreSQL instances now include the pg_sodium extension, enabling users to perform encryption, decryption, hashing, and cryptographic signing directly within database queries and functions through the Supabase Dashboard. The release details educational video guides demonstrating remote procedure calls from JavaScript, invoking external HTTP endpoints from database functions, and setting up triggers to run SQL on table modifications. In addition to ecosystem updates featuring new Python library releases and integrations with Divjoy and n8n, the project surpassed 26,000 GitHub stars and opened fully remote job positions across engineering, marketing, and human resources.
Boosting Wav2Vec2 with n-grams in 🤗 Transformers
Wav2Vec2 models fine-tuned with Connectionist Temporal Classification transcribe speech without external language models, but decoding can still suffer from spelling inaccuracies. Hugging Face Transformers addressed this by integrating Kensho Technologies' pyctcdecode library to support decoding with n-gram language models. Instead of decoding using simple argmax operations over logits, the Wav2Vec2ProcessorWithLM class feeds full probability matrices into beam search guided by KenLM n-gram probabilities. KenLM's build_binary utility compresses language model ARPA files into binary formats, reducing file size by more than half for faster loading and hub deployment. In Swedish xls-r-300m-sv benchmarks on Common Voice 7, this 5-gram boosted decoding setup achieved an 18.85% word error rate, delivering an approximate 30% relative performance gain.
Gradio is joining Hugging Face!
Hugging Face has acquired Gradio, the open-source machine learning library designed for building and sharing interactive model interfaces. Gradio originated in 2019 when its founder struggled to share a medical computer vision model with a physician collaborator who did not write Python. Co-founded alongside Ali Abdalla, Ali Abid, and Dawood Khan, the project expanded from computer vision into text, speech, and video modalities. Over 300,000 demos have been built using Gradio, allowing interdisciplinary industry teams and researchers to debug models internally and showcase them externally to non-technical users. The acquisition unites Gradio with Hugging Face to broaden browser-based machine learning accessibility and expand hiring efforts across the joint team.
Holiday Hackdays Winners 2021
Supabase organized the Holiday Hackdays 2021 hackathon following their launch week and announced the winning and runner-up community submissions. Selected winners include Swappy.one by Zernonia, the realtime polling platform rtPoll by Emilio and Federico Schepis, Santa Banter for holiday jokes by Andy Keogh, and the wishlist app the get list by glowdexapp. Recognized runners-up include Chivel for YouTube channel landing pages, the end-to-end encrypted e2ee-chat, a Flutter-based Holiday Sweater voting app, and a greeting card platform built by high school hackers. Winners receive limited-edition gold medal shirts, while runners-up earn silver medal shirts. All project submissions were made available for viewing on madewithsupabase.com.
Supabase Beta November 2021: Launch Week Recap
Supabase celebrated its third Launch Week and one year in beta by rolling out several major infrastructure and tooling updates. The company open-sourced its Dashboard for self-hosting and integrated Logflare to provide searchable database and API logs directly in the interface. For database querying and APIs, Supabase introduced a Postgres extension that resolves each GraphQL request with a single SQL statement to minimize network IO overhead, while upgrading its default hosted database to PostgreSQL 14 and deploying PostgREST 9.0. Security capabilities were expanded by enabling Row Level Security policies across the Realtime API to restrict streams and subscriptions on a per-user basis. Additional releases during the week included faster media file delivery, an open-sourced launch methodology, and a free course combining Next.js, Stripe, and Supabase.
Perceiver IO: a scalable, fully-attentional model that works on any modality
Standard Transformer architectures scale poorly in compute and memory because pairwise dot-product self-attention depends quadratically on input size. Perceiver IO addresses this constraint by computing self-attention across a small set of latent variables rather than directly on high-dimensional inputs. Inputs and outputs interact with the model via cross-attention operations, decoupling compute and memory costs from input and output dimensions. Integrated into Hugging Face Transformers via the PerceiverModel class, the architecture supports diverse data types using optional preprocessors, decoders, and postprocessors. Experiments demonstrate competitive performance across text, multimodal video classification, 3D point cloud classification on ModelNet40, and StarCraft II reinforcement learning in AlphaStar.
Training CodeParrot 🦜 from Scratch
Training code generation models from scratch requires careful data filtering, tokenization, and compute management. To build CodeParrot, a 1.5-billion-parameter Python generation model, engineers extracted 20 million Python files from BigQuery and cleaned the resulting 180 GB corpus down to 50 GB after discovering extreme duplication. A custom GPT-2 tokenizer was trained over streamed samples, and the GPT-2 large architecture was initialized with layer-scaled, full-precision attention mechanisms. The training pipeline leveraged Hugging Face Accelerate alongside gradient checkpointing and a streaming iterable dataset designed to yield fixed-length concatenated token sequences. CodeParrot was trained on roughly 25 to 30 billion tokens and evaluated on coding tasks using the pass@k metric.
Five more things
Supabase announced a series of platform updates, technical releases, and community initiatives to conclude its Launch Week. The team open-sourced pg_graphql, an in-progress PostgreSQL extension that keeps schema generation, query parsing, and resolvers natively on the database server. Supabase also enabled a global CDN spanning over 250 cities, delivering edge caching with a default one-hour cache time for storage assets alongside TLS 1.3, HTTP/3 over QUIC, and Brotli compression. While Supabase Functions is not yet released, two public RFCs were opened to explore architecture options, leaning heavily toward container-based execution. Finally, the update introduced a free SaaS development course with egghead.io and launched the ten-day Holiday Hackdays hackathon.
pg_graphql: A GraphQL extension for PostgreSQL
Supabase has open-sourced pg_graphql, a native PostgreSQL extension written in C and SQL that enables in-database GraphQL schema reflection and query resolution. Because free-tier virtual machines lacked memory headroom for standalone GraphQL processes like Hasura or Graphile, the team designed an in-engine solution to eliminate extra runtime overhead. The extension parses, validates, and transpiles incoming GraphQL operations into single SQL statements that aggregate data into JSON responses. This architecture avoids N+1 query problems, enforces existing row-level security policies, and maps GraphQL requests directly onto PostgreSQL transactions. The public SQL function can be queried directly or exposed over HTTP via PostgREST remote procedure calls.
Kicking off the Holiday Hackdays
Supabase has launched Holiday Hackdays, a ten-day virtual hackathon running from December 3rd to December 12th. The event invites participants to build open-source projects using Supabase features, following the recent introduction of row-level security for the Realtime API. Teams of one to five developers may build applications using any programming language or framework provided the code and design elements are created during the hackathon. Submissions will be evaluated on criteria such as inventiveness, technical execution, visual polish, and Supabase integration, with prizes dedicated to Realtime API usage and holiday-themed concepts. Members of the Supabase team will also take part by developing an open-source advent calendar showcasing community content.
Supabase acquires Logflare
Supabase announced the acquisition of Logflare, a realtime log ingestion and analytics platform. Modern database architectures encompass APIs, authentication, storage, and CDNs, requiring unified observability to diagnose slow queries and trace user requests effectively. Built with Elixir—the same language powering Supabase's Realtime Engine—Logflare automates schema adaptation for streaming inserts and enables developers to construct APIs from SQL queries via Logflare Endpoints. The integration roadmap includes embedding log querying with SQL directly into the Supabase Dashboard, maintaining existing integrations for Vercel and Cloudflare, and transitioning Logflare toward a fully open-source implementation with multiple backend storage engines. Over the coming months, existing Logflare users will be migrated to the Supabase platform under matching pricing tiers.
Introducing Snowball Fight ☃️, our first ML-Agents environment
Hugging Face has released Snowball Fight 1vs1, its first custom Deep Reinforcement Learning environment created using the Unity Machine Learning Agents Toolkit. The game enables players to shoot snowballs against a trained agent and is hosted on Hugging Face Spaces. This release marks the beginning of an effort to build an ecosystem for Deep Reinforcement Learning researchers and enthusiasts using Unity ML-Agents. The initiative focuses on building open-source environments, hosting models and training environments on the Hugging Face Hub, and deploying interactive demos via Spaces. Future plans include publishing technical tutorials, releasing additional environments, and developing a Snowball Fight 2vs2 version that utilizes the MA-POCA algorithm to train cooperative team behaviors.
Remote rendering: Shopify’s take on extensible UI
Shopify describes a UI extension architecture designed to give third-party code consistent, accessible, cross-platform experiences without allowing it to compromise the host application. Its remote-rendering approach separates UI definition from rendering, with extensions communicating with hosts through MessageChannel-based message passing; hosts are implemented in JavaScript, Kotlin, and Swift. The open-source remote-ui library supplies RPC for passing callbacks across postMessage, automatic memory management, a DOM-like RemoteRoot that serializes updates as JSON, and receivers that map remote components to DOM or React components. A sandbox isolates untrusted extension code and controls exposed APIs across web workers, Android web views, and iOS JsCore. The system powers product-subscription and post-purchase extensions, while a DOM-based version began releasing in July 2025.
Supabase Studio
Supabase has released Supabase Studio, making the web dashboard used on its hosted platform available for local development and self-hosted environments. The dashboard originally existed within a single internal monorepo alongside cloud infrastructure, but managing split repositories and code synchronization significantly slowed down production release cycles. To streamline development, the team unified the frontend into a single open-source codebase modeled after Sentry's shared architecture strategy. Built with Next.js, Tailwind CSS, Supabase UI, and MobX, the application works directly with existing deployments across Docker and the Supabase CLI. The open-source release focuses strictly on database management features, including SQL editors, role policies, and extensions, while excluding cloud project administration.
Getting Started with Hugging Face Transformers for IPUs with Optimum
Transformer prediction speed can hinder latency-sensitive workloads like conversational systems and search, while optimizing hardware performance requires significant effort. Hugging Face introduced Optimum, an open-source library that reduces Transformer model latency across diverse hardware targets. Through a partnership with Graphcore, BERT is introduced as the initial IPU-optimized model for parallel AI hardware. Developers configure the Poplar SDK environment and PopTorch inside Graphcloud, install optimum-graphcore, and execute the run_qa.py script with an IPU configuration file. Fine-tuning bert-base-uncased on SQuAD v1.1 using the IPUTrainer class yields an evaluation F1 score of 88.2757 and an exact match score of 80.6623 across 10,784 evaluation samples.
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