Showing 4 open source projects for "compact"

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  • 1
    FLUX.2-klein-4B

    FLUX.2-klein-4B

    Flux 2 image generation model pure C inference

    FLUX.2-klein-4B is a compact, high-performance C library implementation of the Flux optimization algorithm — an iterative approach for solving large-scale optimization problems common in scientific computing, machine learning, and numerical simulation. Written with a strong emphasis on simplicity, correctness, and performance, it abstracts the core logic of flux-based optimization into a minimal C API that can be embedded in broader applications without pulling in heavy dependencies. ...
    Downloads: 9 This Week
    Last Update:
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  • 2
    zclaw

    zclaw

    Your personal AI assistant at all-in 888KiB

    zclaw is a highly compact personal AI assistant framework designed to run on constrained embedded hardware such as the ESP32. The project focuses on delivering core assistant capabilities within an extremely small footprint, demonstrating how AI-driven automation can operate on microcontrollers. It includes support for GPIO control, scheduled tasks, memory handling, and other embedded automation features that enable real-world device interaction.
    Downloads: 0 This Week
    Last Update:
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  • 3
    llama2.c

    llama2.c

    Inference Llama 2 in one file of pure C

    ...While it can technically load Meta’s official Llama 2 models, current support is limited to fp32 precision, meaning practical use is capped at models up to around 7B parameters. The goal of llama2.c is to demonstrate how a compact and transparent implementation can perform meaningful inference even with small models, emphasizing simplicity, clarity, and accessibility. The project builds upon lessons from nanoGPT and takes inspiration from llama.cpp, focusing instead on minimalism and educational value over large-scale performance.
    Downloads: 2 This Week
    Last Update:
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  • 4
    Llama 2 Everywhere (L2E)

    Llama 2 Everywhere (L2E)

    Llama 2 Everywhere (L2E)

    Llama 2 Everywhere (L2E) is an open-source implementation of the LLaMA-2 large language model architecture designed to demonstrate how transformer-based language models can be executed with extremely minimal code. The project focuses on simplicity and educational clarity by implementing inference for LLaMA-style models in a compact C program rather than relying on large machine learning frameworks. Developers can train models using a Python training pipeline and then run inference using a lightweight C implementation that requires very few dependencies. The architecture mirrors the structure of the LLaMA-2 model family, allowing compatible model checkpoints to be converted and executed within the simplified runtime environment. ...
    Downloads: 0 This Week
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