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    Models/Zero Shot Classification/jeff-legacy/Jeff-Gemma4-E2B
    Zero Shot Classificationtransformersapache-2.0

    Jeff-Gemma4-E2B

    by jeff-legacy

    Identifier
    Model ID
    jeff-legacy/Jeff-Gemma4-E2B

    Deploy Jeff-Gemma4-E2B

    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

    transformerssafetensorsgemma4_textfeature-extractiondecision-modelzero-shot-classificationcalibrationsystem-1localenbase_model:google/gemma-4-E2B-itbase_model:finetune:google/gemma-4-E2B-itlicense:apache-2.0eval-resultsendpoints_compatibleregion:us

    Use Jeff-Gemma4-E2B 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, Jeff-Gemma4-E2B runs as a managed extractor inside a processing pipeline. Point a bucket of zero shot classification 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.