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17 repositorios

Awesome GitHub RepositoriesModel Configuration Settings

Documentation on adjusting model parameters like temperature and top-p for specific outcomes.

Distinguishing note: Focuses on operational settings rather than prompt text.

Explore 17 awesome GitHub repositories matching artificial intelligence & ml · Model Configuration Settings. Refine with filters or upvote what's useful.

Awesome Model Configuration Settings GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • dair-ai/prompt-engineering-guideAvatar de dair-ai

    dair-ai/Prompt-Engineering-Guide

    75,678Ver en GitHub↗

    This project is a comprehensive educational resource and technical guide focused on the development, optimization, and application of large language models. It provides a structured curriculum for mastering prompt engineering, ranging from foundational principles of instruction design to advanced techniques for improving model reasoning, accuracy, and reliability. The guide distinguishes itself by offering deep technical insights into agentic workflows and autonomous system design. It covers the implementation of multi-step reasoning chains, tool integration through function calling, and stat

    Explains how to adjust model settings to influence output behavior.

    MDXagentagentsai-agents
    Ver en GitHub↗75,678
  • vercel/aiAvatar de vercel

    vercel/ai

    21,885Ver en GitHub↗

    This project is a comprehensive framework for building AI-powered applications, providing a unified toolkit for orchestrating language models, autonomous agents, and interactive user interfaces. It serves as a central library for managing the entire lifecycle of AI interactions, from initial prompt generation and model provider abstraction to complex, multi-step reasoning and tool execution. The framework distinguishes itself through its deep integration with frontend development, specifically by enabling generative user interfaces that render dynamic components directly from model outputs. I

    Allows granular control over model behavior by namespacing custom parameters for specific providers.

    TypeScriptanthropicartificial-intelligencegemini
    Ver en GitHub↗21,885
  • gaizhenbiao/chuanhuchatgptAvatar de GaiZhenbiao

    GaiZhenbiao/ChuanhuChatGPT

    15,311Ver en GitHub↗

    This project is a web-based user interface and multi-model API gateway for interacting with various large language model providers and local inference services. It functions as a retrieval-augmented generation chatbot for private document questioning, a manager for model fine-tuning, and an autonomous agent framework. The system distinguishes itself by integrating an autonomous assistant mode that uses web search and external tools to solve complex, multi-step tasks without manual prompting. It also features an API gateway capable of rotating multiple authentication keys to balance usage and

    Provides a user interface to modify model inference parameters such as temperature, top-p, and context window.

    Python
    Ver en GitHub↗15,311
  • pipecat-ai/pipecatAvatar de pipecat-ai

    pipecat-ai/pipecat

    12,846Ver en GitHub↗

    Pipecat is a framework and software development kit for building real-time multimodal AI agents and speech-to-speech systems. It utilizes a frame-based data pipeline to route audio, video, and text through a modular sequence of processors, enabling the orchestration of low-latency conversational AI. The project is distinguished by its ability to coordinate complex multimodal services, including speech-to-text, language models, and text-to-speech, within a single pipeline. It features semantic voice activity detection for natural turn-taking, state-machine conversation flows for dialogue manag

    Provides the ability to adjust model parameters like temperature and token limits during active conversations.

    Pythonaichatbot-frameworkchatbots
    Ver en GitHub↗12,846
  • antimatter15/alpaca.cppAvatar de antimatter15

    antimatter15/alpaca.cpp

    10,138Ver en GitHub↗

    alpaca.cpp is a high-performance local inference engine implemented in C++ for executing instruction-tuned large language models. It serves as a quantized model runtime designed to load and run model tensors on local hardware with minimal dependencies, removing the requirement for a full Python environment. The project focuses on on-device text generation and the deployment of private AI chatbots. It utilizes model weight quantization to reduce memory requirements and increase inference speed on consumer-grade devices. The system covers hardware-optimized model execution through thread-pool

    Provides controls for operational settings like temperature and thread count to manage token prediction.

    C
    Ver en GitHub↗10,138
  • sigoden/aichatAvatar de sigoden

    sigoden/aichat

    9,328Ver en GitHub↗

    This project is a terminal-based command line interface client and agent orchestrator for interacting with multiple large language model providers. It functions as an OpenAI API client and a local API gateway that exposes chat completions and embeddings through an HTTP server. The system distinguishes itself by providing a retrieval-augmented generation tool for indexing local files and URLs into a vector database to provide custom document context. It allows for the creation of specialized AI agents that combine custom system prompts with tool calling and external function execution. The to

    Adjusts model parameters such as temperature and token limits during an active session.

