| Model Type | | text_generation, instruction-following |
|
| Use Cases |
| Areas: | | research, education, commercial |
|
| Applications: | | chatbots, virtual assistants |
|
| Primary Use Cases: | | text generation, instruction following |
|
| Limitations: | | Not suitable for critical real-time applications |
|
| Considerations: | | Test thoroughly before deployment in sensitive environments |
|
|
| Supported Languages | | English (high), Spanish (medium), French (low) |
|
| Training Details |
| Data Sources: | | open-access datasets, Instruction tuning datasets |
|
| Data Volume: | |
| Methodology: | | LoRA tuning on self_attn modules |
|
| Context Length: | |
| Training Time: | |
| Hardware Used: | |
| Model Architecture: | | LLaMA architecture with LoRA tuning |
|
|
| Safety Evaluation |
| Methodologies: | |
| Findings: | |
| Risk Categories: | |
| Ethical Considerations: | | Standard AI ethical practices assumed during model creation. |
|
|
| Responsible Ai Considerations |
| Fairness: | | Trained on a diverse dataset to reduce bias |
|
| Transparency: | | Model weights and training configurations are available publicly |
|
| Accountability: | | Gradient AI maintains accountability for model outputs |
|
| Mitigation Strategies: | | LoRA technique applied to mitigate overfitting |
|
|
| Input Output |
| Input Format: | | Plain text input with instructions |
|
| Accepted Modalities: | |
| Output Format: | |
| Performance Tips: | | Utilize prompt engineering for better results. |
|
|
| Release Notes |
| Version: | |
| Date: | |
| Notes: | | Initial release with LoRA tuning. |
|
|
|