Search Results for "model-builder" - Page 39
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Showing 1975 open source projects for "model-builder"
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1
CutLER
Code release for Cut and Learn for Unsupervised Object Detection
...The method follows a “Cut-and-LEaRn” recipe: bootstrap object proposals, refine them iteratively, and train detection/segmentation heads to discover objects across diverse datasets. The codebase provides training and inference scripts, model configs, and references to benchmarking results that report large gains over prior unsupervised baselines. It’s intended for researchers exploring self-supervised and unsupervised recognition, offering a practical path to scale beyond costly labeled corpora. The README links papers and gives a high-level overview of components and expected outputs, with pointers to demos and assets. ... -
2
Watermark Anything
Official implementation of Watermark Anything with Localized Messages
...Unlike traditional watermarking methods that rely on uniform embedding, WAM supports spatially localized watermarks, enabling targeted protection of specific image regions or objects. The model is trained to balance imperceptibility, ensuring minimal visual distortion, with robustness against transformations and edits such as cropping or motion. -
3
Coconut
Training Large Language Model to Reason in a Continuous Latent Space
Coconut is the official PyTorch implementation of the research paper “Training Large Language Models to Reason in a Continuous Latent Space.” The framework introduces a novel method for enhancing large language models (LLMs) with continuous latent reasoning steps, enabling them to generate and refine reasoning chains within a learned latent space rather than relying solely on discrete symbolic reasoning. It supports training across multiple reasoning paradigms—including standard... -
4
CoreNet
CoreNet: A library for training deep neural networks
CoreNet is Apple’s internal deep learning framework for distributed neural network training, designed for high scalability, low-latency communication, and strong hardware efficiency. It focuses on enabling large-scale model training across clusters of GPUs and accelerators by optimizing data flow and parallelism strategies. CoreNet provides abstractions for data, tensor, and pipeline parallelism, allowing models to scale without code duplication or heavy manual configuration. Its distributed runtime manages synchronization, load balancing, and mixed-precision computation to maximize throughput while minimizing communication bottlenecks. ... -
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5
Sapiens
High-resolution models for human tasks
...It integrates sensory inputs such as vision, audio, and proprioception into a unified learning architecture that allows agents to understand and adapt to their surroundings dynamically. The project emphasizes long-horizon reasoning and cross-modal grounding—connecting language, perception, and action into a single agentic model capable of following abstract goals. It includes simulation environments, datasets, and benchmarks for testing grounded understanding, imitation learning, and decision-making. The system’s modular pipeline supports both imitation-based and reinforcement-based training strategies, allowing flexible experimentation with different embodiments and tasks. -
6
SlowFast
Video understanding codebase from FAIR for reproducing video models
...The slow pathway encodes semantic context by sampling frames sparsely, while the fast pathway captures motion and fine temporal cues by operating on densely sampled frames with fewer channels. Together, these two pathways complement each other, allowing the network to model both appearance and motion without excessive computational cost. The architecture is modular and supports tasks like action recognition, temporal localization, and video segmentation, performing strongly on benchmarks like Kinetics and AVA. The repository provides training recipes, pretrained models, and distributed pipelines optimized for large-scale video datasets. -
7
ML for Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
...The repository includes quizzes, solutions, and instructor materials to make the content usable in classrooms or self-study. It emphasizes ethical considerations and model evaluation—accuracy is not the only metric—so students learn to validate and communicate results responsibly. By the end, participants can build end-to-end ML experiments, interpret outputs, and iterate with confidence rather than just copying code. -
