Search Results for "freetz-linux" - Page 27
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OpenLLM
Operating LLMs in production
An open platform for operating large language models (LLMs) in production. Fine-tune, serve, deploy, and monitor any LLMs with ease. With OpenLLM, you can run inference with any open-source large-language models, deploy to the cloud or on-premises, and build powerful AI apps. Built-in supports a wide range of open-source LLMs and model runtime, including Llama 2， StableLM, Falcon, Dolly, Flan-T5, ChatGLM, StarCoder, and more. Serve LLMs over RESTful API or gRPC with one command, query via... -
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MiMo-V2.5-ASR
Robust Speech Recognition Across Languages, Dialects
MiMo-V2.5-ASR is an advanced automatic speech recognition system developed as part of Xiaomi’s MiMo AI ecosystem. It is designed to handle complex acoustic environments, including noisy conditions and diverse speaker variations. The model supports multiple languages and dialects, enabling robust transcription across global use cases. It leverages modern deep learning architectures to improve accuracy and adaptability in real-world scenarios. The system is built to integrate with broader AI... -
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Claude Ads
Comprehensive paid advertising audit & optimization skill
Claude Ads is an AI-powered auditing and optimization tool designed to analyze paid advertising campaigns across multiple platforms using Claude Code. It processes user-provided data such as exports or screenshots and evaluates campaigns using hundreds of predefined checks. The system generates structured reports, identifies inefficiencies, and suggests optimization strategies based on industry benchmarks. It supports platforms like Google Ads, Meta Ads, TikTok, LinkedIn, and more, offering... -
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LLM Telegram Bot
A Telegram bot for Large Language Models
LLM Telegram Bot is a self-hosted Telegram chatbot that connects messaging interactions with large language models, typically powered by Ollama or similar backends. The project is designed to provide a customizable AI assistant that can operate within Telegram conversations, supporting dynamic responses based on user input and configurable parameters. It includes features such as conversation memory, allowing the bot to maintain context across multiple messages and provide more coherent... -
Stop Storing Third-Party Tokens in Your DatabaseRolling your own OAuth token storage can be a security liability. Token Vault securely stores access and refresh tokens from federated providers and handles exchange and renewal automatically. Connected accounts, refresh exchange, and privileged worker flows included.
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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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Watermark-Removal
Machine learning image inpainting task that removes watermarks
Watermark-Removal repository is a machine learning project focused on removing visible watermarks from digital images using deep learning and image inpainting techniques. The system analyzes an image containing a watermark and attempts to reconstruct the underlying visual content so that the watermark is removed while preserving the original appearance of the image. The project uses neural network models inspired by research in contextual attention and gated convolution, which are methods... -
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cracking-the-data-science-interview
A Collection of Cheatsheets, Books, Questions, and Portfolio
Cracking the Data Science Interview is an open educational repository that collects study materials, resources, and reference links for preparing for data science interviews. The project organizes content across many fundamental areas of data science, including statistics, probability, SQL, machine learning, and deep learning. It includes cheat sheets that summarize important technical concepts commonly discussed during technical interviews. The repository also provides links to recommended... -
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mosaicml composer
Supercharge Your Model Training
composer is a deep learning training framework built on PyTorch and designed to make large-scale model training more efficient, scalable, and customizable. At the center of the project is a highly optimized Trainer abstraction that simplifies the management of training loops, parallelization, metrics, logging, and data loading. The framework is intended for modern workloads that may span anything from a single GPU to very large distributed training environments, which makes it suitable for... -
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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,... -
Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
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SWIFT LLM
Use PEFT or Full-parameter to CPT/SFT/DPO/GRPO 600+ LLMs
SWIFT LLM is a comprehensive framework developed within the ModelScope ecosystem for training, fine-tuning, evaluating, and deploying large language models and multimodal models. The platform provides a full machine learning pipeline that supports tasks ranging from model pre-training to reinforcement learning alignment techniques. It integrates with popular inference engines such as vLLM and LMDeploy to accelerate deployment and runtime performance. The framework also includes support for... -
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MemMachine
Universal memory layer for AI Agents
MemMachine is a universal memory layer designed for AI agents that provides persistent, rich memory storage and retrieval capabilities so autonomous agent systems can recall context, personal preferences, and long-term interaction history across sessions, models, and use cases. Unlike ephemeral LLM prompt state, MemMachine supports distinct memory types—short-term conversational context, long-term persistent knowledge, and profile memory for personalized facts—persisted in optimized stores... -
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SeedVR
Repo for SeedVR2 & SeedVR
SeedVR (from the ByteDance-Seed organization) is an open-source research and implementation repository focused on cutting-edge video restoration using diffusion transformer architectures. The project includes both the original SeedVR and its successor SeedVR2 models, which are designed to restore degraded or low-quality video content by learning to reconstruct high-fidelity frames with temporal coherence. These models leverage advanced techniques such as adaptive attention mechanisms and... -
