pythia-410m-chatbot
| Property | Value |
|---|---|
| Model Size | 410M parameters |
| Quantization | Float16 |
| Accuracy | 0.56 |
| F1 Score | 0.56 |
| Dataset | sewon/ambig_qa |
| Hugging Face | Model Repository |
What is pythia-410m-chatbot?
The pythia-410m-chatbot is a quantized version of the Pythia architecture specifically optimized for question-answering tasks. This model represents a careful balance between efficiency and performance, utilizing float16 quantization to reduce model size while maintaining reasonable accuracy levels of 0.56.
Implementation Details
The model is implemented using the Hugging Face Transformers framework and has been fine-tuned on the ambig_qa dataset. Training was conducted over 3 epochs with a batch size of 4 and a learning rate of 2e-5. The model uses post-training quantization through PyTorch's framework to optimize deployment efficiency.
- Easy integration with Hugging Face Transformers library
- Float16 quantization for reduced model size
- Optimized for resource-constrained environments
- Built-in support for question-answering tasks
Core Capabilities
- Question-answering with 0.56 accuracy and F1 score
- Precision of 0.68 and recall of 0.56
- Efficient inference through quantization
- Maximum sequence length of 512 tokens
- Temperature-controlled response generation
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its optimized balance between performance and efficiency, achieved through float16 quantization while maintaining reasonable accuracy for Q&A tasks. It's specifically designed for deployment in resource-constrained environments while providing reliable question-answering capabilities.
Q: What are the recommended use cases?
The model is best suited for chatbot applications and question-answering systems where resource efficiency is important. It's particularly effective for deployments where full-precision models would be too resource-intensive, though users should note its limitations in domains outside the training dataset.
