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--- |
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license: apache-2.0 |
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inference: false |
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--- |
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# Dromedary Model Card |
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**NOTE: This "delta model" cannot be used directly.** |
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Users have to apply it on top of the original LLaMA weights to get actual Dromedary weights. |
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See https://github.com/IBM/Dromedary#model-weights for instructions. |
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## Model details |
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<div align="center"> |
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<img src="https://raw.githubusercontent.com/IBM/Dromedary/main/assets/images/dromedary_logo.svg" alt="Dromedary Logo"/> |
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</div> |
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**Model type:** |
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Dromedary is an open-source self-aligned language model trained with minimal human supervision. |
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The base language model is LLaMA-65b, based on the transformer architecture. |
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**Model date:** |
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Dromedary was trained between April 2023 and May 2023, but its knowledge only goes up until Sept-2021. |
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**Organizations developing the model:** |
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The Dromedary team as a joint effort between CMU and IBM. |
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**Paper or resources for more information:** |
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https://mitibmdemos.draco.res.ibm.com/dromedary |
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**License:** |
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LLaMA's Non-commercial bespoke license |
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**Where to send questions or comments about the model:** |
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https://github.com/IBM/Dromedary/issues |
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## Intended use |
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**Primary intended uses:** |
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The primary use of Dromedary is research on the alignment of large language models. |
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**Primary intended users:** |
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The primary intended users of the model are researchers in artificial intelligence. |
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## Delta weights |
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We use the following configuration for the LoRA weights: |
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``` |
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--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \ |
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--lora_r=16 \ |
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``` |
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## Training dataset |
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Fewer than 300 lines of human annotations (including < 200 seed prompts, 16 generic principles, and 5 exemplars for in-context learning), |
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## Evaluation dataset |
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We evaluate Dromedary on TruthfulQA and HHH Eval, as well as Vicuna benchmark questions. |
