Search Results for "structure" - Page 5
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Showing 178 open source projects for "structure"
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LongWriter
Unleashing 10,000+ Word Generation from Long Context LLMs
LongWriter is an open-source framework and set of large language models designed to enable ultra-long text generation that can exceed 10,000 words while maintaining coherence and structure. Traditional large language models can process large inputs but often struggle to generate long outputs due to limitations in training data and alignment strategies. LongWriter addresses this challenge by introducing a specialized dataset and training approach that encourages models to produce longer responses. The system uses an agent-based pipeline called AgentWrite that decomposes large writing tasks into smaller subtasks, allowing the model to produce long documents section by section. ... -
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DeepSearcher
Open Source Deep Research Alternative to Reason and Search
DeepSearcher is an open-source “deep research” style system that combines retrieval with evaluation and reasoning to answer complex questions using private or enterprise data. It is designed around the idea that high-quality answers require more than top-k retrieval, so it orchestrates multi-step search, evidence collection, and synthesis into a comprehensive response. The project integrates with vector databases (including Milvus and related options) so organizations can index internal... -
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Dash Data Agent
Self-learning data agent that grounds its answers in layers of content
Dash is a self-learning data agent built by the Agno AI community that generates grounded answers to English queries over structured data by synthesizing SQL and reasoning based on six layers of context, improving automatically with each run. It sidesteps common limitations of simple text-to-SQL agents by incorporating multiple context layers — including schema structure, human annotations, known query patterns, institutional knowledge from docs, machine-discovered error patterns, and live runtime context — to generate SQL queries that are both technically correct and semantically meaningful. The system then executes those queries against a database and interprets the results, returning human-friendly insights not just raw rows, while learning from errors and successes to reduce repeated mistakes. -
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Trail of Bits Skills Marketplace
Trail of Bits Claude Code skills for security research, vulnerability
Trail of Bits Skills Marketplace is a specialized Claude Code skills marketplace built by the security research firm Trail of Bits that focuses on enhancing AI-assisted workflows for vulnerability discovery, testing, and secure development. The repository groups a set of plug-in skills tailored toward static analysis, code auditing, secure defaults detection, and other practices that matter in software security. Users can easily add the marketplace to a Claude Code environment, browse... -
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Fast3R
Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
...It represents a next-generation feedforward 3D reconstruction model capable of producing dense point clouds and camera poses for hundreds to thousands of images or video frames in a single inference pass—eliminating the need for slow, iterative structure-from-motion pipelines. Built on PyTorch Lightning and extending concepts from DUSt3R and Spann3r, Fast3R unifies multi-view geometry, depth estimation, and camera registration within a single transformer-based architecture. It outputs high-quality 3D scene representations from unordered or sequential views, scaling to large datasets and varied camera intrinsics. ... -
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JEPA
PyTorch code and models for V-JEPA self-supervised learning from video
...A context encoder ingests visible regions and predicts target embeddings for masked regions produced by a separate target encoder, avoiding low-level reconstruction losses that can overfit to texture. This makes learning focus on semantics and structure, yielding features that transfer well with simple linear probes and minimal fine-tuning. The repository provides training recipes, data pipelines, and evaluation utilities for image JEPA variants and often includes ablations that illuminate which masking and architectural choices matter. Because the objective is non-autoregressive and operates in embedding space, JEPA tends to be compute-efficient and stable at scale. ... -
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YData Synthetic
Synthetic data generators for tabular and time-series data
...Several example Jupyter Notebooks and Python scripts are included, to show how to use the different architectures. YData synthetic has now a UI interface to guide you through the steps and inputs to generate structure tabular data. The streamlit app is available form v1.0.0 onwards. -
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Paper2Slides
From Paper to Presentation in One Click
Paper2Slides is an automation tool that converts research papers, reports, and other documents into polished slide decks and posters with minimal manual effort. It is designed to replace the repetitive work of turning dense technical documents into presentation-friendly structure by extracting key points, figures, and data into a coherent visual narrative. The system supports multiple input formats, so you can process PDFs and common office documents rather than being locked to a single file type. It uses an extraction approach intended to capture critical insights comprehensively, including important visuals and data points that often get missed in naive summarization. ... -
