Activelegacyllm Open Source

Phi-1.5

by Microsoft· Released September 2023

Phi-1.5 is a small language model with 1.3 billion parameters, trained on a blend of synthetic and textbook-quality data. It is designed for efficient reasoning and code generation, achieving strong performance on benchmarks despite its compact size.

Official Site 🤗 Hugging Face 📄 Research Paper

Input cost

Free (open source)

Output cost

Free (open source)

Context window

2048 tokens

Max output

—

Modalities

text

Parameters

1.3B

License

MIT

Capabilities

Code GenerationText GenerationReasoningMathQA

Best For

Lightweight code generation and reasoning tasks with limited computational resources.

Strengths

Strong performance on coding and math benchmarks for its size
Efficient inference due to small parameter count
Trained on high-quality synthetic data
Open-source and freely available

Limitations

Limited context window of 2048 tokens
Not suitable for complex multi-step reasoning
Smaller capacity compared to larger models
May struggle with nuanced language understanding

Use Cases

Code completion and generation

Educational tools for programming

Simple question answering

Prototyping and experimentation

Embedded systems with limited compute

Automated code review assistance

Math problem solving

Improvements Over Previous Model

First model in the Phi family, so no direct predecessor; improvements over baseline small models include synthetic data training and strong code/math performance.

Back to all models

Activelegacyllm Open Source

Phi-1.5

by Microsoft· Released September 2023

Official Site 🤗 Hugging Face 📄 Research Paper

Input cost

Free (open source)

Output cost

Free (open source)

Context window

2048 tokens

Max output

—

Modalities

text

Parameters

1.3B

License

MIT

Capabilities

Code GenerationText GenerationReasoningMathQA

Best For

Lightweight code generation and reasoning tasks with limited computational resources.

Strengths

Strong performance on coding and math benchmarks for its size
Efficient inference due to small parameter count
Trained on high-quality synthetic data
Open-source and freely available

Limitations

Limited context window of 2048 tokens
Not suitable for complex multi-step reasoning
Smaller capacity compared to larger models
May struggle with nuanced language understanding

Use Cases

Code completion and generation

Educational tools for programming

Simple question answering

Prototyping and experimentation

Embedded systems with limited compute

Automated code review assistance

Math problem solving

Improvements Over Previous Model

First model in the Phi family, so no direct predecessor; improvements over baseline small models include synthetic data training and strong code/math performance.

Back to all models