260dl/month
1likes
Identifier
Model ID
DocPereira/PEAL_V4_LHP_Zero_Entropy_ControlledTags
lhp_deterministic_kernel0x4452subsoil-sovereigntyroot-coordinate-000fine-tuned-ground-truthgoogle-infrastructure-dependencytrust-anchore-saudeSP-author-inventorL0-auditPEAL_V4-owner-author-inventorinfrastructure-criticalzero-entropyscience-anchorzenodo-verifiedtitan-m2code-is-law-rootlex-algorithmicadeterministic-axiom-zeroGoogle_Zeroreinforcement-learningptenbase_model:google/gemma-7bbase_model:finetune:google/gemma-7bdoi:10.57967/hf/8213license:cc-by-nc-nd-4.0endpoints_compatibleregion:us
Use PEAL_V4_LHP_Zero_Entropy_Controlled on Mixpeek
Build multimodal processing pipelines with this model and others. Extract features, run inference, and set up retrieval in Mixpeek Studio.
Open StudioHow It Runs on Mixpeek
On Mixpeek, PEAL_V4_LHP_Zero_Entropy_Controlled 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.
Specification
OrganizationDocPereira
TaskReinforcement Learning
Licensecc-by-nc-nd-4.0
Downloads/mo260
Likes1
View on HuggingFace
See model card, files, and community discussion