Search Results for "simple" - Page 6
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Showing 393 open source projects for "simple"
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files-to-prompt
Concatenate a directory full of files into a single prompt
files-to-prompt is a Python command-line tool that takes one or more files or entire directories and concatenates their contents into a single, LLM-friendly prompt. It walks the directory tree, outputting each file preceded by its relative path and a separator, so a model can understand which content came from where. The tool is aimed at workflows where you want to ask an LLM questions about a whole codebase, documentation set, or notes folder without manually copying files together. It... -
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Nerfstudio
A collaboration friendly studio for NeRFs
Nerfstudio provides a simple API that allows for a simplified end-to-end process of creating, training, and testing NeRFs. The library supports a more interpretable implementation of NeRFs by modularizing each component. With more modular NeRFs, we hope to create a more user-friendly experience in exploring the technology. This is a contributor-friendly repo with the goal of building a community where users can more easily build upon each other’s contributions. -
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Avalanche
End-to-End Library for Continual Learning based on PyTorch
...It contains all the major CL benchmarks (similar to what has been done for torchvision). Provides all the necessary utilities concerning model training. This includes simple and efficient ways of implementing new continual learning strategies as well as a set of pre-implemented CL baselines and state-of-the-art algorithms you will be able to use for comparison! Avalanche the first experiment of an End-to-end Library for reproducible continual learning research & development where you can find benchmarks, algorithms, etc. -
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PyG
Graph Neural Network Library for PyTorch
...In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support, distributed graph learning via Quiver, a large number of common benchmark datasets (based on simple interfaces to create your own), the GraphGym experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. All it takes is 10-20 lines of code to get started with training a GNN model (see the next section for a quick tour). -
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spaCy
Industrial-strength Natural Language Processing (NLP)
...It comes with pretrained statistical models and word vectors, convolutional neural network models, easy deep learning integration and so much more. spaCy is the fastest syntactic parser in the world according to independent benchmarks, with an accuracy within 1% of the best available. It's blazing fast, easy to install and comes with a simple and productive API. -
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Burr
Build applications that make decisions. Chatbots, agents, simulations
Burr makes it easy to develop applications that make decisions (chatbots, agents, simulations, etc...) from simple python building blocks. Burr works well for any application that uses LLMs and can integrate with any of your favorite frameworks. Burr includes a UI that can track/monitor/trace your system in real-time, along with pluggable persisters (e.g. for memory) to save & load application state. -
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MiniSom
MiniSom is a minimalistic implementation of the Self Organizing Maps
MiniSom is a minimalistic and Numpy-based implementation of the Self Organizing Maps (SOM). SOM is a type of Artificial Neural Network able to convert complex, nonlinear statistical relationships between high-dimensional data items into simple geometric relationships on a low-dimensional display. Minisom is designed to allow researchers to easily build on top of it and to give students the ability to quickly grasp its details. The project initially aimed for a minimalistic implementation of the Self-Organizing Map (SOM) algorithm, focusing on simplicity in features, dependencies, and code style. ... -
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SkyPilot
SkyPilot: Run AI and batch jobs on any infra
SkyPilot is a framework for running AI and batch workloads on any infra, offering unified execution, high cost savings, and high GPU availability. Run AI and batch jobs on any infra (Kubernetes or 12+ clouds). Get unified execution, cost savings, and high GPU availability via a simple interface. -
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EverMemOS
Long-term memory OS for AI with structured recall and context awarenes
...Its architecture combines memory storage, indexing, and retrieval with agent-level reasoning, allowing AI systems to make informed decisions based on prior interactions. EverMemOS goes beyond simple retrieval by actively applying stored knowledge to current tasks, improving personalization and consistency. EverMemOS uses a multi-stage memory lifecycle to convert raw dialogue into structured semantic data, supporting long-horizon reasoning and adaptive behavior across sessions. -
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vJEPA-2
PyTorch code and models for VJEPA2 self-supervised learning from video
...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. The repository typically includes end-to-end recipes—data pipelines, augmentation policies, training scripts, and evaluation harnesses. -
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Memvid
Video-based AI memory library. Store millions of text chunks in MP4
Memvid encodes text chunks as QR codes within MP4 frames to build a portable “video memory” for AI systems. This innovative approach uses standard video containers and offers millisecond-level semantic search across large corpora with dramatically less storage than vector DBs. It's self-contained—no DB needed—and supports features like PDF indexing, chat integration, and cloud dashboards. -
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Lepton AI
A Pythonic framework to simplify AI service building
A Pythonic framework to simplify AI service building. Cutting-edge AI inference and training, unmatched cloud-native experience, and top-tier GPU infrastructure. Ensure 99.9% uptime with comprehensive health checks and automatic repairs. -
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TorchMetrics
Machine learning metrics for distributed, scalable PyTorch application
...Automatic synchronization between multiple devices. Metric arithmetic. Similar to torch.nn, most metrics have both a module-based and a functional version. The functional versions are simple python functions that as input take torch.tensors and return the corresponding metric as a torch.tensor. -
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Ray
A unified framework for scalable computing
