Showing 4 open source projects for "design experiments"

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  • 1
    SwanLab

    SwanLab

    An open-source, modern-design AI training tracking and visualization

    SwanLab is an open-source experiment tracking and visualization platform designed to help machine learning engineers monitor, compare, and analyze the training of artificial intelligence models. The tool records training metrics, hyperparameters, model outputs, and experiment configurations so that developers can easily understand how different experiments perform over time. It provides a modern user interface for visualizing results, enabling teams to compare runs, track model performance...
    Downloads: 2 This Week
    Last Update:
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  • 2
    esp32-ai

    esp32-ai

    Running a 28.9M parameter LLM on an $8 microcontroller

    ...The repository includes firmware, wiring and flashing instructions, training code, quantization experiments, ablations, and measured results.
    Downloads: 4 This Week
    Last Update:
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  • 3
    llm.c

    llm.c

    LLM training in simple, raw C/CUDA

    ...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 understand the cost of each operation. Portability is a goal: it aims to compile with common toolchains and run on modest hardware for small experiments. Rather than delivering a production-grade stack, it serves as a reference and learning scaffold for people who want to “see the metal” behind LLMs.
    Downloads: 0 This Week
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  • 4
    Skywork-R1V4

    Skywork-R1V4

    Skywork-R1V is an advanced multimodal AI model series

    Skywork-R1V is an open-source multimodal reasoning model designed to extend the capabilities of large language models into vision-language tasks that require complex logical reasoning. The project introduces a model architecture that transfers the reasoning abilities of advanced text-based models into visual domains so the system can interpret images and perform multi-step reasoning about them. Instead of retraining both language and vision models from scratch, the framework uses a...
    Downloads: 0 This Week
    Last Update:
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