Pythia-2.8B
| Property | Value |
|---|---|
| Parameter Count | 2.8B parameters |
| Model Type | Transformer-based Language Model |
| Architecture | 32 layers, 2560 dim, 32 heads |
| License | Apache 2.0 |
| Paper | Pythia Paper |
What is Pythia-2.8B?
Pythia-2.8B is part of the Pythia Scaling Suite, a collection of language models specifically designed for interpretability research. This particular model features 2.8 billion parameters and was trained on The Pile dataset, making it comparable to models like GPT-Neo 2.7B and OPT-2.7B in terms of architecture and capabilities.
Implementation Details
The model implements a transformer architecture with 32 layers, a model dimension of 2560, and 32 attention heads. It was trained with a batch size of 2M tokens and a learning rate of 1.6x10^-4. The training process involved seeing approximately 300B tokens, with 154 checkpoints saved throughout training.
- Trained on The Pile dataset (825GiB of diverse English text)
- Uses GPT-NeoX architecture
- Implements Flash Attention for improved performance
- Available in FP16 and U8 tensor formats
Core Capabilities
- Text generation and completion tasks
- Research-focused applications
- Interpretability studies
- Scientific experimentation on language model behavior
- Foundation for fine-tuning specialized models
Frequently Asked Questions
Q: What makes this model unique?
Pythia-2.8B stands out for its research-oriented design, providing 154 training checkpoints that allow researchers to study model development over time. It's part of a carefully controlled experimental environment where all models in the suite are trained on identical data in the same order.
Q: What are the recommended use cases?
The model is primarily intended for research purposes, particularly in studying model behavior and interpretability. It's not designed for deployment in production environments or direct human-facing applications without additional fine-tuning and safety measures.
