Результаты по «local models» — 264

A TypeScript library for building apps with LLMs and other ML models (speech-to-text, text-to-speech, image generation).

Implementations of Machine Learning models from scratch in Python with a focus on transparency. Aims to showcase the nuts and bolts of ML in an accessible way.

ModelFox is a platform for managing and deploying machine learning models.

🐍 🏠 - Create, read, validate, and save Stella system dynamics models (.stmx files in XMILE format) for scientific simulation and modeling.

Code and documentation to train Stanford's Alpaca models, and generate the data.

PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more.

A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glued together seamlessly with objective to build predictive models with one line of code.

A comprehensive testing and evaluation framework for voice agents across language models, prompts, and agent personas. !GitHub Repo stars

This paper list focuses on the theoretical or empirical analysis of language models, e.g., the learning dynamics, expressive capacity, interpretability, generalization, and other interesting topics.

📇 🏠 🍎 🪟 🐧 - Token usage tracker for OpenAI and Claude APIs with MCP support, real-time session tracking, and accurate pricing for 2025 models

Applying Large language models (LLMs) for diverse optimization tasks (Opt) is an emerging research area. This is a collection of references and papers of LLM4Opt.

The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models !GitHub Repo stars

Open-source LLM evaluation and red teaming framework. Test prompts, models, agents, and RAG pipelines. Run adversarial attacks (jailbreaks, prompt injection) and integrate security testing into CI/CD.

🕵️‍♂️ Library designed for developers eager to explore the potential of Large Language Models (LLMs) and other generative AI through a clean, effective, and Go-idiomatic approach !GitHub Repo stars

📇 ☁️ 🍎 🪟 🐧 - CLI and MCP server for AI video and image generation with unified access to 7 models (Kling, Veo, Sora, Seedance, Flux, Grok Imagine). Provides 9 tools for generating, managing, and browsing media.

📇 🏠 - Translation quality evaluation using xCOMET models. Provides quality scoring (0-1), error detection with severity levels (minor/major/critical), and optimized batch processing with 25x speedup.

A central community library by Hugging Face for AI in robotics — end-to-end learning tools, data pipelines, and support for training/deploying VLA models.

Kedro is a data and development workflow framework that implements best practices for data pipelines with an eye towards productionizing machine learning models.

Interact with LLM using Ollama models(or openAI, mistralAI)via pure shell scripts on your Linux(or MacOS) system, enhancing intelligent system management without any dependencies.

.NET Standard bindings for Apache MxNet with Imperative, Symbolic and Gluon Interface for developing, training and deploying Machine Learning models in C#. https://mxnet.tech-quantum.com/

🐍 - This Kaggle MCP Server makes Kaggle more accessible by letting you browse competitions, leaderboards, models, datasets, and kernels directly within MCP, streamlining discovery for data scientists and developers.

Python library for time series forecasting using machine learning models. It works with any regressor compatible with the scikit-learn API, including popular options like LightGBM, XGBoost, CatBoost, Keras, and many others.

🏠 – A Model Context Protocol (MCP) server implementation that connects Large Language Models (LLMs) to GIS operations using GIS libraries, enabling AI assistants to perform accurate geospatial operations and transformations.

An ONNX runtime written in pure C (99) with zero dependencies focused on small embedded devices. Run inference on your machine learning models no matter which framework you train it with. Easy to install and compiles everywhere, even in very old devices.

FastEdit aims to assist developers with injecting fresh and customized knowledge into large language models efficiently using one single command.

🐍 🏠 - Modern, secure MCP server for accessing ZIM format knowledge bases offline. Enables AI models to search and navigate Wikipedia, educational content, and other compressed knowledge archives with smart retrieval, caching, and comprehensive API.

Gateway streamlines requests to 100+ open & closed source models with a unified API. It is also production-ready with support for caching, fallbacks, retries, timeouts, loadbalancing, and can be edge-deployed for minimum latency.

📇 ☁️ 🏠 - AI-powered architectural analysis server for software projects. Provides technology stack detection, ADR management, security checks, enhanced TDD workflow, and deployment readiness validation with support for multiple AI models.

An efficient VLA model leveraging State Space Models (Mamba) instead of standard self-attention, offering linear inference complexity for efficient, recurrent robotic reasoning.

Neural network 3D visualization framework, build interactive and intuitive model in browsers, support pre-trained deep learning models from TensorFlow, Keras, TensorFlow.js.

> Python Outlier Detection, comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. Featured for Advanced models, including Neural Networks/Deep Learning and Outlier Ensembles.

Validation & testing of machine learning models and data during model development, deployment, and production. This includes checks and suites related to various types of issues, such as model performance, data integrity, distribution mismatches, and more.

A library for doing continuous integration with ML projects. Use GitHub Actions & GitLab CI to train and evaluate models in production like environments and automatically generate visual reports with metrics and graphs in pull/merge requests. Framework & language agnostic.

A lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for Python covering cutting-edge models such as VGG-Face, FaceNet, OpenFace, DeepFace, DeepID, Dlib and ArcFace.

Evaluate, monitor, analyze, and improve machine learning and generative models from pre-production to production. Ship more ML and LLMs into production, and monitor ML and LLM metrics like hallucination, PII, and toxicity.

An optimized prompt tuning strategy achieving comparable performance to fine-tuning on small/medium-sized models and sequence tagging challenges. (ACL 2022)