Phi-2
Parameters
2.7B
Context Length
2K
Modality
Text
Architecture
Dense
KV Cache Rate
256 KB/tok
License
MIT License
Release Date
12 Oct 2023
Knowledge Cutoff
-
API Pricing (per 1M)
Self-hosted only
Hardware Sizing
Precision
Context
Workload Memory: 4.26 GB
INT4 · 2048 context
Base Weights: 1.26 GB
KV Cache: 0.50 GB
Runtime & Graphs: 2.50 GB
Minimum Required Hardware
1x NVIDIA L4 24GB
4.3 / 21.6 GB usable
1x RTX 3060 12GB
4.3 / 10.8 GB usable
Apple M4 24GB
4.3 / 18.0 GB usable
Determine requirements for your specific workload:
Open in VRAM Calculator
Architecture Diagram
Evaluation Benchmarks
No evaluation benchmarks for Phi-2 available.
Rankings
Overall Rank
-
Coding Rank
-
About Phi-2
Phi-2 is Microsoft's 2.7B parameter small language model engineered for advanced reasoning, mathematical logic, and code generation. Trained on 1.4T textbook-quality tokens, it delivers benchmark performance rivaling significantly larger models.
Technical Specifications
Attention
Attention Structure
Multi-Head Attention
Attention Heads
32
Key-Value Heads
32
Position Embedding
ROPE
RoPE Theta
10,000
Normalization
Layer Normalization
Activation Function
GELU
Dimensions
Hidden Dimension Size
2,048
Number of Layers
32
FFN Intermediate Size (Dense)
10,240
Tokenizer
Vocabulary Size
51,200
About Phi-2
Microsoft's Phi-2 is a 2.7 billion parameter Transformer-based model, developed for efficient language understanding and reasoning. Its technical innovations include training on "textbook-quality" synthetic and filtered web data, alongside scaled knowledge transfer from its predecessor, Phi-1.5, facilitating emergent capabilities within a compact architecture.
Other Phi-2 Models
- No related models available