Velvet-2B-1.5
October 14, 2025
A small, highly optimized model designed to deliver strong performance even in edge deployments.
The essence of Velvet-2B-1.5
The updated version of the smallest model in the family is designed to adapt effectively to specific tasks and a wide range of use cases. Its compact size and high optimization deliver reliable performance even in resource-constrained or edge deployment environments.
Languages
Architecture
Auto-regressive language model with a transformer-based causal decoder-only design, built on 28 transformer layers.
Training Dataset
3 trillion tokens.
Context Window
32K Tokens.
Parameters
2 Billion parameters.
Vocabulary
127K
Tokens.
Safety
Over 50K human generated examples for safety instructions.
License
Open weight with Apache 2.0 license.
Specialization
1M synthetic examples for SFT.
Data Freshness
Data cutoff date: June 2025.
Capabilities
Question
Answering
Machine
Translation
Information Extraction
Advanced Reasoning
Text
Classification
Textual
Entailment
Text
Completion
Natural Language
Interface
RAG
Summarization
Paraphrasing
Performance Evaluation
An Independent Evaluation Board compared Velvet with other models under 30B parameters built from scratch, using several metrics to assess the model’s logical reasoning, problem-solving capabilities, and ability to go beyond statistical correlations.
Italian language
| Category | Benchmark | Velvet-2B |
|---|---|---|
| General | MMLU (5-shot) | 39.6 |
| Commonsense | Hellaswag (0-shot) | 54.3 |
| WinoGrande ITA-bench (0-shot) | 61.9 | |
| PIQA ITA-bench (0-shot) | 67.3 | |
| SciQ ITA-bench (0-shot) with p. | 86.6 | |
| Reasoning | ARC-Challenge (0-shot) | 41.7 |
English language
| Category | Benchmark | Velvet-2B |
|---|---|---|
| General | MMLU (5-shot) | 43.4 |
| Instruction Following | IFEval (0-shot) | 53.2 |
| Commonsense | Hellaswag (10-shot) | 65.0 |
| WinoGrande (0-shot) | 60.9 | |
| Reasoning | ARC-Challenge (25-shot) | 50.6 |
These metrics evaluate its scientific reasoning, capacity to generate plausible, contextually relevant responses based on common sense, and overall understanding across multiple subjects, focusing on providing accurate and informed answers.
Why Velvet-2B-1.5
Velvet-2B-1.5 is designed for efficient fine-tuning on specialized tasks, making it a flexible solution across different use cases. Not all challenges demand the same approach; some scenarios require speed and scalability, while others face constraints related to cost or hardware capacity.
High Volumes
Velvet-2B provides a responsive and agile solution for organizations handling vast amounts of data in (near) real time, keeping an optimal balance between speed and performances.
Tighter Scope
Velvet-2B delivers efficient, cost-effective results tailored to specific needs of smaller organizations with limited computational resources.
Narrow Tasks
Due to its ability to be easily fine-tuned with minimal hardware, Velvet-2B is ideal for highly specific tasks with well-defined objectives.