Phi-3.5-mini-instruct
| Property | Value |
|---|---|
| Parameter Count | 3.82B |
| Context Length | 128K tokens |
| License | MIT |
| Paper | Technical Report |
| Supported Languages | 23 languages including English, Chinese, Arabic, German, etc. |
What is Phi-3.5-mini-instruct?
Phi-3.5-mini-instruct is a lightweight, state-of-the-art language model that achieves remarkable performance despite its compact size of 3.82B parameters. Built upon the datasets used for Phi-3, it focuses on high-quality, reasoning-dense data and supports an impressive 128K token context length.
Implementation Details
The model leverages a decoder-only Transformer architecture and has undergone comprehensive enhancement through supervised fine-tuning, proximal policy optimization, and direct preference optimization. It requires specific GPU hardware for optimal performance, being tested on NVIDIA A100, A6000, and H100.
- Training involved 3.4T tokens across multiple data sources
- Supports flash attention for improved performance
- Implements robust safety measures and instruction adherence
Core Capabilities
- Multilingual support across 23 languages with competitive performance
- Strong performance in reasoning tasks, particularly in code, math, and logic
- Long-context understanding with 128K token support
- Efficient operation in memory/compute constrained environments
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to achieve performance comparable to much larger models (7B-12B parameters) while maintaining a compact size of 3.82B parameters makes it unique. It also offers extensive multilingual capabilities and long context support, making it versatile for various applications.
Q: What are the recommended use cases?
The model is ideal for scenarios requiring: 1) Memory/compute constrained environments, 2) Latency-sensitive applications, 3) Strong reasoning capabilities in code and math, and 4) Multilingual support. It's particularly suitable for commercial and research applications needing efficient language processing.
