Model Type | |
Use Cases |
Areas: | commercial applications, research |
|
Applications: | chatbots, text generation, sensitivity analysis, multilingual assistance |
|
Primary Use Cases: | assistant-like chat, natural language generation tasks |
|
Limitations: | Use in languages beyond those explicitly referenced as supported is out of scope without additional fine-tuning. |
|
Considerations: | Developers may fine-tune models for unsupported languages while ensuring safe and responsible use. |
|
|
Additional Notes | Llama 3.1 models are not designed to be deployed in isolation and require additional safety guardrails when integrated into AI systems. |
|
Supported Languages | English (high), German (high), French (high), Italian (high), Portuguese (high), Hindi (high), Spanish (high), Thai (high) |
|
Training Details |
Data Sources: | publicly available online data |
|
Data Volume: | |
Context Length: | |
Model Architecture: | Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. |
|
|
Safety Evaluation |
Methodologies: | fine-tuning, adversarial testing, red teaming, multi-faceted data collection |
|
Findings: | Model refusals to benign prompts as well as refusal tone have been an area of focus., Adversarial prompts and comprehensive safety data responses have been incorporated. |
|
Risk Categories: | CBRNE helpfulness, Child Safety, Cyber attack enablement |
|
Ethical Considerations: | Llama 3.1 addresses users and their needs without imposing unnecessary judgment or normativity, focusing on the values of free thought and expression. |
|
|
Responsible Ai Considerations |
Fairness: | The model is designed to be accessible to people across different backgrounds and experiences. |
|
Transparency: | Includes transparency tools for safety and content evaluations. |
|
Accountability: | Llama models should be part of an overall AI system with additional safety guardrails deployed by developers. |
|
Mitigation Strategies: | Strategies include a three-pronged approach to managing trust & safety risks, developer guidance, and community engagement. |
|
|
Input Output |
Input Format: | ChatML prompt template or Alpaca prompt template |
|
Accepted Modalities: | |
Output Format: | |
Performance Tips: | Use specific prompt templates for better performance. |
|
|
Release Notes |
Version: | |
Date: | |
Notes: | Introduces new capabilities including longer context window and multilingual inputs. |
|
|
|