Phi-3-mini
Parameters
3.8B
Context Length
4K
Modality
Text
Architecture
Dense
License
MIT
Release Date
22 Apr 2024
Knowledge Cutoff
Oct 2023
API Pricing (per 1M)
Input: $0.13 · Output: $0.52
System Requirements
VRAM requirements for different quantization methods and context sizes
1,024 tokens
Consumer
1x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
4,096 tokens
Consumer
1x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
Architecture Diagram
Evaluation Benchmarks
Rank
#186
| Benchmark | Score | Rank |
|---|---|---|
General Text | 1143 | 167 |
Intelligence Index | 0.06 | 217 |
Rankings
Overall Rank
#186
Coding Rank
-
About Phi-3-mini
Phi-3-mini is a lightweight 3.8B parameter dense language model developed by Microsoft for edge devices and mobile applications. It combines high reasoning density with LongRope context support extending up to 128K tokens.
Technical Specifications
Attention
Attention Structure
Grouped-Query Attention
Attention Heads
32
Key-Value Heads
8
Attention Head Dimension
-
Position Embedding
ROPE
RoPE Theta
10,000
Sliding Window Attention
Yes
Sliding Window Size
2,047
Sliding Window Ratio
-
Linear Attention
-
Linear Attention Ratio
-
Normalization
RMS Normalization
Activation Function
Swish
Dimensions
Hidden Dimension Size
3,072
Number of Layers
32
FFN Intermediate Size (Dense)
8,192
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
32,064
About Phi-3
Microsoft's Phi-3 models are small language models designed for efficient operation on resource-constrained devices. They utilize a transformer decoder architecture and are trained on extensively filtered, high-quality data, including synthetic compositions. This approach enables a compact yet capable model family.