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Hunyuan3D-1
A Unified Framework for Text-to-3D and Image-to-3D Generation
Hunyuan3D-1 is an earlier version in the same 3D generation line (the unified framework for text-to-3D and image-to-3D tasks) by Tencent Hunyuan. It provides a framework combining shape generation and texture synthesis, enabling users to create 3D assets from images or text conditions. While less advanced than version 2.1, it laid the foundations for the later PBR, higher resolution, and open-source enhancements. (Note: less detailed public documentation was found for Hunyuan3D-1 compared to... -
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PaddleSpeech
Easy-to-use Speech Toolkit including Self-Supervised Learning model
PaddleSpeech is an open-source toolkit on PaddlePaddle platform for a variety of critical tasks in speech and audio, with state-of-art and influential models. Via the easy-to-use, efficient, flexible and scalable implementation, our vision is to empower both industrial application and academic research, including training, inference & testing modules, and deployment process. Low barriers to install, CLI, Server, and Streaming Server is available to quick-start your journey. We provide... -
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Best-of Machine Learning with Python
A ranked list of awesome machine learning Python libraries
This curated list contains 900 awesome open-source projects with a total of 3.3M stars grouped into 34 categories. All projects are ranked by a project-quality score, which is calculated based on various metrics automatically collected from GitHub and different package managers. If you like to add or update projects, feel free to open an issue, submit a pull request, or directly edit the projects.yaml. Contributions are very welcome! General-purpose machine learning and deep learning... -
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YOLOv9
Learning What You Want to Learn Using Programmable Gradient Info
...The project introduces Programmable Gradient Information and the GELAN architecture to improve gradient flow, parameter efficiency, and train-from-scratch performance. It provides scripts and model assets for training, testing, and running inference on detection tasks. YOLOv9 is designed for real-time detection scenarios where both accuracy and efficiency matter. It is especially relevant for researchers and engineers comparing next-generation YOLO architectures or building production computer vision systems. -
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Moshi
A speech-text foundation model for real time dialogue
Moshi is a speech-text foundation model and full-duplex spoken dialogue framework. It uses Mimi, a state-of-the-art streaming neural audio codec. Mimi processes 24 kHz audio, down to a 12.5 Hz representation with a bandwidth of 1.1 kbps, in a fully streaming manner (latency of 80ms, the frame size), yet performs better than existing, non-streaming, codecs like SpeechTokenizer (50 Hz, 4kbps), or SemantiCodec (50 Hz, 1.3kbps). -
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nano-graphrag
A simple, easy-to-hack GraphRAG implementation
nano-graphrag is a lightweight implementation of the GraphRAG approach designed to simplify experimentation with graph-based retrieval-augmented generation systems. GraphRAG expands traditional RAG pipelines by constructing knowledge graphs from documents and using relationships between entities to improve the quality and reasoning of AI responses. The nano-GraphRAG project focuses on reducing complexity by providing a compact and readable codebase that preserves the core functionality of... -
14
Lyra 2
Project Lyra: Open Generative 3D World Models
The Lyra 2 project is a research-driven framework developed by NVIDIA that focuses on building open generative 3D world models using advanced diffusion-based techniques. It enables the creation of fully explorable 3D environments from minimal inputs such as a single image or video, leveraging self-distillation methods to generate consistent spatial representations. The system evolves across versions, with newer iterations introducing long-horizon generation and improved 3D consistency across... -
15
Open Gauss
Project-scoped Lean workflow orchestrator from Math, Inc.
