4 projects for "arc" with 2 filters applied:

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  • 1
    Poetiq

    Poetiq

    Reproduction of Poetiq's record-breaking submission to the ARC-AGI-1

    poetiq-arc-agi-solver is the open-source codebase from Poetiq that replicates their record-breaking submission to the challenging benchmark suite ARC-AGI (both ARC-AGI-1 and ARC-AGI-2). The project demonstrates a system that orchestrates large language models (LLMs) — like those from major providers — with carefully engineered prompting, reasoning workflows, and dynamic strategies, to tackle the abstract, logic-heavy problems in ARC-AGI.
    Downloads: 0 This Week
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  • 2
    GLM-4.6

    GLM-4.6

    Agentic, Reasoning, and Coding (ARC) foundation models

    GLM-4.6 is the latest iteration of Zhipu AI’s foundation model, delivering significant advancements over GLM-4.5. It introduces an extended 200K token context window, enabling more sophisticated long-context reasoning and agentic workflows. The model achieves superior coding performance, excelling in benchmarks and practical coding assistants such as Claude Code, Cline, Roo Code, and Kilo Code. Its reasoning capabilities have been strengthened, including improved tool usage during inference...
    Downloads: 29 This Week
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  • 3
    Llama-3.2-1B

    Llama-3.2-1B

    Llama 3.2–1B: Multilingual, instruction-tuned model for mobile AI

    meta-llama/Llama-3.2-1B is a lightweight, instruction-tuned generative language model developed by Meta, optimized for multilingual dialogue, summarization, and retrieval tasks. With 1.23 billion parameters, it offers strong performance in constrained environments like mobile devices, without sacrificing versatility or multilingual support. It is part of the Llama 3.2 family, trained on up to 9 trillion tokens and aligned using supervised fine-tuning, preference optimization, and safety...
    Downloads: 0 This Week
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  • 4
    Llama-3.2-1B-Instruct

    Llama-3.2-1B-Instruct

    Instruction-tuned 1.2B LLM for multilingual text generation by Meta

    ...Llama-3.2-1B is lightweight enough for deployment on constrained devices like smartphones, using formats like SpinQuant and QLoRA to reduce model size and latency. Despite its small size, it performs competitively across benchmarks such as MMLU, ARC, and TLDR summarization. The model is distributed under the Llama 3.2 Community License, requiring attribution and adherence to Meta’s Acceptable Use Policy.
    Downloads: 0 This Week
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