Model Type | text generation, instruction-tuned |
|
Use Cases |
Areas: | Commercial applications, Research |
|
Applications: | Assistant-like chat, Natural language generation tasks |
|
Primary Use Cases: | Dialogue, Instruction following |
|
Limitations: | Limited to English applications, Potential outputs: inaccurate, biased, objectionable |
|
Considerations: | Safety testing and tuning required before deployment |
|
|
Supported Languages | |
Training Details |
Data Sources: | publicly available online data, SlimPajama dataset, UltraChat dataset |
|
Data Volume: | 15 trillion tokens (pretraining), 10M human-annotated examples (fine-tuning) |
|
Methodology: | Supervised fine-tuning, Reinforcement learning with human feedback |
|
Context Length: | |
Hardware Used: | |
Model Architecture: | Auto-regressive transformer with optimized architecture |
|
|
Safety Evaluation |
Methodologies: | Red teaming, Adversarial evaluations |
|
Risk Categories: | CBRNE, Cybersecurity, Child Safety |
|
Ethical Considerations: | Assess responses related to adversarial risks and CBRNE threats |
|
|
Responsible Ai Considerations |
Fairness: | Fairness considerations are interwoven with model alignment strategies. |
|
Transparency: | Models openly released for safety evaluation and transparency. |
|
Accountability: | Model developers are accountable for ensuring alignment and safety. |
|
Mitigation Strategies: | Meta Llama Guard and Code Shield as safeguards. |
|
|
Input Output |
Input Format: | |
Accepted Modalities: | |
Output Format: | |
Performance Tips: | Apply supervised fine-tuning or RLHF for specific applications. |
|
|