Pythia-1.4B
| Property | Value |
|---|---|
| Parameter Count | 1.4B (1,414,647,808 parameters) |
| Architecture | 24 layers, 2048 model dimension, 16 attention heads |
| Training Data | The Pile (825GiB dataset) |
| License | Apache 2.0 |
| Paper | Pythia Paper |
What is pythia-1.4b?
Pythia-1.4B is part of the Pythia Scaling Suite, a collection of language models specifically designed for interpretability research. This particular model represents a medium-sized variant with 1.4 billion parameters, trained on The Pile dataset. It features 154 intermediate checkpoints, making it particularly valuable for studying model behavior during training.
Implementation Details
The model is implemented using the GPT-NeoX architecture and trained with a batch size of 2M tokens. It uses a learning rate of 2.0 x 10^-4 and matches the architecture of models like GPT-Neo 1.3B and OPT-1.3B.
- 24 transformer layers with 2048 dimensional states
- 16 attention heads for parallel processing
- Trained on 299,892,736,000 tokens
- Implements Flash Attention for improved performance
Core Capabilities
- Text generation and completion tasks
- Research-focused applications in interpretability studies
- Checkpoint analysis through 154 training snapshots
- English language processing and generation
Frequently Asked Questions
Q: What makes this model unique?
Pythia-1.4B stands out for its research-oriented design with extensive checkpoint availability, allowing researchers to study model evolution during training. It's part of a carefully controlled experimental setting with consistent training conditions across different model sizes.
Q: What are the recommended use cases?
The model is primarily intended for research on language model behavior and interpretability studies. It's not designed for deployment in production environments or direct human-facing applications, and should not be used for factual generation or commercial chatbots without appropriate fine-tuning.
