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  • 1
    Transformer Explainer

    Transformer Explainer

    Learn How LLM Transformer Models Work with Interactive Visualization

    ...Through visual diagrams and interactive interfaces, the tool reveals how tokens are processed through layers such as embeddings, attention mechanisms, and feed-forward networks. Users can observe how attention weights change as the model predicts the next token, offering insight into how transformer architectures capture relationships between words. The design of the platform emphasizes educational accessibility, allowing students, researchers, and developers to explore complex machine learning concepts without requiring specialized hardware or installations.
    Downloads: 2 This Week
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  • 2
    gpu_poor

    gpu_poor

    Calculate token/s & GPU memory requirement for any LLM

    gpu_poor is an open-source tool designed to help developers determine whether their hardware is capable of running a specific large language model and to estimate the performance they can expect from it. The project focuses on calculating GPU memory requirements and predicted inference speed for different models, hardware configurations, and quantization strategies. By analyzing factors such as model size, context length, batch size, and GPU specifications, the system estimates how much VRAM...
    Downloads: 0 This Week
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