...It is based on the PostgreSQL ecosystem but significantly extends its capabilities through architectural optimizations, AI-driven features, and enterprise-level enhancements. The database organizes data using the relational model, storing structured information in tables composed of rows and columns while supporting standard SQL for querying and management. One of its defining strengths is its optimization for multi-core and distributed environments, allowing it to efficiently process high volumes of concurrent transactions with minimal latency. OpenGauss also incorporates AI-based optimization techniques, such as intelligent query planning, performance prediction, and automated tuning, which help reduce operational complexity and improve efficiency. -
16
Memori
SQL-native memory layer enabling persistent context for AI agents
...It extracts structured information such as facts, preferences, rules, and summaries from interactions and stores them in standard SQL databases for later retrieval. By recalling relevant context during future model calls, Memori helps AI agents produce more consistent and context-aware responses while reducing the need to repeatedly provide background information. Memori is designed to work with multiple LLM providers, data stores, and AI frameworks, allowing it to integrate into existing software architectures without requiring major changes. -
17
deepjazz
Deep learning driven jazz generation using Keras & Theano
...The project was originally created during a hackathon and was designed to show how neural networks can emulate creative tasks traditionally associated with human musicians. The repository includes preprocessing scripts for preparing MIDI data, training scripts for building the neural network model, and code for generating new compositions. -
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Kaggle Solutions
Collection of Kaggle Solutions and Ideas
Kaggle Solutions is an open-source repository that compiles winning solutions, insights, and educational resources from hundreds of Kaggle data science competitions. The repository acts as a knowledge base for competitive machine learning by collecting solution write-ups, discussion threads, code notebooks, and tutorial resources shared by top Kaggle participants. Each competition entry typically includes information about the dataset, evaluation metrics, modeling strategies, and techniques... -
19
FlexLLMGen
Running large language models on a single GPU
FlexLLMGen is an open-source inference engine designed to run large language models efficiently on limited hardware resources such as a single GPU. The system focuses on high-throughput generation workloads where large batches of text must be processed quickly, such as large-scale data extraction or document analysis tasks. Instead of requiring expensive multi-GPU systems, the framework uses techniques such as memory offloading, compression, and optimized batching to run large models on... -
20
LongBench
LongBench v2 and LongBench (ACL 25'&24')
LongBench is a comprehensive benchmark designed to evaluate the ability of large language models to understand and reason over very long textual contexts. Traditional language model benchmarks typically evaluate tasks involving relatively short inputs, which does not reflect many real-world applications such as analyzing large documents or entire code repositories. LongBench addresses this gap by providing datasets that require models to process and reason over long sequences of text across multiple tasks. The benchmark includes multiple categories such as single-document question answering, multi-document reasoning, summarization, long dialogue understanding, and code analysis. ... -
21
Prompt Poet
Streamlines and simplifies prompt design for both developers
Prompt Poet is an open-source framework designed to simplify the creation, organization, and maintenance of prompts for large language model applications. The project focuses on transforming prompt engineering into a structured design process rather than ad-hoc string manipulation within application code. It allows developers and non-technical users to build prompts using templated configurations based on YAML and Jinja2, which makes prompts easier to compose, reuse, and modify across different environments. ... -
22
KVCache-Factory
Unified KV Cache Compression Methods for Auto-Regressive Models
...In large language models, the key-value cache stores intermediate attention states that enable efficient token generation during inference, but these caches can consume large amounts of GPU memory when handling long contexts. KVCache-Factory provides a platform for implementing and evaluating multiple compression strategies that reduce memory usage while preserving model performance. The framework integrates several state-of-the-art methods such as PyramidKV, SnapKV, H2O, and StreamingLLM, allowing researchers to compare and experiment with different approaches within the same environment. It also supports advanced inference configurations such as Flash Attention v2 and multi-GPU inference setups for very large models. -
23
Agentless
An agentless approach to automatically solve software development
...When solving a problem, the system first performs localization to determine which files, functions, or code segments are most likely responsible for the issue. It then generates multiple candidate patches for the identified locations using language model reasoning and diff-style edits. In the final stage, the framework validates potential patches by running regression tests and additional reproduction tests to confirm whether the fix resolves the original error. Based on these results, the system ranks the candidate patches and selects the most reliable solution to submit. -
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AgentBench
A Comprehensive Benchmark to Evaluate LLMs as Agents (ICLR'24)
AgentBench is an open-source benchmark designed to evaluate the capabilities of large language models when used as autonomous agents. Unlike traditional language model benchmarks that focus on static text tasks, AgentBench measures how models perform in interactive environments that require planning, reasoning, and decision-making. The benchmark includes multiple environments that simulate realistic scenarios such as web interaction, database querying, and problem solving tasks. These environments require agents to interpret instructions, take actions, and adapt their strategies based on feedback from the environment. ... -
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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...
