A.7 Model Card Table 52 presents a model card (Mitchell et al., 2018; Anil et al., 2023) that summarizes details of the models. Model Details Model Developers Meta AI Variations Llama 2 comes in a range of parameter sizes—7B, 13B, and 70B—as well as pretrained and fine-tuned variations. Input Models input text only. Output Models generate text only. Model Architecture Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforce- ment learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. Model Dates Llama 2 was trained between January 2023 and July 2023. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License A custom commercial license is available at: ai.meta.com/resources/ models-and-libraries/llama-downloads/ Where to send com- ments Instructions on how to provide feedback or comments on the model can be found in the model README, or by opening an issue in the GitHub repository (https://github.com/facebookresearch/llama/). Intended Use Intended Use Cases Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. Out-of-Scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. Hardware and Software (Section 2.2) Training Factors We used custom training libraries, Meta’s Research Super Cluster, and produc- tion clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. Carbon Footprint Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO 2 eq, 100% of which were offset by Meta’s sustainability program. Training Data (Sections 2.1 and 3) Overview Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. Data Freshness The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. Evaluation Results See evaluations for pretraining (Section 2); fine-tuning (Section 3); and safety (Section 4). Ethical Considerations and Limitations (Section 5.2) Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available available at https://ai.meta.com/llama/responsible-user-guide Table 52: Model card for Llama 2. 77