Pythia-6.9B
| Property | Value |
|---|---|
| Parameter Count | 6.9B (6,444,163,072 non-embedding) |
| Architecture | 32 layers, 4096 model dimension, 32 attention heads |
| License | Apache 2.0 |
| Paper | Pythia Paper |
| Training Data | The Pile (825GiB dataset) |
What is pythia-6.9b?
Pythia-6.9B is part of EleutherAI's Pythia Scaling Suite, a collection of language models specifically designed for interpretability research. This particular model contains 6.9B parameters and was trained on The Pile dataset, featuring 32 transformer layers with a model dimension of 4096 and 32 attention heads.
Implementation Details
The model was trained using the GPT-NeoX framework with a batch size of 2M tokens and a learning rate of 1.2 x 10⁻⁴. It provides 154 intermediate checkpoints throughout training, making it particularly valuable for studying model behavior during the training process.
- Trained on 299,892,736,000 tokens
- Uses Flash Attention for improved performance
- Compatible with Hugging Face Transformers library
- Implements a learning rate schedule decaying to 10% of initial rate
Core Capabilities
- English language text generation
- Research-focused architecture suitable for interpretability studies
- Checkpoint analysis through 154 saved model states
- Comparable performance to similar-sized models like OPT-6.7B
Frequently Asked Questions
Q: What makes this model unique?
Pythia-6.9B stands out for its research-oriented design and extensive checkpoint system, allowing researchers to study model behavior throughout the training process. 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 the field of AI interpretability. 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 measures.
