\begin{table*}[htp]
\resizebox{\textwidth}{!}{
\begin{tabular}{lrrrrrr}
\toprule
Model/adapter & Program Accuracy & # eval & # train & truncated & # prompt tokens & # output tokens \\
\midrule
Llama 3 Instruct (70B) & 0.39 & 100.0 & 5.0 &  & 6657.32 & 15.56 \\
Llama 3 Instruct (8B) & 0.2 & 100.0 & 5.0 &  & 6657.32 & 9.88 \\
Llama 3.1 Instruct Turbo (405B) & 0.51 & 100.0 & 5.0 &  & 6657.32 & 14.44 \\
Llama 3.1 Instruct Turbo (70B) & 0.56 & 100.0 & 5.0 &  & 6657.32 & 2.04 \\
Llama 3.1 Instruct Turbo (8B) & 0.27 & 100.0 & 5.0 &  & 6657.32 & 16.12 \\
Mistral Instruct v0.3 (7B) & 0.33 & 100.0 & 5.0 &  & 8068.51 & 23.29 \\
Mixtral Instruct (8x22B) & 0.4 & 100.0 & 5.0 &  & 8068.51 & 22.98 \\
Mixtral Instruct (8x7B) & 0.24 & 100.0 & 5.0 &  & 8068.51 & 25.0 \\
Qwen1.5 Chat (72B) & 0.28 & 100.0 & 5.0 &  & 7415.31 & 19.36 \\
Qwen2 Instruct (72B) & 0.39 & 100.0 & 5.0 &  & 7415.31 & 18.06 \\
Claude 3 Haiku (20240307) & 0.38 & 100.0 & 5.0 &  & 6420.56 & 33.97 \\
Claude 3 Sonnet (20240229) & 0.33 & 100.0 & 5.0 &  & 6420.56 & 12.66 \\
Claude 3 Opus (20240229) & 0.35 & 100.0 & 5.0 &  & 6420.56 & 33.83 \\
Claude 3.5 Sonnet (20240620) & 0.22 & 100.0 & 5.0 &  & 6420.56 & 43.0 \\
Gemini 1.5 Pro (001) & 0.51 & 100.0 & 5.0 &  & 7529.62 &  \\
Gemini 1.5 Flash (001) & 0.48 & 100.0 & 5.0 &  & 7529.62 &  \\
GPT-3.5 Turbo (0125) & 0.37 & 100.0 & 5.0 &  & 6658.45 & 13.27 \\
GPT-4o (2024-05-13) & 0.47 & 100.0 & 5.0 &  & 6641.44 & 13.21 \\
GPT-4o mini (2024-07-18) & 0.41 & 100.0 & 5.0 &  & 6641.44 & 12.87 \\
\bottomrule
\end{tabular}}
\caption{Results for fin_qa_ (fin_qa)}
\label{fig:fin_qa_ (fin_qa)}
\end{table*}