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DataBubble·

Model Detail

Kaikaku logo

epicure-core

▲ 12.9%
Provider: KaikakuCategory: llmPipeline: feature-extraction
DB Score
24.2
Downloads
881
Likes
14
Day
+12.9%
Week
+0.0%
Month
+0.0%
Overview

epicure-core is a large language model released by Kaikaku. The model is registered under the feature-extraction pipeline tag on Hugging Face, distributed under the permissive cc-by-4.0 license.

Technical

The cc-by-4.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.

Trending Signal

Downloads of epicure-core have moved +12.9% over the past 24 hours. That is a slight downtrend, consistent with normal cooling as newer models compete for the same workloads. These numbers are signal, not guarantee — week-over-week download counts on Hugging Face also reflect mirror traffic, CI scrapes, and one-off benchmarking runs.

Read about databubble_score →
Use Cases

epicure-core is best fit for general-purpose chat and instruction-following workloads, and semantic search, retrieval, and clustering pipelines. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.

Download History
Research Paper
arXiv: 2605.22391→
Model Info
Licensecc-by-4.0
Citations0 (0 influential)
Recent newsView all news →
Related News
arxivneutral14d ago

Epicure: Navigating the Emergent Geometry of Food Ingredient Embeddings

arXiv:2605.22391v1 Announce Type: cross Abstract: We present Epicure, a family of three sibling skip-gram ingredient embeddings retrained from scratch on a multilingual recipe corpus. We aggregate 4.14M recipes from 11 sources spanning seven languages, English, Chinese, Russian, Vietnamese, Spanish,

arxivneutral37d ago

Epicure: Multidimensional Flavor Structure in Food Ingredient Embeddings

arXiv:2604.22776v1 Announce Type: cross Abstract: A chef's intuition about flavor, texture, and cultural identity represents tacit knowledge that is difficult to articulate yet central to culinary practice. We show that this knowledge is already encoded in FlavorGraph's 300-dimensional ingredient em

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