T5-Efficient-Tiny-NL32
| Property | Value |
|---|---|
| Parameter Count | 67.06 Million |
| Memory Usage | 268.25 MB (FP32) / 134.12 MB (FP16) |
| Architecture Type | Deep-Narrow T5 Variant |
| Research Paper | Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers |
| Author |
What is t5-efficient-tiny-nl32?
T5-efficient-tiny-nl32 is an innovative variant of Google's T5 model that implements a deep-narrow architecture strategy. With 32 transformer layers, this model represents a significant departure from the standard Tiny T5 architecture, emphasizing depth over width to achieve better efficiency and downstream performance.
Implementation Details
The model implements a unique architectural approach with 32 transformer blocks while maintaining the tiny model's narrow dimensions. It was pretrained on the Colossal, Cleaned version of Common Crawl (C4) for 524,288 steps using span-based masked language modeling.
- Model depth: 32 transformer blocks
- Embedding dimension (dm): 256
- Key/value dimension (kv): 32
- Number of attention heads (nh): 4
- Feed-forward dimension (ff): 1024
Core Capabilities
- Efficient parameter usage through deep-narrow architecture
- Optimized for English NLP tasks
- Suitable for fine-tuning on tasks like summarization, question answering, and text classification
- Balanced trade-off between model size and performance
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its deep-narrow architecture, featuring 32 transformer layers while maintaining a compact parameter count. This design choice follows research showing that increasing depth before width leads to better efficiency and performance.
Q: What are the recommended use cases?
The model requires fine-tuning for practical usage and is specifically designed for English NLP tasks. It can be fine-tuned for summarization, question answering, and text classification tasks using PyTorch, TensorFlow, or JAX/Flax frameworks.
