Search Results for "path-setting"
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Showing 81 open source projects for "path-setting"
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Pixel2Motion
AI logo animation skill: turn raster logos into smooth SVG animation
...It is built for designers and developers who need polished logo reveals without manually rebuilding every asset from scratch. Its workflow emphasizes visual quality, semantic SVG structure, and motion QA evidence. It also includes scripts for overlay checks, path audits, frame captures, and deterministic animation previews. -
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HRM-Text
1B text generation model based on the HRM architecture
...Its training stack includes PrefixLM sequence packing, FlashAttention 3 kernels, PyTorch FSDP2, evaluation scripts, and checkpoint conversion tools. The repository supports reference pretraining runs for smaller and larger configurations, with Hopper-class GPUs expected for the attention path. It is useful for researchers and engineers exploring efficient language model pretraining, reasoning-focused architectures, and reproducible foundation model experiments. -
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llms-from-scratch-cn
Build a large language model from 0 only with Python foundation
llms-from-scratch-cn is an educational open-source project designed to teach developers how to build large language models step by step using practical code and conceptual explanations. The repository provides a hands-on learning path that begins with the fundamentals of natural language processing and gradually progresses toward implementing full GPT-style architectures from the ground up. Rather than focusing on using pre-trained models through APIs, the project emphasizes understanding the internal mechanisms of modern language models, including tokenization, attention mechanisms, transformer architecture, and training workflows. ... -
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MCP Shell Server
Shell command execution server implementing the Model Context Protocol
A secure shell command execution server implementing the Model Context Protocol (MCP), allowing remote execution of whitelisted shell commands with support for standard input. -
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LLaMA 3
The official Meta Llama 3 GitHub site
...As the Llama stack evolved, Meta consolidated repositories and marked this one deprecated, pointing users to newer, centralized hubs for models, utilities, and docs. Even as a deprecated repo, it documents the transition path and preserves references that clarify how Llama 3 releases map into the current ecosystem. Practically, it functioned as a bridge between Llama 2 and later Llama releases by standardizing distribution and starter code for inference and fine-tuning. Teams still treat it as historical reference material for version lineage and migration notes. -
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Gorden Super PPT Skills
AI PPT Track Terminator, the strongest PPT Skill ever
Gorden Super PPT Skills is an AI presentation skill package for generating high-density visual presentations and converting them into editable PowerPoint files. The workflow is split into three skills that can be used separately or together. One skill generates image-based presentation pages from a topic or content brief. Another skill reconstructs image slides into editable PPTX files by separating backgrounds, layout structures, icons, decorations, and text. The orchestration skill chains... -
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Get Physics Done (GPD)
The first open-source agentic AI physicist
...It aims to simplify the process of performing simulations, calculations, and experimental analysis by providing structured workflows that integrate computational physics methods with reproducible research practices. The project focuses on reducing the friction involved in setting up experiments, running simulations, and analyzing results, allowing researchers to focus more on scientific insight rather than infrastructure. It emphasizes automation and reproducibility, ensuring that experiments can be easily replicated and extended by other researchers. The framework is adaptable to different areas of physics, making it suitable for both theoretical and applied research scenarios. -
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CS-Ebook
Curated list of classic, high-quality computer science books
...CS-Ebook is actively maintained and updated to reflect relevant and modern resources while preserving foundational texts. Its organized structure allows users to navigate topics efficiently and follow a progressive learning path. Contributions are encouraged, ensuring the list evolves with community input and continues to highlight valuable resources. -
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Scikit-LLM
Seamlessly integrate LLMs into scikit-learn
...If this is not the case, a label will be selected randomly (label probabilities are proportional to label occurrences in the training set). Note: unlike in a typical supervised setting, the performance of a zero-shot classifier greatly depends on how the label itself is structured. It has to be expressed in natural language, descriptive, and self-explanatory. -
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ElevenLabs Python
The official Python SDK for the ElevenLabs API
...It exposes ElevenLabs’ main models such as Eleven Multilingual v2, Eleven Flash v2.5, and Eleven Turbo v2.5, each targeting different trade-offs between latency, cost, and quality. The SDK is designed for quick setup: after installing the package and setting an API key, you can generate speech in multiple languages and play or process the resulting audio bytes. It includes helper utilities (like play and stream) so you can either play audio locally or integrate it into your own playback or networking pipeline. -
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Arize Phoenix
Uncover insights, surface problems, monitor, and fine tune your LLM
...Deep Learning Models (CV, LLM, and Generative) are an amazing technology that will power many of future ML use cases. A large set of these technologies are being deployed into businesses (the real world) in what we consider a production setting. -
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SAM 3D Body
Code for running inference with the SAM 3D Body Model 3DB
...The repository provides Python code to run inference, utilities to download checkpoints from Hugging Face, and demo scripts that turn images into 3D meshes and visualizations. There are Jupyter notebooks that walk you through setting up the model, running it on example images, and visualizing outputs in 3D, making it approachable even if you are not a 3D expert. -
