Pythia-410M-Deduped
| Property | Value |
|---|---|
| Parameter Count | 405M parameters (302M non-embedding) |
| Model Type | Transformer-based Language Model |
| Architecture | 24 layers, 1024 model dimension, 16 attention heads |
| License | Apache 2.0 |
| Paper | Pythia Paper |
What is pythia-410m-deduped?
Pythia-410M-deduped is part of EleutherAI's Pythia Scaling Suite, a collection of models specifically developed for interpretability research. This particular model represents the mid-tier offering in the suite, trained on a deduplicated version of the Pile dataset. What makes it special is its controlled training environment and the availability of 154 intermediate checkpoints, making it an invaluable tool for studying model behavior during training.
Implementation Details
The model employs a GPT-NeoX architecture with precise specifications: 24 transformer layers, a model dimension of 1024, and 16 attention heads. It was trained using a batch size of 2M tokens and a learning rate of 3.0 x 10^-4, with training conducted over approximately 1.5 epochs on the deduplicated Pile dataset.
- Fully compatible with Hugging Face Transformers library
- Trained on 299,892,736,000 tokens
- Includes 154 training checkpoints for research purposes
- Uses Flash Attention for improved performance
Core Capabilities
- Next-token prediction for English text generation
- Research-focused architecture suitable for interpretability studies
- Supports academic investigation of language model behavior
- Comparable performance to similar-sized models like OPT-350M
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its research-oriented design and the availability of extensive training checkpoints, making it ideal for studying model development and behavior. It's part of a carefully controlled scaling suite where all models were trained on identical data in the same order.
Q: What are the recommended use cases?
This model is primarily intended for research purposes, particularly in the field of AI interpretability. While it can be used for text generation, it's not recommended for production deployment or direct human-facing applications without appropriate fine-tuning and safety measures.