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Map-Anything
MapAnything: Universal Feed-Forward Metric 3D Reconstruction
Map-Anything is a universal, feed-forward transformer for metric 3D reconstruction that predicts a scene’s geometry and camera parameters directly from visual inputs. Instead of stitching together many task-specific models, it uses a single architecture that supports a wide range of 3D tasks—multi-image structure-from-motion, multi-view stereo, monocular metric depth, registration, depth completion, and more. The model flexibly accepts different input combinations (images, intrinsics, poses, sparse or dense depth) and produces a rich set of outputs including per-pixel 3D points, camera intrinsics, camera poses, ray directions, confidence maps, and validity masks. ... -
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vJEPA-2
PyTorch code and models for VJEPA2 self-supervised learning from video
...Instead of reconstructing pixels, it predicts the missing high-level embeddings of masked space-time regions using a context encoder and a slowly updated target encoder. This objective encourages the model to learn semantics, motion, and long-range structure without the shortcuts that pixel-level losses can invite. The architecture is designed to scale: spatiotemporal ViT backbones, flexible masking schedules, and efficient sampling let it train on long clips while remaining stable. Trained representations transfer well to downstream tasks such as action recognition, temporal localization, and video retrieval, often with simple linear probes or light fine-tuning. ... -
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python-small-examples
Focus on creating classic Python small examples and cases
python-small-examples is an open-source educational repository that contains hundreds of concise Python programming examples designed to illustrate practical coding techniques. The project focuses on teaching programming concepts through small, focused scripts that demonstrate common tasks in data processing, visualization, and general programming. Each example highlights a specific function or programming pattern so that learners can quickly understand how to apply Python features in... -
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MuseGAN
An AI for Music Generation
...This representation allows the neural network to capture rhythmic patterns, harmonic relationships, and structural dependencies across instruments. The architecture is based on convolutional GAN models that learn temporal musical structure and inter-track relationships from training data. The project was trained using the Lakh Pianoroll Dataset, a large collection of multitrack musical sequences derived from MIDI files. -
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refinery
Open-source choice to scale, assess and maintain natural language data
The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact. You are one of the people we've built refinery for. refinery helps you to build better NLP models in a data-centric approach. Semi-automate your labeling, find low-quality subsets in your training data, and monitor your data in one place. refinery doesn't get rid of manual labeling, but it makes sure that your valuable time is spent well. Also, the makers... -
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AutoViz
Automatically Visualize any dataset, any size
...The primary goal of the project is to help data scientists and analysts quickly understand patterns, relationships, and anomalies within datasets without manually writing complex plotting code. With a single command, the library can automatically generate dozens of charts and graphs that reveal insights into the structure and quality of the data. AutoViz supports a wide range of visualization types including scatter plots, histograms, bar charts, and correlation plots, making it suitable for analyzing both structured and large datasets. The system also includes built-in tools for evaluating data quality and identifying potential issues such as missing values or unusual distributions. ... -
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SoniTranslate
Synchronized Translation for Videos
...Under the hood, it uses advanced speech and diarization models to separate speakers, align audio with timecodes and respect subtitle timing, which lets the generated dub track stay in sync with the original video structure. The project supports a wide range of languages for translation, spanning major world languages (English, Spanish, French, German, Chinese, Arabic, etc.) and many regional or less widely spoken languages, making it suitable for broad internationalization. It offers multiple usage modes, including a Colab notebook for cloud-based experimentation, a Hugging Face Space demo for quick trials, and instructions. -
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ChatGPT Clone
ChatGPT interface with better UI
ChatGPT Clone demonstrates a ChatGPT-style conversational interface wired to large-language-model backends, packaged so developers can self-host and extend. The goal is to replicate the core chat UX—message history, streaming tokens, code blocks, and system prompts—while letting you plug in different provider APIs or local models. It showcases a clean separation between the web client and the message orchestration layer so you can experiment with prompts, roles, and memory strategies. The... -
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WhisperJAV
A subtitle generator for Japanese Adult Videos.
