Arc53/arc-decide-0.4b-v0.2

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AI 工具 A compact 0.4B-parameter text-classification model from Arc53, built on Qwen3 and published on Hugging Face.

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The model's main draw is its small footprint: at roughly 0.4B parameters it can run on CPU and be served through text-embeddings-inference, which suits latency- or cost-sensitive pipelines better than large rerankers. Its tag list points to a broad decision-making role across intent classification,

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This page is a Hugging Face model repository for arc-decide-0.4b-v0.2, published under the Arc53 organization. Rather than a standalone product, it is a downloadable checkpoint with a model card, file listings, and deployment instructions. The repository carries a MIT license and is tagged for text classification within the Transformers ecosystem, with ONNX and Safetensors weights available.

The model is built on Qwen3 and sized at about 0.4 billion parameters, which places it in the lightweight range. Its tags reference text generation, reranking, prompt-injection and hallucination detection, intent classification, conversation analytics, RAG, calibration, and CPU inference via text-embeddings-inference. That combination suggests a general-purpose decision or scoring model rather than a single narrow classifier.

It is aimed at developers and ML engineers building retrieval-augmented generation systems, chatbots, or moderation and analytics pipelines who want a small model they can host themselves. Because it can run on CPU, it may appeal to teams without dedicated GPU capacity or those prototyping before committing to larger models.

Usage follows standard Hugging Face patterns: load it with a Transformers text-classification pipeline or AutoTokenizer and AutoModel classes, or deploy through supported inference providers and local apps. Before adopting it, review the model card and files, check the label configuration, and evaluate it against your own task data, since the