Phi-1.5
Parameters
1.3B
Context Length
2K
Modality
Text
Architecture
Dense
License
MIT
Release Date
10 Sept 2023
Knowledge Cutoff
-
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
Microsoft's Phi-1.5 is a Transformer-based language model containing 1.3 billion parameters. It was developed to continue the investigation into the capabilities of smaller language models, specifically focusing on common sense reasoning and general knowledge in natural language contexts. The model's design aims to provide the research community with a non-restricted, accessible model to explore challenges associated with large language models, such as reducing toxicity and enhancing controllability.
The architecture of Phi-1.5 is consistent with its predecessor, Phi-1, employing a decoder-only Transformer configuration. This architecture comprises 24 layers, with 32 attention heads, each having a dimension of 64. The model integrates Rotary Position Embeddings (RoPE) for positional encoding, utilizing a rotary dimension of 32, and leverages Flash Attention to enhance training speed and memory efficiency. A key innovation in Phi-1.5's development lies in its training methodology, which predominantly utilized a high-quality, synthetic "textbook-like" dataset. This dataset, totaling 30 billion tokens, includes 7 billion tokens from Phi-1's training data and approximately 20 billion newly generated synthetic tokens, primarily for imparting common sense reasoning and broad knowledge.
Phi-1.5 demonstrates capabilities in various natural language processing tasks, including text generation, question answering, and Python code generation. Although it is a base model not specifically fine-tuned for instruction following or through reinforcement learning from human feedback, it can produce relevant responses in formats such as QA and chat. Its compact size and specialized training regimen enable it to perform complex reasoning tasks, positioning it as a tool for research in areas like in-context learning and addressing model limitations.
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
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