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Velvet 2B

Developed by Almawave
Velvet-2B is a 2-billion-parameter Italian-English bilingual large language model, trained from scratch based on the Transformer architecture, focusing on text generation and comprehension tasks.
Downloads 3,784
Release Time : 2/10/2025

Model Overview

Velvet-2B is the 2-billion-parameter version in the Velvet series, optimized for Italian and English. The model adopts a dense architecture design and supports various natural language processing tasks, including text generation, classification, summarization, and question answering.

Model Features

Bilingual optimization
Specially balanced training for Italian and English, effectively reducing overfitting bias
Extended context window
Supports 4K token context window, extendable to 32K, suitable for processing long documents
Multi-task capability
Fine-tuned with instructions to perform various natural language processing tasks
Efficient inference
Adopts grouped query attention (GQA) mechanism to improve inference efficiency

Model Capabilities

Text summarization
Information extraction
Retrieval-augmented generation (RAG)
Text paraphrasing
Text entailment
Natural language inference
Common sense reasoning
Text classification
Machine translation
Question answering systems
Text completion

Use Cases

Content generation
Article writing
Generate news, blogs, and other content in Italian or English
Can produce fluent and coherent text
Product descriptions
Generate product descriptions for e-commerce platforms
Can generate professional descriptions tailored to product features
Information processing
Document summarization
Extract key information and summarize long documents
Can generate summaries that accurately reflect the main points of the original text
Question answering systems
Build knowledge-based question answering applications
Can provide accurate and relevant answers
Language services
Machine translation
Bidirectional translation between Italian and English
Can provide fluent translation results
Text rewriting
Paraphrase existing text with synonyms
Can maintain the original meaning while changing the expression
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