Search Results for "python ai game" - Page 36
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Showing 1487 open source projects for "python ai game"
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LitGPT
20+ high-performance LLMs with recipes to pretrain, finetune at scale
LitGPT is a collection of over 20 high-performance large language models (LLMs) accompanied by recipes to pretrain, finetune, and deploy them at scale. It provides implementations without abstractions, making it beginner-friendly while offering advanced features like flash attention and support for various precision levels. LitGPT is designed to run efficiently across multiple GPUs or TPUs, catering to both small-scale and large-scale deployments. -
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NVIDIA PhysicsNeMo
Open-source deep-learning framework for building and training
NVIDIA PhysicsNeMo is an open-source deep learning framework designed for building artificial intelligence models that incorporate physical laws and scientific knowledge into machine learning workflows. The framework focuses on the emerging field of physics-informed machine learning, where neural networks are used alongside physical equations to model complex scientific systems. PhysicsNeMo provides modular Python components that allow developers to create scalable training and inference... -
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rag-search
RAG Search API
rag-search is a lightweight Retrieval-Augmented Generation API service designed to provide structured semantic search and answer generation through a simple FastAPI backend. The project integrates web search, vector embeddings, and reranking logic to retrieve relevant context before passing it to a language model for response generation. It is built to be easily deployable, requiring only environment configuration and dependency installation to run a functional RAG service. The system... -
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ZAPI
ZAPI by Adopt AI is an open-source Python library
ZAPI is a developer-centric API framework that streamlines building, testing, and deploying APIs with strong type safety and minimal boilerplate, helping teams deliver backend services faster with fewer errors. It emphasizes a declarative router and schema model that uses types to define request and response formats, providing clear contracts for frontend and backend teams while automatically generating documentation. Zapi abstracts many repetitive tasks such as validation, authentication... -
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shimmy
Python-free Rust inference server
The shimmy project is a lightweight local inference server designed to run large language models with minimal overhead. Written primarily in Rust, the tool provides a small standalone binary that exposes an API compatible with the OpenAI interface, allowing existing applications to interact with local models without significant code changes. This compatibility enables developers to replace remote AI services with locally hosted models while keeping their existing software architecture... -
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MCP UI
SDK for building interactive UI components over MCP for AI tools
mcp-ui is a software development kit designed to bring interactive user interface capabilities to applications built on the Model Context Protocol (MCP). It enables developers to create rich, dynamic UI components that can be delivered from an MCP server and rendered seamlessly by a compatible client. Instead of returning only text responses, tools can provide structured UI resources such as HTML or remote-rendered components, allowing more engaging and functional interactions. mcp-ui... -
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LangServe
Helps developers deploy LangChain runnables and chains as a REST API
...The framework is built on top of FastAPI and uses Pydantic for request validation and structured data handling. It also includes client libraries that allow developers to interact with deployed chains from Python or JavaScript applications. LangServe is commonly used to deploy AI applications such as chatbots, document analysis pipelines, and agent-based systems that require scalable access through APIs. -
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CS-Ebook
Curated list of classic, high-quality computer science books
CS-Ebook is a curated repository that compiles high-quality and classic computer science books across a wide range of software-related fields. It focuses on depth over volume, selecting only well-regarded titles that support structured learning and long-term skill development. It spans core areas such as computer fundamentals, programming languages, software engineering, mathematics, data science, and artificial intelligence, making it suitable for learners at different stages. Rather than... -
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Step-Audio 2
Multi-modal large language model designed for audio understanding
Step-Audio2 is an advanced, end-to-end multimodal large language model designed for high-fidelity audio understanding and natural speech conversation: unlike many pipelines that separate speech recognition, processing, and synthesis, Step-Audio2 processes raw audio, reasons about semantic and paralinguistic content (like emotion, speaker characteristics, non-verbal cues), and can generate contextually appropriate responses — including potentially generating or transforming audio output. It... -
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Step-Audio
Open-source framework for intelligent speech interaction
Step-Audio is a unified, open-source framework aimed at building intelligent speech systems that combine both comprehension and generation: it integrates large language models (LLMs) with speech input/output to handle not only semantic understanding but also rich vocal characteristics like tone, style, dialect, emotion, and prosody. The design moves beyond traditional separate-component pipelines (ASR → text model → TTS), instead offering a multimodal model that ingests speech or audio and... -
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SDGym
Benchmarking synthetic data generation methods
The Synthetic Data Gym (SDGym) is a benchmarking framework for modeling and generating synthetic data. Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the... -
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AIMET
