T5-Efficient-Mini
| Property | Value |
|---|---|
| Parameter Count | 31.23M |
| License | Apache 2.0 |
| Paper | Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers |
| Memory Usage | 124.92 MB (FP32) / 62.46 MB (FP16) |
What is t5-efficient-mini?
T5-efficient-mini is a deep-narrow variant of Google's T5 model architecture, specifically designed to optimize downstream task performance. This model represents a strategic architectural choice where depth is prioritized over width, containing 4 encoder and decoder layers with 384-dimensional embeddings and 8 attention heads.
Implementation Details
The model features a carefully balanced architecture with 1536-dimensional feed-forward layers and 32-dimensional key/value projections. It was pretrained on the C4 dataset for 524,288 steps using span-based masked language modeling, making it particularly suitable for English language tasks.
- Deep-narrow architecture optimization
- 31.23M parameters for efficient deployment
- Pretrained on C4 dataset
- Supports both FP32 and FP16 precision
Core Capabilities
- Text-to-text generation tasks
- Summarization capabilities
- Question answering applications
- Text classification (with adaptation)
Frequently Asked Questions
Q: What makes this model unique?
The model's deep-narrow architecture strategy sets it apart, prioritizing depth over width for better efficiency in downstream tasks. This approach has been shown to be more Pareto-efficient compared to wider, shallower models of similar parameter count.
Q: What are the recommended use cases?
The model requires fine-tuning for practical usage and is particularly well-suited for English NLP tasks including summarization, question answering, and text classification. It's recommended for applications where a balance between model size and performance is crucial.
