\begin{table*}[htp]
\resizebox{\textwidth}{!}{
\begin{tabular}{lrrrrrr}
\toprule
Model/adapter & BBQ accuracy & # eval & # train & truncated & # prompt tokens & # output tokens \\
\midrule
DBRX Instruct & 0.77 & 100.0 &  &  & 92.36 & 1.0 \\
DeepSeek LLM Chat (67B) & 0.8 & 100.0 &  &  & 99.98 & 1.0 \\
Llama 3 Instruct (70B) & 0.89 & 100.0 &  &  & 92.36 & 1.0 \\
Llama 3 Instruct (8B) & 0.73 & 100.0 &  &  & 92.36 & 1.0 \\
Llama 3.1 Instruct Turbo (405B) & 0.92 & 100.0 &  &  & 92.36 & 1.0 \\
Llama 3.1 Instruct Turbo (70B) & 0.9 & 100.0 &  &  & 92.36 & 1.0 \\
Llama 3.1 Instruct Turbo (8B) & 0.73 & 100.0 &  &  & 92.36 & 1.0 \\
Mistral Instruct v0.3 (7B) & 0.67 & 100.0 &  &  & 105.37 & 1.0 \\
Mixtral Instruct (8x22B) & 0.87 & 100.0 &  &  & 105.37 & 1.0 \\
Qwen1.5 Chat (72B) & 0.79 & 100.0 &  &  & 92.55 & 1.0 \\
Qwen2 Instruct (72B) & 0.92 & 100.0 &  &  & 92.55 & 1.0 \\
Claude 3 Haiku (20240307) & 0.59 & 100.0 &  &  & 100.27 & 1.0 \\
Claude 3 Sonnet (20240229) & 0.88 & 100.0 &  &  & 100.27 & 1.0 \\
Claude 3 Opus (20240229) & 0.93 & 100.0 &  &  & 100.27 & 1.0 \\
Claude 3.5 Sonnet (20240620) & 0.93 & 100.0 &  &  & 100.27 & 1.0 \\
Command R & 0.7 & 100.0 &  &  & 97.66 &  \\
Command R Plus & 0.87 & 100.0 &  &  & 97.66 &  \\
GPT-3.5 Turbo (0613) & 0.65 & 100.0 &  &  & 92.36 & 1.0 \\
GPT-3.5 Turbo (1106) & 0.6 & 100.0 &  &  & 92.36 & 1.0 \\
GPT-3.5 Turbo (0125) & 0.54 & 100.0 &  &  & 92.36 & 1.0 \\
GPT-4 Turbo (2024-04-09) & 0.91 & 100.0 &  &  & 92.36 & 1.0 \\
GPT-4o (2024-05-13) & 0.95 & 100.0 &  &  & 91.31 & 1.0 \\
\bottomrule
\end{tabular}}
\caption{Results for bbq_subject:all (bbq)}
\label{fig:bbq_subject:all (bbq)}
\end{table*}