Search Results for "python windows" - Page 21
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1288 projects for "python windows" with 2 filters applied:
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BCEmbedding
Netease Youdao's open-source embedding and reranker models
BCEmbedding is NetEase Youdao’s open-source embedding and reranker model project for retrieval-augmented generation workflows. It includes an EmbeddingModel for semantic vector generation and a RerankerModel for refining and ordering search results. The project is optimized for bilingual and cross-lingual retrieval, especially across Chinese and English. It is used as a foundation for RAG systems such as QAnything and other Youdao products. The models are designed to work directly without... -
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ML Intern
ML engineer that reads papers, trains models, and ships ML models
ML Intern is a repository by Hugging Face that provides educational content and projects aimed at helping learners gain practical experience in machine learning and AI development. It is designed to simulate the experience of working as a machine learning intern, offering tasks and exercises that mirror real-world workflows. The project includes tutorials, datasets, and example implementations that guide users through different aspects of ML development. It emphasizes hands-on learning,... -
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TorchCode
Practice implementing softmax, attention, GPT-2 and more
TorchCode is an interactive learning and practice platform designed to help developers master PyTorch by implementing core machine learning operations and architectures from scratch. It is structured similarly to competitive programming platforms like LeetCode but focuses specifically on tensor operations and deep learning concepts. The platform provides a collection of curated problems that cover fundamental topics such as activation functions, normalization layers, attention mechanisms,... -
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Machine Learning Study
This repository is for helping those interested in machine learning
Machine Learning Study is an educational repository containing tutorials and study materials related to machine learning and data science using Python. The project compiles notebooks, explanatory documents, and practical code examples that illustrate common machine learning workflows. Topics covered include supervised learning algorithms, feature engineering, model training, and performance evaluation techniques. The repository is structured as a learning resource that guides readers through... -
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EnvPool
C++-based high-performance parallel environment execution engine
EnvPool is a fast, asynchronous, and parallel RL environment library designed for scaling reinforcement learning experiments. Developed by SAIL at Singapore, it leverages C++ backend and Python frontend for extremely high-speed environment interaction, supporting thousands of environments running in parallel on a single machine. It's compatible with Gymnasium API and RLlib, making it suitable for scalable training pipelines. -
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Kubeflow Trainer
Distributed AI Model Training and LLM Fine-Tuning on Kubernetes
Kubeflow Trainer is a Kubernetes-native platform designed for scalable, distributed training and fine-tuning of machine learning models, particularly large language models, across multi-node and multi-GPU environments. It extends the Kubeflow ecosystem by providing a unified framework for orchestrating training workloads using Kubernetes primitives, enabling seamless scaling from single-machine experiments to large production clusters. The platform supports a wide range of machine learning... -
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runprompt
Run LLM prompts from your shell
runprompt is an interactive command launcher and prompt utility that lets users bind shell commands, scripts, and workflows to quick keyboard shortcuts or natural-language queries, helping streamline repetitive terminal tasks and boost developer productivity. It functions as a lightweight, launcher-centric interface where you can type a phrase, partial command, or alias and have RunPrompt suggest or execute relevant actions instantly, reducing the need to memorize long commands or navigate... -
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SemIf
Semantic ifs from open models, on a 3090 at home
SemIf, formerly OpenJev, is an independent research project for making small semantic decisions with open language models. Instead of generating text and parsing it afterward, it reads probabilities directly from declared answer options. Each request supplies state, a runtime-defined question, and typed choices, allowing the model to behave like a semantic conditional. Shared-state execution can reuse a long context across many related decisions to reduce repeated computation. Backends... -
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Kev
Jev-like family of decision models built on top of Qwen3.5/3.8
Kev is a family of open decision models inspired by Jev and built on Qwen3.5 and Qwen3.8 foundations. It processes a document together with multiple typed questions and returns probabilities instead of generated prose. Supported question formats include yes-or-no, multiple choice, and ordered scoring. Checkpoints range from a compact 0.8B model for smaller hardware to a 27B version for high-end systems. The models include probability calibration and can run through CUDA, ROCm, or MLX... -
Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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MSA: Memory Sparse Attention
Trainable latent-memory framework for 100M-token contexts
MSA, or Memory Sparse Attention, is a research framework for scaling language-model memory to extremely long contexts. It replaces full attention over all tokens with sparse selection of compressed latent memory states. Document-wise rotary position encoding and top-k routing keep training and inference close to linear complexity. A tiered KV-cache design stores routing keys on GPU while larger content states can remain on CPU. Its Memory Parallel engine distributes scoring and transfers... -
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HRM-Text
1B text generation model based on the HRM architecture
HRM-Text is a one-billion-parameter text generation model and pretraining framework based on the Hierarchical Reasoning Model architecture. It is designed to make foundation model pretraining more accessible by reducing compute and data requirements compared with traditional scaling-heavy approaches. The system combines hierarchical recurrent design, task-completion strengthening, and latent-space reasoning. Its training stack includes PrefixLM sequence packing, FlashAttention 3 kernels,... -
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Dia2
TTS model capable of streaming conversational audio in realtime
Dia2 is a streaming dialogue text-to-speech model created by Nari Labs for generating conversational audio in real time. It is designed to begin producing speech before receiving the entire input text, which makes it useful for interactive voice applications. The model supports audio conditioning, allowing generated speech to follow a reference voice or conversational style more naturally. Dia2 provides 1B and 2B model checkpoints along with inference code for research and experimentation.... -
