Phi-1.5
Parameters
1.3B
Context Length
2K
Modality
Text
Architecture
Dense
License
MIT
Release Date
10 Sept 2023
Knowledge Cutoff
-
API Pricing (per 1M)
Self-hosted only
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
2,048 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
No evaluation benchmarks for Phi-1.5 available.
Rankings
Overall Rank
-
Coding Rank
-
About Phi-1.5
Phi-1.5 is a 1.3B parameter open language model from Microsoft Research engineered for common sense reasoning and general natural language understanding. It was trained on 30B high-quality synthetic tokens for efficient NLP research.
Technical Specifications
Attention
Attention Structure
Multi-Head Attention
Attention Heads
32
Key-Value Heads
32
Attention Head Dimension
-
Position Embedding
ROPE
RoPE Theta
10,000
Sliding Window Attention
No
Sliding Window Size
-
Sliding Window Ratio
-
Linear Attention
-
Linear Attention Ratio
-
Normalization
RMS Normalization
Activation Function
GELU
Dimensions
Auxiliary Parameters
-
Hidden Dimension Size
2,048
Number of Layers
24
FFN Intermediate Size (Dense)
8,192
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
51,200
About Phi-1.5
Microsoft's Phi-1.5 is a 1.3 billion parameter Transformer model, a successor to Phi-1. It was trained on a curated synthetic dataset of "textbook-quality" for common sense reasoning. The architecture comprises 24 layers, 32 attention heads, and incorporates rotary embeddings.
Other Phi-1.5 Models
- No related models available