Phi-3-small
Parameters
7B
Context Length
8K
Modality
Text
Architecture
Dense
License
MIT License
Release Date
22 Apr 2024
Knowledge Cutoff
Oct 2023
API Pricing (per 1M)
Input: $0.15 · Output: $0.60
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
8,192 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
#194
| Benchmark | Score | Rank |
|---|---|---|
General Text | 1171 | 162 |
Rankings
Overall Rank
#194
Coding Rank
-
About Phi-3-small
Phi-3-small is a 7B parameter small language model from Microsoft engineered for commercial reasoning, mathematics, and code generation. It utilizes blocksparse attention and LongRope technology to support contexts 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
1,000,000
Sliding Window Attention
No
Sliding Window Size
-
Sliding Window Ratio
-
Linear Attention
-
Linear Attention Ratio
-
Normalization
-
Activation Function
Gated GELU
Dimensions
Hidden Dimension Size
4,096
Number of Layers
32
FFN Intermediate Size (Dense)
14,336
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
100,352
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.