    Rustaiai-agentschatbot
    Ver en GitHub↗9,328
  • automl/auto-sklearnAvatar de automl

    automl/auto-sklearn

    8,111Ver en GitHub↗

    This is a scikit-learn automated machine learning framework designed to optimize model selection and hyperparameters. It functions as an automated model selector and hyperparameter optimization tool for classification and regression tasks, utilizing an automated ensemble builder to combine high-performing models for increased predictive accuracy. The system features a distributed search engine that uses Dask for parallel machine learning optimization across CPU cores or clusters. It implements a budget-based evaluation strategy through successive halving to prioritize promising model configur

    Creates execution commands to run automated machine learning across multiple datasets with specific time budgets.

    Python
    Ver en GitHub↗8,111
  • open-mmlab/mmagicAvatar de open-mmlab

    open-mmlab/mmagic

    7,434Ver en GitHub↗

    mmagic is a multimodal training pipeline and framework for generative AI, focusing on visual synthesis and restoration. It provides the infrastructure to build and train models for tasks such as text-to-image and text-to-video generation, 3D-aware content synthesis, and high-fidelity image translation using diffusion models and generative adversarial networks. The project distinguishes itself through specialized capabilities for generative model personalization, including techniques for fine-tuning subjects and styles. It also supports advanced visual manipulations such as latent space interp

    Provides capabilities to print and verify the resolved set of parameters used during training and testing.

    Jupyter Notebookaigccomputer-visiondeep-learning
    Ver en GitHub↗7,434
  • refly-ai/reflyAvatar de refly-ai

    refly-ai/refly

    7,393Ver en GitHub↗

    Refly is an open-source platform for building, running, and sharing deterministic agent skills. It provides a visual workflow compiler that converts natural language descriptions into executable, versioned agent workflows, and includes a runtime that deploys these compiled skills as APIs, webhooks, Slack bots, or native tools for AI coding platforms like Claude Code and Cursor. The platform distinguishes itself through a central skill registry with versioning and audit logging, enabling teams to manage agent capabilities as governed corporate assets. It supports human-in-the-loop automation,

    Sets a preferred AI model to be used automatically for new conversations and workflows.

    TypeScriptagentagent-skillsautomation
    Ver en GitHub↗7,393
  • nagi-ovo/gemini-voyagerAvatar de Nagi-ovo

    Nagi-ovo/gemini-voyager

    7,038Ver en GitHub↗

    Gemini Voyager is a browser-based toolkit designed to enhance the interface and workflow of large language model web applications. It serves as a conversation manager, an output renderer, and a prompt library manager, allowing users to customize the layout and functionality of AI chat interfaces. The project distinguishes itself through advanced content handling, such as removing image watermarks by reversing alpha blending to restore original pixels. It also provides specialized rendering for LaTeX mathematical formulas and Mermaid diagrams, alongside tools to fix broken Markdown formatting

    Allows users to set a preferred AI model that is automatically selected whenever a new conversation begins.

    TypeScriptai-studiobunchat-management
    Ver en GitHub↗7,038
  • moonshotai/kimi-cliAvatar de MoonshotAI

    MoonshotAI/kimi-cli

    6,503Ver en GitHub↗

    Kimi is a terminal-based AI agent that autonomously plans and executes software development tasks by reading, editing, and running code. It operates as an intelligent command-line agent that breaks down high-level goals into sequences of shell commands and code edits, carrying them out without manual step-by-step guidance. The agent can run in an interactive loop, switch to a shell mode for direct terminal command execution, and operate in non-interactive or one-shot modes suitable for scripting. The project distinguishes itself through multiple integration and execution modes. It can run as

    Overrides the default language model used for generating responses in a session.

    Python
    Ver en GitHub↗6,503
  • olimorris/codecompanion.nvimAvatar de olimorris

    olimorris/codecompanion.nvim

    6,166Ver en GitHub↗

    CodeCompanion is a Neovim plugin that brings large language model capabilities directly into the editor, enabling turn-based conversations with AI models in a dedicated chat buffer. It provides a comprehensive interface for interacting with LLMs, supporting multiple providers through a flexible adapter system that can route requests to various hosted or local language model services. The plugin distinguishes itself through its extensive context-sharing capabilities, allowing users to send buffer contents, visual selections, git diffs, LSP diagnostics, terminal output, quickfix lists, and view

    Edits a YAML block at the top of the chat buffer to adjust adapter hyperparameters before the next LLM turn.