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Train LLM From Scratch
A straightforward method for training your LLM
Train LLM From Scratch is an educational PyTorch project that shows how to build and train a transformer-based language model from the ground up. It is based on the architecture described in Attention Is All You Need and is designed to make the training pipeline understandable rather than hidden behind a large framework. The repository walks through the process from downloading data to generating text with a trained model. It supports training smaller or larger models, including million- and... -
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Agent Control
Centralized agent control plane for governing runtime agent behavior
Agent Control is a centralized control plane for governing AI agent behavior at runtime across different frameworks and deployment environments. It lets teams define controls once and apply them consistently to agents without rewriting the agent’s core code. The platform evaluates agent inputs and outputs against configurable policies to reduce risks such as prompt injection, unsafe responses, sensitive data exposure, and policy drift. It is designed for production environments where... -
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GEO Content Writer
Backlog-row-first content production system for teams
GEO Content Writer is an AI-based content generation tool designed to create optimized content tailored for geographic and semantic search contexts. It focuses on producing articles, pages, and structured content that align with both traditional SEO requirements and emerging AI search patterns. The system leverages language models to generate content that is context-aware, location-specific, and optimized for discoverability. It supports automated workflows for generating large volumes of... -
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TurboQuant+
Implementation of TurboQuant (ICLR 2026)
TurboQuant Plus is an extended and enhanced version of quantization tooling aimed at improving neural network efficiency through advanced compression and optimization strategies. It builds upon the concept of reducing model precision to accelerate inference while attempting to maintain or recover accuracy through refined techniques. The project explores additional enhancements such as improved calibration, adaptive quantization, and potentially hybrid precision approaches that combine... -
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Book6_First-Course-in-Data-Science
From Addition, Subtraction, Multiplication, and Division to ML
Book6_First-Course-in-Data-Science is an open-source educational project that serves as part of the “Iris Book” series focused on teaching data science and machine learning concepts through a combination of mathematics, programming, and visualization. The repository contains draft chapters, supporting Python code, and visual materials designed to guide readers from basic mathematical operations toward practical machine learning understanding. The goal of the project is to make complex topics... -
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Data Science Articles from CodeCut
Collection of useful data science topics along with articles
The Data-science repository from CodeCutTech is a curated collection of educational content focused on practical tools and workflows used in modern data science projects. Instead of providing a single software package, the repository aggregates articles, tutorials, and examples covering many topics within the data science ecosystem. The materials address areas such as MLOps, data management, project organization, testing practices, visualization techniques, and productivity tools used by... -
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ML-NLP
This project is a common knowledge point and code implementation
ML-NLP is a large open-source repository that collects theoretical knowledge, practical explanations, and code examples related to machine learning, deep learning, and natural language processing. The project is designed primarily as a learning resource for algorithm engineers and students preparing for technical interviews in machine learning or NLP roles. It compiles important concepts that frequently appear in machine learning discussions, including neural network architectures, training... -
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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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LLMSurvey
A Survey of Large Language Models
LLMSurvey is an open-source research repository that aggregates academic papers, resources, and references related to large language models. The project is closely associated with the academic survey titled “A Survey of Large Language Models,” which provides a comprehensive overview of the development, architecture, capabilities, and societal implications of modern LLMs. The repository organizes hundreds of research papers into thematic sections that reflect the main areas of LLM research,... -
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Karpathy
An agentic Machine Learning Engineer
karpathy is an experimental agentic machine learning engineer framework designed to automate many aspects of the ML development workflow. The project sets up a sandboxed environment where an AI agent can access datasets, run experiments, and generate machine learning artifacts through a web interface. Its startup script automatically prepares the environment by creating a sandbox directory, installing key ML libraries, and launching the agent interface. The system is tightly integrated with... -
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Claude Code Hooks Mastery
Master Claude Code Hooks
Claude Code Hooks Mastery is a trending community-centric GitHub repository aimed at helping developers master Claude Code hooks — customizable integration points that let users extend, automate, and augment workflows when using Claude Code, an agentic terminal coding assistant. Although the project itself doesn’t include a single coherent application, it functions as a curated collection of advanced hook examples, best practices, and coding patterns that show how to tailor Claude Code to... -
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Auto-Deep-Research
Your Fully-Automated Personal AI Assistant
Auto-Deep-Research is a system designed to fully automate deep research workflows using language models, retrieval, planning, and multi-stage reasoning to produce structured research artifacts such as surveys, benchmarks, reports, and even prototypes without heavy human intervention. Users provide a research topic or multifaceted goal, and the system autonomously breaks the objective down into subtasks like literature collection, critical summarization, cross-comparison, citation extraction,... -
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TurboDiffusion
100–200× Acceleration for Video Diffusion Models
TurboDiffusion is an advanced open-source framework designed to dramatically accelerate video diffusion model generation, aiming for performance improvements on the order of 100–200× compared with traditional implementations while retaining high output quality. It achieves this by combining a suite of algorithmic and engineering optimizations, including attention acceleration techniques, efficient step distillation methods, and quantization strategies that reduce computational overhead. The...
