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    Models/Visual Question Answering/m7mdal7aj/fine_tuned_llama_2_7b_chat_OKVQA
    Visual Question Answeringtransformersapache-2.0

    fine_tuned_llama_2_7b_chat_OKVQA

    by m7mdal7aj

    Identifier
    Model ID
    m7mdal7aj/fine_tuned_llama_2_7b_chat_OKVQA

    Deploy fine_tuned_llama_2_7b_chat_OKVQA

    Single-tenant

    Mixpeek has no managed extractor for this model. On a single-tenant deployment you upload the weights and a custom plugin serves them next to the rest of your pipeline.

    Tags

    transformerssafetensorsllamatext-generationmultimodalKBVQAVQAFinetuningvisual-question-answeringendataset:m7mdal7aj/OK-VQAbase_model:meta-llama/Llama-2-7b-chat-hfbase_model:finetune:meta-llama/Llama-2-7b-chat-hflicense:apache-2.0text-generation-inferenceendpoints_compatibleregion:us

    Use fine_tuned_llama_2_7b_chat_OKVQA 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, fine_tuned_llama_2_7b_chat_OKVQA runs as a managed extractor inside a processing pipeline. Point a bucket of visual question answering 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.