    Luaacpagent-client-protocolanthropic
    Ver en GitHub↗6,166
  • anc95/chatgpt-codereviewAvatar de anc95

    anc95/ChatGPT-CodeReview

    4,445Ver en GitHub↗

    ChatGPT-CodeReview es una herramienta de análisis de código impulsada por IA y un bot que utiliza un modelo de lenguaje grande para revisar automáticamente los diffs de pull requests y publicar comentarios sobre los cambios en el código. Funciona como un sistema para detectar errores y sugerir mejoras en el código fuente. La herramienta proporciona un runtime contenedorizado para el despliegue como proceso en segundo plano o mediante una GitHub Action. Los usuarios pueden personalizar el comportamiento del análisis, el estilo y la profundidad técnica ajustando los parámetros del modelo y los prompts del sistema. El sistema maneja flujos de trabajo de revisión de código automatizados activando el análisis mediante webhooks y tuberías de CI, actualizando los comentarios cuando se envían nuevos commits. Incluye capacidades de filtrado para excluir archivos o directorios específicos utilizando expresiones regulares. El proyecto puede desplegarse en infraestructura autohospedada utilizando Docker.

    Allows adjustment of model parameters such as temperature and selection to tailor the review style.

    JavaScript
    Ver en GitHub↗4,445
  • crmne/ruby_llmAvatar de crmne

    crmne/ruby_llm

    3,566Ver en GitHub↗

    ruby_llm is an LLM integration framework and AI agent orchestrator designed to connect applications to multiple large language model providers through a unified interface. It serves as a toolkit for building autonomous assistants with custom personas, managing structured output via JSON schemas, and implementing vector embedding engines for semantic search. The project distinguishes itself as an observability suite and multimodal toolkit. It provides specialized capabilities for tracking token usage, calculating model costs, and tracing workflows via OpenTelemetry, while supporting the proces

    Allows defining global default models for chat, embeddings, and image generation to simplify API calls.

    Rubyaianthropicchatgpt
    Ver en GitHub↗3,566
  • karthink/gptelAvatar de karthink

    karthink/gptel

    3,192Ver en GitHub↗

    gptel is an LLM Emacs client and multi-backend AI integration system that allows users to interact with large language models directly inside the Emacs text editor. It serves as an AI-powered text refactoring tool and a context-aware prompt manager, providing a unified interface to connect with various AI providers, including local Ollama instances, AWS Bedrock, and Gemini. The project distinguishes itself as a Model Context Protocol client, connecting to MCP servers to provide language models with external tools and data sources. It enables context-augmented prompting by aggregating text fro

    Saves and restores model configurations and temperature settings directly within document properties.

    Emacs Lispchatgptemacsllms
    Ver en GitHub↗3,192
  • vladmandic/humanAvatar de vladmandic

    vladmandic/human

    2,999Ver en GitHub↗

    Human is a TensorFlow.js computer vision library used for face, body, and hand tracking within the browser or Node.js. It provides a framework for human pose and gesture tracking, facial recognition, and biometric liveness detection to verify a live human presence. The project distinguishes itself through a full suite of identity and motion tools, including a facial recognition framework that generates embeddings for similarity matching and a background segmenter for separating humans from their environment. It incorporates a liveness detector to prevent spoofing during facial analysis. The

    Configures processing backends and operational modes to control how analysis and logging are performed.

    HTMLage-estimationbody-segmentationbody-tracking
    Ver en GitHub↗2,999
  • rohitg00/awesome-ai-appsAvatar de rohitg00

    rohitg00/awesome-ai-apps

    723Ver en GitHub↗

    This project serves as a curated directory and resource hub for developers working with generative artificial intelligence. It provides a comprehensive index of open-source software solutions, frameworks, and project examples designed to help users discover and implement advanced AI systems. The repository focuses on practical implementations of agentic, multimodal, and retrieval-augmented generation architectures. It highlights tools for building conversational assistants, voice-enabled agents, and automated workflows that leverage large language models. By showcasing diverse technical domai

    Allows adjustment of model parameters like temperature and top-p for specific outcomes.

    HTMLagentsaiapps
    Ver en GitHub↗723
  1. Home
  2. Artificial Intelligence & ML
  3. Model Configuration Settings

Explorar subetiquetas

  • Buffer-Local Model SettingsPersisting AI model parameters within specific document properties. **Distinct from Model Configuration Settings:** Distinct from general Model Configuration Settings: focuses on the persistence of these settings within the editor buffer properties.
  • Default Model SelectionCapabilities for setting a preferred AI model to be used automatically for new sessions. **Distinct from Model Configuration Settings:** Focuses on the selection of a default model for new chats, whereas Model Configuration Settings focus on parameter tuning like temperature.
  • Execution Command GenerationGeneration of commands to run automated ML searches across multiple datasets. **Distinct from Model Configuration Settings:** Generates execution commands for batch runs rather than adjusting operational settings for a single model.
  • Experimental Parameter VerificationTools for inspecting and logging resolved model parameters to verify experimental training and testing settings. **Distinct from Model Configuration Settings:** Focuses on the verification and printing of active parameters for debugging, rather than the act of adjusting settings.