large concept model

We have hosted the application large concept model in order to run this application in our online workstations with Wine or directly.

Quick description about large concept model:

Large Concept Model is a research codebase centered on concept-centric representation learning at scale, aiming to capture shared structure across many categories and modalities. It organizes training around concepts (rather than just raw labels), encouraging models to understand attributes, relations, and compositional structure that transfer across tasks. The repository provides training loops, data tooling, and evaluation routines to learn and probe these concept embeddings, typically from large image�text or weakly supervised corpora. It includes utilities to build concept vocabularies, map supervision signals to those vocabularies, and measure zero-shot or few-shot generalization. Probing tools help diagnose what the model knows�e.g., attribute recognition, relation understanding, or compositionality�so you can iterate on data and objectives. The design is modular, making it straightforward to swap backbones, change objectives, or integrate retrieval components.

Features:

• Concept-centric training pipelines with modular backbones and objectives
• Data utilities to build vocabularies, map supervision, and handle weak labels
• Probing and evaluation for attributes, relations, and compositionality
• Zero-shot and few-shot evaluation harnesses for transfer studies
• Retrieval and grounding helpers to connect concepts to examples
• Configurable recipes to scale from small runs to web-scale corpora

Programming Language: Python.
Categories: