Pythia-410M
| Property | Value |
|---|---|
| Parameter Count | 405M total (302M non-embedding) |
| Architecture | 24 layers, 1024 model dimension, 16 attention heads |
| Training Data | The Pile (825GB dataset) |
| License | Apache 2.0 |
| Paper | Pythia Paper |
What is pythia-410M?
Pythia-410M is part of EleutherAI's Pythia Scaling Suite, a collection of models specifically designed for interpretability research. This medium-sized transformer model represents a careful balance between computational efficiency and capability, trained on The Pile dataset using the GPT-NeoX architecture.
Implementation Details
The model features a sophisticated architecture with 24 transformer layers, a model dimension of 1024, and 16 attention heads. It was trained with a batch size of 2M tokens and a learning rate of 3.0 x 10⁻⁴, making it comparable to models like OPT-350M in terms of architecture.
- Training included 299,892,736,000 tokens
- Provides 154 checkpoints throughout training
- Uses the same tokenizer as GPT-NeoX-20B
- Implements Flash Attention for improved performance
Core Capabilities
- English language text generation
- Research-focused model for interpretability studies
- Supports scientific investigation of language model behavior
- Compatible with Hugging Face Transformers library
Frequently Asked Questions
Q: What makes this model unique?
Pythia-410M stands out for its research-focused design, offering extensive training checkpoints and controlled experimental conditions. It's part of a carefully crafted model suite that enables systematic study of language model behavior across different scales.
Q: What are the recommended use cases?
The model is primarily intended for research purposes, particularly in interpretability studies. While it can be fine-tuned for downstream tasks, it's not recommended for direct deployment in production environments or human-facing applications without appropriate fine-tuning and safety considerations.