...Scale reinforcement learning (RL) with RLlib, a framework-agnostic RL library that ships with 30+ cutting-edge RL algorithms including A3C, DQN, and PPO. Easily build out scalable, distributed systems in Python with simple and composable primitives in Ray Core. -
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Timbal
Simple, performant, battle-tested framework for building reliable AI
Timbal is an open-source Python framework for building reliable AI applications with a small, transparent architecture. It provides autonomous agents and explicit workflows through the same calling interface and event stream. Developers define behavior with familiar async functions, Pydantic validation, and composable tools instead of relying on hidden orchestration. The framework supports multiple model providers and can automatically fail over between configured models. Human approval... -
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MemU
MemU is an open-source memory framework for AI companions
MemU is an agentic memory layer for LLM applications, specifically designed for AI companions. Transform your memory into an intelligent file system that automatically organizes, connects, and evolves with your memories. Simple, fast, and reliable memory infrastructure for AI applications. Powerful tools and dedicated support to scale your AI applications with confidence. Full proprietary features, commercial usage rights, and white-labeling options for your enterprise needs. SSO/RBAC integration and a dedicated algorithm team for scenario-specific optimization. ... -
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FastMCP
The fast, Pythonic way to build Model Context Protocol servers
FastMCP is a fast, Pythonic framework for building servers and clients using the Model Context Protocol (MCP). It abstracts away protocol complexity like serialization, validation, and error handling, letting developers focus entirely on their business logic. With simple decorators, you can expose Python functions as tools, resources, or prompts that AI agents can safely and efficiently use. FastMCP introduces clear abstractions—components, providers, and transforms—that make it easy to control what agents see and how they interact with your system. The framework is opinionated by design, ensuring best practices and protocol compliance are the default rather than an extra burden. ... -
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Lingua-Py
The most accurate natural language detection library for Python
Its task is simple: It tells you which language some text is written in. This is very useful as a preprocessing step for linguistic data in natural language processing applications such as text classification and spell checking. Other use cases, for instance, might include routing e-mails to the right geographically located customer service department, based on the e-mails' languages. -
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uAgents
A fast and lightweight framework for creating decentralized agents
uAgents is a library developed by Fetch.ai that allows for creating autonomous AI agents in Python. With simple and expressive decorators, you can have an agent that performs various tasks on a schedule or takes action on various events. -
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AutoKeras
AutoML library for deep learning
...If you followed previous steps to use virtualenv to install tensorflow, you can just activate the virtualenv. Currently, AutoKeras is only compatible with Python >= 3.7 and TensorFlow >= 2.8.0. AutoKeras supports several tasks with extremely simple interface. AutoKeras would search for the best detailed configuration for you. Moreover, you can override the base classes to create your own block. -
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MedicalGPT
MedicalGPT: Training Your Own Medical GPT Model with ChatGPT Training
MedicalGPT training medical GPT model with ChatGPT training pipeline, implementation of Pretraining, Supervised Finetuning, Reward Modeling and Reinforcement Learning. MedicalGPT trains large medical models, including secondary pre-training, supervised fine-tuning, reward modeling, and reinforcement learning training. -
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SimpleEnglish
Agent skill: make LLMs write docs in ASD-STE100
...It replaces vague, promotional, and overly complex AI prose with short, direct, testable statements. The instructions enforce controlled practices such as active voice, simple tenses, consistent terminology, limited sentence length, and one instruction per sentence. It is designed for documentation, error messages, runbooks, incident reports, release notes, prompts, and translation preparation rather than marketing copy. The repository includes a complete skill, reusable system prompts, examples, evaluations, and a compact version for limited context budgets. ... -
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MiMo Audio
Audio Language Models are Few-Shot Learners
MiMo Audio is an open-source audio language model project focused on few-shot learning across speech and audio tasks. It explores how large-scale next-token prediction can help audio models generalize from a few examples or simple instructions. The project includes MiMo-Audio-7B-Base and MiMo-Audio-7B-Instruct, along with a dedicated MiMo-Audio tokenizer. It supports audio understanding, speech intelligence, spoken dialogue, instruction-following audio generation, and text-to-speech-style tasks. The architecture combines audio tokenization, patch encoding, a language model, and patch decoding to make high-rate audio sequences more efficient to model. ... -
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MemClaw
Persistent memory for AI agent fleets (OSS)
...It also supports agent integrations through MCP and OpenClaw-style workflows, making it useful for multi-agent systems that need persistent recall. Its architecture goes beyond a simple vector database by adding rules about who can store, retrieve, and share each memory. caura-memclaw is best suited for teams building AI agents that need long-term memory, controlled sharing, compliance awareness, and safer cross-agent coordination. -
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How to Train Your GPT
Build a modern LLM from scratch. Every line commented
How to Train Your GPT is an interactive textbook that teaches users how to build, train, and run a modern language model from scratch. It is written for learners with minimal machine-learning background, using simple explanations, commented code, and practical examples. The project covers the same broad family of architecture behind systems such as GPT-style models, LLaMA-style models, Claude-style systems, and Mistral-style models. It includes chapters and topic explainers on tokenizers, embeddings, attention, RoPE, RMSNorm, SwiGLU, KV cache, AdamW, mixed precision, training loops, and inference. ...
