MPT-7B-Instruct
| Property | Value |
|---|---|
| Parameter Count | 6.7B |
| License | Apache 2.0 |
| Architecture | Modified decoder-only transformer |
| Context Length | 2048 tokens (expandable) |
| Training Data | Dolly-15k and Anthropic HH-RLHF datasets |
What is MPT-7B-Instruct?
MPT-7B-Instruct is an instruction-tuned language model developed by MosaicML, built upon their MPT-7B base model. It's specifically designed for short-form instruction following, incorporating advanced attention mechanisms and architectural optimizations. The model represents a significant step forward in open-source, commercially usable LLMs.
Implementation Details
The model implements several cutting-edge technical innovations, making it both efficient and powerful:
- Uses FlashAttention for improved computational efficiency
- Implements ALiBi (Attention with Linear Biases) instead of traditional positional embeddings
- Features 32 layers and 32 attention heads
- Employs a 4096-dimensional model representation
- Supports dynamic sequence length adjustment during inference
Core Capabilities
- Instruction following and task completion
- Expandable context window beyond training length
- Commercial usage under Apache 2.0 license
- Efficient inference with triton implementation support
- BFloat16 precision support for optimized performance
Frequently Asked Questions
Q: What makes this model unique?
MPT-7B-Instruct combines commercial usability with state-of-the-art architectural features like FlashAttention and ALiBi, while being trained on high-quality instruction datasets. Its Apache 2.0 license makes it particularly valuable for commercial applications.
Q: What are the recommended use cases?
The model excels at short-form instruction following tasks, making it suitable for chatbots, question-answering systems, and general instruction-based applications. Its expandable context length also allows for handling longer inputs than its training sequence length.