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The Hypersim Dataset
Photorealistic Synthetic Dataset for Holistic Indoor Scene
Hypersim is a large-scale, photorealistic synthetic dataset and tooling suite for indoor scene understanding research. It provides richly annotated renderings—RGB, depth, surface normals, instance and semantic segmentations, and material/lighting metadata—produced from high-fidelity virtual environments. The dataset spans diverse furniture layouts, room types, and camera trajectories, enabling robust training for geometry, segmentation, and SLAM-adjacent tasks. Rendering pipelines and... -
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Simple Evals
Lightweight framework for evaluating large language model performance
simple-evals is a lightweight evaluation framework developed by OpenAI for quickly testing models against small, focused benchmarks. It is designed to help researchers and developers run targeted evaluations without the complexity of large-scale pipelines. By emphasizing simplicity, the framework makes it easy to define new tasks, run evaluations, and interpret results in a reproducible way. It is particularly useful for sanity checks, exploratory research, and comparing performance across... -
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AWorld
Build, evaluate and train General Multi-Agent Assistance with ease
AWorld (Agent World) is an agent runtime/framework. It supports building, evaluating, and training self-improving intelligent agents and multi-agent systems (MAS). It is designed to provide infrastructure for agent orchestration, iterative learning, and environment interaction at scale. Scalable training across environments and distributed setups. Support for multi-agent collaboration/orchestration (MAS). The system is intended to help agents evolve via experience. It provides features to... -
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Arcade AI
Arcade Tool Development Kit (TDK), Worker, Evals, and CLI
Arcade AI Platform is a developer-oriented toolkit for building, deploying, and managing tools tailored to AI agents, structured as modular Python packages for flexibility and extensibility. Core platform functionality and schemas. This repository contains the core Arcade libraries, organized as separate packages for maximum flexibility and modularity. Evaluation framework for testing tool performance. Test your MCP server's tools, resources, prompts, elicitation, and OAuth 2. MCPJam is... -
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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... -
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AndroidEnv
RL research on Android devices
android_env is a reinforcement learning (RL) environment developed by Google DeepMind that enables agents to interact with Android applications directly as a learning environment. It provides a standardized API for training agents to perform tasks on Android apps, supporting tasks ranging from games to productivity apps, making it suitable for research in real-world RL settings. -
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Causal ML
Uplift modeling and causal inference with machine learning algorithms
Causal ML is a Python package that provides a suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research [1]. It provides a standard interface that allows users to estimate the Conditional Average Treatment Effect (CATE) or Individual Treatment Effect (ITE) from experimental or observational data. Essentially, it estimates the causal impact of intervention T on outcome Y for users with observed features X, without strong assumptions on... -
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OpenBB
Investment Research for Everyone, Everywhere
Customize and speed up your analysis, bring your own data, and create instant reports to gain a competitive edge. Whether it’s a CSV file, a private endpoint, an RSS feed, or even embed an SEC filing directly. Chat with financial data using large language models. Don’t waste time reading, create summaries in seconds and ask how that impacts investments. Create your dashboard with your favorite widgets. Create charts directly from raw data in seconds. Create charts directly from raw data in... -
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MLRun
Machine Learning automation and tracking
MLRun is an open MLOps framework for quickly building and managing continuous ML and generative AI applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications, significantly reducing engineering efforts, time to production, and computation resources. MLRun breaks the silos between data, ML, software, and DevOps/MLOps teams, enabling collaboration and fast continuous... -
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Scikit-LLM
Seamlessly integrate LLMs into scikit-learn
Seamlessly integrate powerful language models like ChatGPT into sci-kit-learn for enhanced text analysis tasks. At the moment the majority of the Scikit-LLM estimators are only compatible with some of the OpenAI models. Hence, a user-provided OpenAI API key is required. Additionally, Scikit-LLM will ensure that the obtained response contains a valid label. If this is not the case, a label will be selected randomly (label probabilities are proportional to label occurrences in the training... -
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bbox-visualizer
Make drawing and labeling bounding boxes easy as cake
Make drawing and labeling bounding boxes easy as cake. This package helps users draw bounding boxes around objects, without doing the clumsy math that you'd need to do for positioning the labels. It also has a few different types of visualizations you can use for labeling objects after identifying them. There are optional functions that can draw multiple bounding boxes and/or write multiple labels on the same image, but it is advisable to use the above functions in a loop in order to have... -
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TokenSpeed
TokenSpeed is a speed-of-light LLM inference engine
TokenSpeed is an LLM inference engine designed for high-performance production agent workloads. It aims to combine TensorRT-LLM-level speed with vLLM-level usability, making it relevant for teams that need fast generation without sacrificing developer ergonomics. The project is focused on the specific needs of agentic systems, where latency, throughput, and efficient scheduling matter across many short or tool-heavy requests. It builds on ideas and components from the broader open-source... -
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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,...
