Showing 4 open source projects for "iot projects coding"

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  • 1
    E2B Cookbook

    E2B Cookbook

    Examples of using E2B

    E2B Cookbook is an open-source collection of example projects, guides, and reference implementations demonstrating how to build applications using the E2B platform. The repository acts as a practical learning resource for developers who want to integrate AI agents with secure cloud execution environments that allow large language models to run code and interact with tools. The examples illustrate how developers can build AI workflows capable of performing tasks such as data analysis, code...
    Downloads: 0 This Week
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  • 2
    AI-Codereview-Gitlab

    AI-Codereview-Gitlab

    GitLab automatic code review tool based on large models

    ...In addition to code analysis, the tool can produce daily development summaries and notifications that help teams track progress and review activity across projects.
    Downloads: 1 This Week
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  • 3
    Qwen2.5-Coder

    Qwen2.5-Coder

    Qwen2.5-Coder is the code version of Qwen2.5, the large language model

    Qwen2.5-Coder, developed by QwenLM, is an advanced open-source code generation model designed for developers seeking powerful and diverse coding capabilities. It includes multiple model sizes—ranging from 0.5B to 32B parameters—providing solutions for a wide array of coding needs. The model supports over 92 programming languages and offers exceptional performance in generating code, debugging, and mathematical problem-solving. Qwen2.5-Coder, with its long context length of 128K tokens, is...
    Downloads: 18 This Week
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  • 4
    InternLM

    InternLM

    Official release of InternLM series

    ...The repository highlights multiple model sizes intended to serve different needs, from efficient research and prototyping to more capable deployments for complex scenarios. Beyond model weights, the project emphasizes an ecosystem view, pointing developers to compatible tools and projects across training and inference so teams can build end-to-end workflows. InternLM’s direction includes strong general-purpose capabilities and ongoing iterations that target improved reasoning, coding, and tool-use behaviors. The broader InternLM ecosystem also includes training tooling and guidance aimed at making fine-tuning and adaptation more accessible across hardware setups, including smaller single-GPU environments and larger multi-node configurations.
    Downloads: 0 This Week
    Last Update:
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