AIMET is a library that provides advanced quantization and compression
Qualcomm Innovation Center (QuIC) is at the forefront of enabling low-power inference at the edge through its pioneering model-efficiency research. QuIC has a mission to help migrate the ecosystem toward fixed-point inference. With this goal, QuIC presents the AI Model Efficiency Toolkit (AIMET) - a library that provides advanced quantization and compression techniques for trained neural network models. AIMET enables neural networks to run more efficiently on fixed-point AI hardware... -
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Xtuner
A Next-Generation Training Engine Built for Ultra-Large MoE Models
Xtuner is a large-scale training engine designed for efficient training and fine-tuning of modern large language models, particularly mixture-of-experts architectures. The framework focuses on enabling scalable training for extremely large models while maintaining efficiency across distributed computing environments. Unlike traditional 3D parallel training strategies, XTuner introduces optimized parallelism techniques that simplify scaling and reduce system complexity when training massive... -
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AppAgent
Multimodal Agents as Smartphone Users, an LLM-based multimodal agent
AppAgent is an open-source multimodal agent framework designed to enable large language models to operate smartphone applications through natural interactions with graphical user interfaces. The system allows an AI agent to interpret visual information from the screen and translate natural language instructions into actions such as tapping, swiping, and navigating between application screens. Instead of requiring backend access to application APIs, the framework interacts with apps the same... -
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Recognizers-Text
Recognition and resolution of numbers, units, date/time, etc.
Recognizers-Text is a multilingual text recognition library that extracts structured information such as dates, numbers, and currency values from unstructured text. -
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Guardrails
Framework for validating and controlling LLM outputs in AI apps
Guardrails is an open source Python framework designed to help developers build more reliable and controlled applications powered by large language models. It provides mechanisms for validating and constraining both the inputs sent to a model and the outputs generated by it, helping reduce risks such as harmful content, prompt injection, or inaccurate responses. Guardrails works by applying configurable guards that intercept and evaluate interactions with the model before results are... -
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Code2Prompt
Convert codebases into structured prompts optimized for LLM analysis
...The generated output can be saved to a file, printed to standard output, or copied to the clipboard for immediate use. In addition to the core command line interface, the project also includes a library, Python bindings, and an MCP server. -
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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... -
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OmAgent
Build multimodal language agents for fast prototype and production
OmAgent is an open-source Python framework designed to simplify the development of multimodal language agents that can reason, plan, and interact with different types of data sources. The framework provides abstractions and infrastructure for building AI agents that operate on text, images, video, and audio while maintaining a relatively simple interface for developers. -
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SkillOpt
Text-space optimizer that trains reusable natural-language skills
SkillOpt is a Microsoft research project for improving frozen LLM agents by optimizing reusable natural-language skill documents. Instead of changing model weights, it treats a compact skill file as the trainable state of the agent. The system learns from agent rollouts, reflection, bounded edits, and validation gates to produce better instructions over time. Its output is a deployable best_skill.md artifact that can be reused across agent tasks. The project is focused on making agents more... -
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NeMo Retriever Library
Document content and metadata extraction microservice
NeMo Retriever Library is a scalable microservice framework designed for extracting, structuring, and enriching content from documents to support downstream generative AI applications. It processes various document types by splitting them into components such as text, tables, charts, and images, and then applies OCR and contextual analysis to convert them into structured data formats. The system is built on NVIDIA NIM microservices, enabling high-performance parallel processing and efficient... -
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MathModelAgent
An Agent Designed for Mathematical Modeling
MathModelAgent is an AI agent system designed specifically for assisting with mathematical modeling tasks and academic problem solving. The platform automates the process of analyzing mathematical problems, constructing models, generating code for simulations or computations, and producing a complete research-style report. The project uses a multi-agent architecture where different specialized agents handle tasks such as problem interpretation, modeling design, programming implementation,... -
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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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Machine Learning Engineering Open Book
Machine Learning Engineering Open Book
Machine Learning Engineering Open Book is an open “living book” that captures practical methodologies, tooling advice, and operational knowledge for successfully training and deploying large language models and multimodal systems. The repository functions as a field guide compiled from real-world experience, particularly from work on large-scale models such as BLOOM-176B and IDEFICS-80B. It is heavily oriented toward practitioners who need hands-on solutions, including copy-paste commands,... -
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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,...
