gpt2-117M
| Property | Value |
|---|---|
| Model Size | 117M parameters |
| Author | huseinzol05 |
| Model Type | GPT-2 Language Model |
| Source | Hugging Face |
What is gpt2-117M?
gpt2-117M is a compact implementation of OpenAI's GPT-2 architecture, featuring 117 million parameters. This model represents a balanced approach between computational efficiency and performance, making it suitable for various natural language processing tasks while remaining deployable in environments with limited resources.
Implementation Details
The model is built on the transformer architecture, specifically following the GPT-2 design principles. With 117M parameters, it sits at the smaller end of the GPT-2 model family, offering a good trade-off between performance and resource requirements.
- Transformer-based architecture with attention mechanisms
- Trained on broad internet text data
- Optimized for efficient inference
- Hosted on Hugging Face's model hub
Core Capabilities
- Text generation and completion
- Language understanding tasks
- Content summarization
- Question answering
- Zero-shot learning capabilities
Frequently Asked Questions
Q: What makes this model unique?
This model provides a practical balance between model size and capability, making it accessible for developers who need GPT-2's capabilities without the computational overhead of larger versions.
Q: What are the recommended use cases?
The model is well-suited for text generation tasks, content creation assistance, and general NLP applications where a lighter-weight model is preferred over larger alternatives.
