MPT-30B
| Property | Value |
|---|---|
| Parameter Count | 29.95B |
| License | Apache-2.0 |
| Context Length | 8192 tokens (expandable) |
| Architecture | Modified decoder-only transformer |
| Training Tokens | 1 trillion |
What is MPT-30B?
MPT-30B is a state-of-the-art language model developed by MosaicML, representing a significant advancement in open-source AI. This decoder-style transformer was trained from scratch on 1 trillion tokens of English text and code, incorporating several innovative architectural improvements for enhanced efficiency and performance.
Implementation Details
The model features a modified transformer architecture with 48 layers, 64 attention heads, and a dimensional size of 7168. It implements several technical innovations:
- FlashAttention for improved computational efficiency
- ALiBi (Attention with Linear Biases) for handling variable sequence lengths
- Bias-free architecture design
- 8k token context window with capability for extension
- Compatible with NVIDIA's FasterTransformer for efficient serving
Core Capabilities
- Long-form text generation with extended context handling
- Code generation and understanding
- Efficient inference on single GPU deployments
- Commercial usage support under Apache-2.0 license
- Scalable context length through ALiBi implementation
Frequently Asked Questions
Q: What makes this model unique?
MPT-30B stands out for its commercial-friendly license, extensive training data (1T tokens), ability to handle extremely long inputs via ALiBi, and optimized architecture for both training and inference. It's specifically designed to run on single GPU configurations, making it more accessible for deployment.
Q: What are the recommended use cases?
The model serves as a strong foundation for various downstream tasks through fine-tuning. It's particularly well-suited for applications requiring long context understanding, code generation, and general text generation tasks. However, it's recommended to fine-tune the base model before deployment in production environments.
