Phi-3.5-mini-instruct-bnb-4bit
| Property | Value |
|---|---|
| Parameter Count | 3.8B |
| Context Length | 128K tokens |
| License | MIT |
| Paper | Technical Report |
| Supported Languages | 23 languages including English, Chinese, Arabic, etc. |
What is Phi-3.5-mini-instruct-bnb-4bit?
Phi-3.5-mini-instruct-bnb-4bit is Microsoft's lightweight yet powerful language model optimized for 4-bit inference. It's a dense decoder-only Transformer model that achieves remarkable performance despite its relatively small size of 3.8B parameters. The model has been trained on 3.4T tokens and supports an impressive 128K token context length, making it suitable for both short and long-form content processing.
Implementation Details
The model utilizes Flash Attention by default and has been optimized for efficient inference through 4-bit quantization. It's specifically designed to work with modern GPU architectures like NVIDIA A100, A6000, and H100.
- Trained on high-quality educational data, code, and synthetic "textbook-like" content
- Incorporates supervised fine-tuning, proximal policy optimization, and direct preference optimization
- Supports chat format with system, user, and assistant messages
- Implements comprehensive safety measures and multilingual capabilities
Core Capabilities
- Strong performance in reasoning tasks, particularly in code, math, and logic
- Multilingual support across 23 languages with competitive performance
- Long context processing with 128K token support
- Efficient deployment in memory/compute constrained environments
- Demonstrates strong performance in benchmarks like MMLU, GSM8K, and HumanEval
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for achieving near-competitive performance with much larger models while maintaining a small parameter count of 3.8B. It's particularly notable for its optimization for 4-bit inference and extensive multilingual capabilities.
Q: What are the recommended use cases?
The model is well-suited for commercial and research applications requiring efficient processing in resource-constrained environments, particularly for tasks involving reasoning, code generation, and multilingual content. It's especially effective for long-form content processing and technical applications.
