Use in languages other than English, Use in violation of applicable laws or regulations
Additional Notes
Llama 2 models use Grouped-Query Attention (GQA) for improved inference scalability.
Supported Languages
English (proficient)
Training Details
Data Sources:
*A new mix of publicly available online data*
Data Volume:
2 trillion tokens
Methodology:
Supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF)
Context Length:
4000
Hardware Used:
Meta's Research Super Cluster, A100-80GB
Model Architecture:
Auto-regressive language model with transformer architecture
Responsible Ai Considerations
Fairness:
Testing conducted to date has been in English and cannot cover all scenarios.
Transparency:
Developers should perform safety testing and tuning tailored to their specific applications.
Accountability:
Restrictions on use to prevent infringement of laws and regulations.
Mitigation Strategies:
Align to human preferences for helpfulness and safety.
Input Output
Input Format:
Text
Accepted Modalities:
text
Output Format:
Text
Performance Tips:
To get the expected features and performance, a specific formatting needs to be followed, including `INST` and `<>` tags, `BOS` and `EOS` tokens, and whitespaces/breaklines.
Note: green Score (e.g. "73.2") means that the model is better than NousResearch/Llama-2-7b-chat-hf.
Rank the Llama 2 7B Chat Hf Capabilities
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Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation
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