C

Cocom V1 4 Mistral 7b

Developed by naver
COCOM is an efficient context compression method that compresses long contexts into a small number of context embeddings, thereby accelerating the generation time for question-answering tasks.
Downloads 17
Release Time : 10/14/2024

Model Overview

COCOM is an efficient context compression method for Retrieval-Augmented Generation (RAG). It accelerates generation time by compressing long contexts into a small number of context embeddings and supports different compression rates to balance decoding time and answer quality.

Model Features

Efficient Context Compression
Compresses long contexts into a small number of context embeddings, significantly reducing decoding time.
Multi-Context Processing Support
Efficiently handles multiple contexts, making it suitable for complex question-answering scenarios.
Adjustable Compression Rate
Supports different compression rates, allowing users to balance decoding time and answer quality.

Model Capabilities

Context Compression
Question Answering Generation
Retrieval-Augmented Generation (RAG)

Use Cases

Information Retrieval & Question Answering
Movie Character Query
Quickly answers questions about actors playing roles in movies or TV shows.
Achieves up to 5.69x acceleration compared to existing methods.
Featured Recommended AI Models
AIbase
Empowering the Future, Your AI Solution Knowledge Base
© 2025AIbase