A subtitle generator for Japanese Adult Videos. Transformer-based ASR architectures like Whisper suffer significant performance degradation when applied to the spontaneous and noisy domain of JAV. This degradation is driven by specific acoustic and temporal characteristics that defy the statistical distributions of standard training data. -
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Django friendly finite state machine
Django friendly finite state machine support
...You may also take a look at the Django-fsm-admin project containing a mixin and template tags to integrate Django-fsm state transitions into the Django admin. FSM really helps to structure the code, especially when a new developer comes to the project. FSM is most effective when you use it for some sequential steps. Transition logging support could be achieved with help of django-fsm-log package. -
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snorkel
A system for quickly generating training data with weak supervision
...The Snorkel project started at Stanford in 2016 with a simple technical bet: that it would increasingly be the training data, not the models, algorithms, or infrastructure, that decided whether a machine learning project succeeded or failed. Given this premise, we set out to explore the radical idea that you could bring mathematical and systems structure to the messy and often entirely manual process of training data creation and management, starting by empowering users to programmatically label, build, and manage training data. Snorkel Flow, an end-to-end machine learning platform for developing and deploying AI applications. Snorkel Flow incorporates many of the concepts of the Snorkel project with a range of newer techniques around weak supervision modeling, data augmentation, multi-task learning, data slicing and structuring. -
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Bard API
The unofficial python package that returns response of Google Bard
...Please note that the bardapi is not a free service, but rather a tool provided to assist developers with testing certain functionalities due to the delayed development and release of Google Bard's API. It has been designed with a lightweight structure that can easily adapt to the emergence of an official API. Therefore, I strongly discourage using it for any other purposes. If you have access to official PaLM-2 API, replace the provided response with the corresponding official code. -
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Plugins Quickstart
Get a ChatGPT plugin up and running in under 5 minutes
plugins-quickstart is a starter project created by OpenAI to help developers build and deploy ChatGPT plugins quickly. It provides a minimal but complete example of how to structure a plugin, implement an API, and define the necessary configuration files. The repository demonstrates how a plugin can be served, authenticated, and integrated with ChatGPT for real-world use. By including both the backend code and plugin manifest, it guides developers through the end-to-end development workflow. This makes it a useful resource for those experimenting with extending ChatGPT capabilities or adding custom functionality to their own workflows. ... -
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sketch
AI code-writing assistant that understands data content
Sketch is an open-source AI-powered data analysis assistant designed specifically for pandas users, enabling natural language interaction with tabular datasets to generate code, insights, and transformations. It works by summarizing the structure and statistical properties of a dataset and providing that context to a language model, allowing it to generate highly relevant and accurate responses tailored to the data. The tool integrates directly into pandas dataframes through an extension, making it easy to use within existing Python workflows without requiring additional IDE plugins. ... -
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State of Open Source AI
Clarity in the current fast-paced mess of Open Source innovation
...It acts as both a snapshot and a guide: readers can see what’s “hot now” in open AI infrastructure, what open licensing or governance issues are emerging, how deployment options compare, and what gaps remain. Because the AI domain moves quickly, part of the aim is to make the content maintainable and updateable by the community. The structure includes chapters or sections about model formats, evaluation benchmarks, hardware/backends, MLOps systems, alignment and safety issues, and open datasets. The repository contains the text (in Markdown or similar), configuration for build or publishing (static site or e-book), and contributor guidelines. -
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ControlNet
Let us control diffusion models
...Rather than training from scratch, ControlNet “locks” the weights of a pre-trained diffusion model and introduces a parallel trainable branch that learns additional conditions—like edges, depth maps, segmentation, human pose, scribbles, or other guidance signals. This allows the system to control where and how the model should focus during generation, enabling users to steer layout, structure, and content more precisely than prompt text alone. The project includes many trained model variants that accept different types of conditioning (e.g., canny edge input, normal maps, skeletal pose) and produce improved fidelity in stable diffusion outputs. It is widely adopted in the community as a go-to tool for semi-automatic image generation workflows, especially when users want structure plus creative freedom. -
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Demucs
Code for the paper Hybrid Spectrogram and Waveform Source Separation
...The system is based on a U-Net-like convolutional architecture combined with recurrent and transformer elements to capture both short-term and long-term temporal structure. It processes raw waveforms directly rather than spectrograms, allowing for higher-quality reconstruction and fewer artifacts in separated tracks. The repository includes pretrained models for common tasks such as isolating vocals, drums, bass, and accompaniment from stereo music, achieving state-of-the-art results in benchmarks like MUSDB18. ...
