assistant-like chat, natural language generation tasks
Considerations:
A specific formatting needs to be followed for chat versions to get expected features.
Additional Notes
Pretrained models can be adapted for a variety of natural language generation tasks. Out-of-scope uses include violation of laws, non-English languages, and prohibited uses by the Acceptable Use Policy.
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, production clusters, third-party cloud compute, A100-80GB GPUs
Model Architecture:
Optimized transformer architecture
Safety Evaluation
Ethical Considerations:
Llama 2 may produce inaccurate, biased or objectionable responses. Testing conducted has not covered all scenarios.
Responsible Ai Considerations
Mitigation Strategies:
Safety testing and tuning should be done before deploying.
Input Output
Input Format:
text
Accepted Modalities:
text
Output Format:
text
Performance Tips:
Follow specific formatting including INST, <>, BOS, EOS tokens, etc. for chat versions.
Note: green Score (e.g. "73.2") means that the model is better than localmodels/Llama-2-7B-Chat-GPTQ.
Rank the Llama 2 7B Chat GPTQ 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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