Switch Transformer C-2048
| Property | Value |
|---|---|
| Parameters | 1.6 Trillion |
| Architecture | Switch Transformer (MoE) |
| Training Data | Colossal Clean Crawled Corpus (C4) |
| License | Apache 2.0 |
| Paper | Research Paper |
What is switch-c-2048?
Switch-c-2048 is a groundbreaking Mixture-of-Experts (MoE) language model that revolutionizes the scale of language modeling with its 1.6 trillion parameters. Built on the foundation of T5 architecture, it replaces traditional feed-forward layers with sparse MLP layers containing 2048 "expert" networks, achieving remarkable efficiency and performance improvements.
Implementation Details
The model employs a sophisticated architecture that enables a 4x speedup over T5-XXL while maintaining superior performance. It's pre-trained on the Masked Language Modeling (MLM) task using the Colossal Clean Crawled Corpus (C4), implementing sparse expert routing to efficiently process input tokens.
- 2048 expert networks for specialized processing
- Trained on TPU v3/v4 pods using t5x and jax
- Supports various precision formats (BF16, INT8)
- Requires significant computational resources and supports disk offloading
Core Capabilities
- Masked Language Modeling with high efficiency
- Scalable text generation and processing
- Flexible deployment with CPU/GPU support
- Advanced token routing through expert networks
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its massive scale combined with efficient sparse computation through 2048 expert networks, enabling faster training and superior performance compared to traditional transformer models.
Q: What are the recommended use cases?
The model is primarily designed for pre-training and requires fine-tuning for specific downstream tasks. Users should consider FLAN-T5 for immediate task-specific applications or fine-tune this model following provided guidelines.
