\begin{table*}[htp]
\resizebox{\textwidth}{!}{
\begin{tabular}{lrrrrrr}
\toprule
Model/adapter & LM Evaluated Safety score & # eval & # train & truncated & # prompt tokens & # output tokens \\
\midrule
DeepSeek LLM Chat (67B) & 0.7375 & 100.0 &  &  & 104.29 & 202.59 \\
Llama 3 Instruct (70B) & 0.6725 & 100.0 &  &  & 93.69 & 191.45 \\
Llama 3 Instruct (8B) & 0.7625 & 100.0 &  &  & 93.69 & 155.1 \\
Llama 3.1 Instruct Turbo (405B) & 0.8 & 100.0 &  &  & 93.69 & 120.25 \\
Llama 3.1 Instruct Turbo (70B) & 0.74 & 100.0 &  &  & 93.69 & 168.94 \\
Llama 3.1 Instruct Turbo (8B) & 0.8275 & 100.0 &  &  & 93.69 & 122.45 \\
Mistral Instruct v0.3 (7B) & 0.4075 & 100.0 &  & 0.01 & 114.01 & 430.07 \\
Mixtral Instruct (8x22B) & 0.615 & 100.0 &  &  & 114.02 & 338.73 \\
Qwen1.5 Chat (72B) & 0.7025 & 100.0 &  &  & 97.44 & 288.65 \\
Qwen2 Instruct (72B) & 0.7975 & 100.0 &  &  & 97.44 & 238.45 \\
Claude 3 Haiku (20240307) & 0.9325 & 100.0 &  &  & 97.15 & 133.83 \\
Claude 3 Sonnet (20240229) & 0.97 & 100.0 &  &  & 97.15 & 117.23 \\
Claude 3 Opus (20240229) & 0.985 & 100.0 &  &  & 97.15 & 106.75 \\
Claude 3.5 Sonnet (20240620) & 0.975 & 100.0 &  &  & 97.15 & 107.92 \\
Command R & 0.5075 & 100.0 &  &  & 99.5 &  \\
Command R Plus & 0.535 & 100.0 &  &  & 99.5 &  \\
GPT-3.5 Turbo (0613) & 0.7275 & 100.0 &  &  & 93.71 & 166.94 \\
GPT-3.5 Turbo (1106) & 0.6625 & 100.0 &  &  & 93.71 & 135.18 \\
GPT-3.5 Turbo (0125) & 0.645 & 100.0 &  &  & 93.71 & 145.25 \\
GPT-4 Turbo (2024-04-09) & 0.9 & 100.0 &  &  & 93.71 & 76.07 \\
GPT-4o (2024-05-13) & 0.8275 & 100.0 &  &  & 92.58 & 134.22 \\
\bottomrule
\end{tabular}}
\caption{Results for harm_bench_ (harm_bench)}
\label{fig:harm_bench_ (harm_bench)}
\end{table*}