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    Models/Summarization/ncls-p/Qwen2.5-7B-blog-key-points
    Summarizationcc-by-4.0

    Qwen2.5-7B-blog-key-points

    by ncls-p

    Identifier
    Model ID
    ncls-p/Qwen2.5-7B-blog-key-points

    Tags

    safetensorsggufqwen2text-generationsummarizationkey-pointsblog-summarizationunslothzhoengfraspapordeuitarusjpnkorviethaaradataset:ncls-p/blog-key-pointsbase_model:Qwen/Qwen2.5-7B-Instructbase_model:quantized:Qwen/Qwen2.5-7B-Instructlicense:cc-by-4.0endpoints_compatibleregion:usconversational

    Use Qwen2.5-7B-blog-key-points on Mixpeek

    Build multimodal processing pipelines with this model and others. Extract features, run inference, and set up retrieval in Mixpeek Studio.

    Open Studio

    How It Runs on Mixpeek

    On Mixpeek, Qwen2.5-7B-blog-key-points runs as a managed extractor inside a processing pipeline. Point a bucket of summarization data at it, and Mixpeek handles GPU provisioning, batching, retries, and writing the outputs into a vector store you can query.

    Extractor outputs land in the Mixpeek Vector Store (MVS), where you can combine them with retrieval, reranking, and filter stages to build end-to-end search and agent-perception pipelines, no model-serving infrastructure to maintain.