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Ollama RAG Chatbot
Chat with multiple PDFs locally
...The main value of the project is its ability to process multiple PDF inputs and turn them into a question-answering workflow centered on document retrieval. With Docker support, script-based setup, optional ngrok exposure, and a clear local run path, it serves as a compact starter project for people who want a hands-on, self-hosted PDF chat system. -
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RWARE
MuA multi-agent reinforcement learning environment
robotic-warehouse is a simulation environment and framework for robotic warehouse automation, enabling research and development of AI and robotic agents to manage warehouse logistics, such as item picking and transport. -
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DeepVariant
DeepVariant is an analysis pipeline that uses a deep neural networks
DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data. DeepVariant is a deep learning-based variant caller that takes aligned reads (in BAM or CRAM format), produces pileup image tensors from them, classifies each tensor using a convolutional neural network, and finally reports the results in a standard VCF or gVCF file. DeepTrio is a deep learning-based trio variant caller built on top of DeepVariant. DeepTrio... -
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Weights and Biases
Tool for visualizing and tracking your machine learning experiments
...Set wandb.config once at the beginning of your script to save your hyperparameters, input settings (like dataset name or model type), and any other independent variables for your experiments. This is useful for analyzing your experiments and reproducing your work in the future. Setting configs also allows you to visualize the relationships between features of your model architecture or data pipeline and model performance. -
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All-in-RAG
Big Model Application Development Practice 1
All-in-RAG is an open-source educational project designed to teach developers how to build applications using retrieval-augmented generation techniques. The repository provides a structured learning path that covers both theoretical foundations and practical implementation steps for RAG systems. It explains the full development pipeline required to create knowledge-aware AI assistants, including data preparation, document indexing, vector embedding generation, and retrieval strategies. The project also explores advanced topics such as hybrid retrieval methods, query optimization, and evaluation techniques for improving system accuracy. ... -
18
dstack
Open-source tool designed to enhance the efficiency of workloads
dstack is an open-source tool designed to enhance the efficiency of running ML workloads in any cloud (AWS, GCP, Azure, Lambda, etc). It streamlines development and deployment, reduces cloud costs, and frees users from vendor lock-in. -
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Map-Anything
MapAnything: Universal Feed-Forward Metric 3D Reconstruction
...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. Its inference path is fully feed-forward with optional mixed-precision and memory-efficient modes, making it practical to scale to long image sequences while keeping latency predictable. -
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OpenHome Abilities
Open-source abilities for OpenHome agents
...The system is meant to support a wide range of voice-driven actions, from API calls and media playback to quiz flows, device control, and multi-turn conversations, so it functions as a practical extension framework rather than a narrow template library. The repository includes official abilities maintained by the OpenHome team as well as community-contributed ones, creating both a stable baseline and a path for outside developers to publish their own work. -
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mcpo
A simple, secure MCP-to-OpenAPI proxy server
...The project emphasizes “dead-simple” setup and pairs with Open WebUI documentation that shows end-to-end integration. It supports running multiple tools and makes them discoverable to clients that expect Swagger/JSON schemas. In practice, mcpo shortens the path from a local MCP tool to a shareable, network-accessible microservice. -
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MLJAR Studio
Python package for AutoML on Tabular Data with Feature Engineering
We are working on new way for visual programming. We developed a desktop application called MLJAR Studio. It is a notebook-based development environment with interactive code recipes and a managed Python environment. All running locally on your machine. We are waiting for your feedback. The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. It is designed to save time for a data scientist. It abstracts the common way to preprocess the data,... -
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ML Retreat
Machine Learning Journal for Intermediate to Advanced Topics
ML Retreat is an open-source learning repository that serves as a structured journal documenting advanced topics in machine learning and artificial intelligence. The project compiles detailed notes, technical explanations, and curated resources that guide readers through complex concepts across modern AI research. Rather than functioning as a traditional tutorial series, the repository is organized as a learning journey that progressively explores increasingly advanced subjects. Topics... -
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TimesFM
Pretrained time-series foundation model developed by Google Research
TimesFM is a pretrained time-series foundation model from Google Research built for forecasting tasks, designed to generalize across many domains without requiring extensive per-dataset retraining. It provides a decoder-only model approach to forecasting, aiming for strong performance even in zero-shot or low-data settings where traditional models often struggle. The project includes code and an inference API intended to make it practical to run forecasts programmatically, with options to... -
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Granite TSFM
Foundation Models for Time Series
granite-tsfm collects public notebooks, utilities, and serving components for IBM’s Time Series Foundation Models (TSFM), giving practitioners a practical path from data prep to inference for forecasting and anomaly-detection use cases. The repository focuses on end-to-end workflows: loading data, building datasets, fine-tuning forecasters, running evaluations, and serving models. It documents the currently supported Python versions and points users to where the core TSFM models are hosted and how to wire up service components. ...