GPT-Neo 125M
| Property | Value |
|---|---|
| Parameter Count | 150M |
| Training Data | The Pile Dataset |
| License | MIT |
| Paper | Research Paper |
| Training Steps | 572,300 |
What is gpt-neo-125m?
GPT-Neo 125M is a transformer-based language model developed by EleutherAI, designed as part of their initiative to replicate and improve upon the GPT-3 architecture. With 150 million parameters, it represents an accessible entry point into large language models while maintaining reasonable computational requirements.
Implementation Details
The model was trained on The Pile, a diverse 800GB dataset, for 300 billion tokens across 572,300 training steps. It implements a masked autoregressive language model architecture, utilizing cross-entropy loss for training optimization.
- Transformer-based architecture optimized for text generation
- Supports both PyTorch and JAX implementations
- Available in multiple tensor formats (F32, U8)
- Includes Safetensors support for enhanced security
Core Capabilities
- Text generation with contextual understanding
- Achieves 25.79% average performance on benchmark tasks
- Notable performance on TruthfulQA (45.58%) and Winogrande (51.78%)
- Supports zero-shot to 25-shot learning scenarios
Frequently Asked Questions
Q: What makes this model unique?
GPT-Neo 125M stands out for its efficient architecture and open-source nature, making it accessible for researchers and developers who need a smaller but capable language model. Its training on The Pile dataset provides it with broad knowledge across various domains.
Q: What are the recommended use cases?
The model excels at text generation tasks and can be effectively used for content creation, text completion, and basic language understanding tasks. However, users should note the model's limitations regarding potential biases and the need for output curation.
