distilgpt2-tiny-conversational
| Property | Value |
|---|---|
| Base Model | DistilGPT2 |
| Training Dataset | Wizard of Wikipedia |
| Final Loss | 2.2461 |
| Training Duration | 30 Epochs |
| Author | ethzanalytics |
| Model Link | HuggingFace |
What is distilgpt2-tiny-conversational?
distilgpt2-tiny-conversational is a specialized conversational AI model that builds upon the DistilGPT2 architecture. It's specifically fine-tuned for dialogue generation using the Wizard of Wikipedia dataset, implementing a unique persona framework that distinguishes between two conversational entities: person alpha and person beta.
Implementation Details
The model was trained using DeepSpeed and HuggingFace Trainer, with careful optimization using Adam optimizer (betas=0.9,0.999). Training utilized multi-GPU setup with gradient accumulation steps of 4 and a total batch size of 128. The learning rate was set at 2e-05 with a cosine scheduler and 5% warmup ratio.
- Training Infrastructure: Multi-GPU with DeepSpeed optimization
- Batch Processing: 32 batch size with 4 gradient accumulation steps
- Training Duration: 30 epochs with progressive loss improvement
- Final Validation Loss: 2.2461
Core Capabilities
- Conversational dialogue generation between two distinct personas
- Integration with ai-msgbot framework
- Custom conversation handling using alpha/beta framework
- Knowledge-grounded responses based on Wikipedia data
Frequently Asked Questions
Q: What makes this model unique?
The model's unique feature is its persona-based framework that uses custom tokens to delineate between different speakers in a conversation, making it particularly effective for chatbot applications.
Q: What are the recommended use cases?
This model is specifically designed for integration with the ai-msgbot repository for building conversational agents. It's particularly suited for applications requiring back-and-forth dialogue with distinct speaker roles.
