gliclass-base-v2.0-rac-init

Name: gliclass-base-v2.0-rac-init
Author: knowledgator

by knowledgator

28dl/month

11likes

HuggingFace Run on your data, free

Identifier

Model ID

knowledgator/gliclass-base-v2.0-rac-init

Tags

safetensorsGLiClasstext classificationzero-shotsmall language modelsRAGsentiment analysiszero-shot-classificationenfrgedataset:MoritzLaurer/synthetic_zeroshot_mixtral_v0.1dataset:knowledgator/gliclass-v1.0dataset:fancyzhx/amazon_polaritydataset:cnmoro/QuestionClassificationdataset:Arsive/toxicity_classification_jigsawdataset:shishir-dwi/News-Article-Categorization_IABdataset:SetFit/qnlidataset:nyu-mll/multi_nlidataset:SetFit/student-question-categoriesdataset:SetFit/tweet_sentiment_extractiondataset:SetFit/hate_speech18dataset:saattrupdan/doc-nlidataset:knowledgator/gliclass-v2.0-RACarxiv:2508.07662base_model:microsoft/deberta-v3-basebase_model:finetune:microsoft/deberta-v3-baselicense:apache-2.0region:us

Use gliclass-base-v2.0-rac-init 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, gliclass-base-v2.0-rac-init 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.