Showing 7 open source projects for "run sql command line"

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    LLM CLI

    LLM CLI

    Access large language models from the command-line

    A CLI utility and Python library for interacting with Large Language Models, both via remote APIs and models that can be installed and run on your own machine.
    Downloads: 1 This Week
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  • 2
    local-llm

    local-llm

    Run LLMs locally on Cloud Workstations

    local-llm is a development framework that enables developers to run large language models locally within Google Cloud Workstations or standard environments without requiring GPU hardware. It focuses on making generative AI development more accessible by leveraging quantized models and CPU-based execution, eliminating the dependency on expensive GPU infrastructure. The repository includes tools, Docker configurations, and command-line utilities that simplify the process of downloading, running, and interacting with language models directly on local or cloud-based workstations. ...
    Downloads: 3 This Week
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  • 3
    Claude Code Bridge

    Claude Code Bridge

    Real-time multi-AI collaboration: Claude, Codex & Gemini

    Claude Code Bridge is an open-source command-line tool designed to enable real-time collaboration between multiple AI coding assistants within a unified development environment. The system allows developers to coordinate interactions between models such as Claude, Codex, and Gemini so that they can work together on programming tasks. By maintaining persistent shared context between these models, the tool reduces redundant prompts and minimizes token usage while allowing each AI system to contribute specialized capabilities. ...
    Downloads: 0 This Week
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  • 4
    self-llm

    self-llm

    Tutorial tailored for Chinese babies on rapid fine-tuning

    self-llm is an open source educational project created by the Datawhale community that serves as a practical guide for deploying, fine-tuning, and using open-source large language models on Linux systems. The repository focuses on helping beginners and developers understand how to run and customize modern LLMs locally rather than relying solely on hosted APIs. It provides step-by-step tutorials covering environment setup, model deployment, inference workflows, and efficient fine-tuning techniques such as LoRA and parameter-efficient training. The project also includes guides for integrating models into real applications, including command-line interfaces, web demos, and frameworks like LangChain. ...
    Downloads: 0 This Week
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  • 5
    llm.c

    llm.c

    LLM training in simple, raw C/CUDA

    llm.c is a minimalist, systems-level implementation of a small transformer-based language model in C that prioritizes clarity and educational value. By stripping away heavy frameworks, it exposes the core math and memory flows of embeddings, attention, and feed-forward layers. The code illustrates how to wire forward passes, losses, and simple training or inference loops with direct control over arrays and buffers. Its compact design makes it easy to trace execution, profile hotspots, and...
    Downloads: 0 This Week
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  • 6
    Xorbits Inference

    Xorbits Inference

    Replace OpenAI GPT with another LLM in your app

    Replace OpenAI GPT with another LLM in your app by changing a single line of code. Xinference gives you the freedom to use any LLM you need. With Xinference, you're empowered to run inference with any open-source language models, speech recognition models, and multimodal models, whether in the cloud, on-premises, or even on your laptop. Xorbits Inference(Xinference) is a powerful and versatile library designed to serve language, speech recognition, and multimodal models. ...
    Downloads: 0 This Week
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  • 7
    Claude Code Tools

    Claude Code Tools

    Practical productivity tools for Claude Code, Codex-CLI

    Claude Code Tools is an open-source collection of command-line utilities and productivity plugins designed to enhance developer workflows when using AI coding agents such as Claude Code and Codex-CLI. The project focuses on solving common problems encountered in AI-assisted development environments, including managing session history, automating terminal interactions, and maintaining context across multiple coding sessions.
    Downloads: 1 This Week
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