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    Models/Reinforcement Learning/mradermacher/Vero-Qwen35-9B-i1-GGUF
    Reinforcement Learningtransformersapache-2.0

    Vero-Qwen35-9B-i1-GGUF

    by mradermacher

    Identifier
    Model ID
    mradermacher/Vero-Qwen35-9B-i1-GGUF

    Tags

    transformersggufverovision-language-modelmultimodalvisual-reasoningreinforcement-learningenbase_model:zlab-princeton/Vero-Qwen35-9Bbase_model:quantized:zlab-princeton/Vero-Qwen35-9Blicense:apache-2.0endpoints_compatibleregion:usimatrixconversational

    Use Vero-Qwen35-9B-i1-GGUF 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, Vero-Qwen35-9B-i1-GGUF runs as a managed extractor inside a processing pipeline. Point a bucket of reinforcement learning 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.