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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import os\n",
"import json\n",
"import datetime\n",
"\n",
"time_now = datetime.datetime.now().strftime(\"%Y-%m-%dT%H-%M-%S\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(\"results.csv\")\n",
"new_df = df.groupby([\"model\", \"problem\"], as_index=False)[['weighted_accuracy']].sum()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Claude 2', 'Claude Instant', 'GPT 3.5 Turbo', 'GPT 4 Turbo',\n",
" 'MPT-30b', 'Mistral-7b', 'PaLM 2', 'Phi-1.5', 'Phi-2', 'Qwen-14b',\n",
" 'Vicuna-13b', 'Yi-34b'], dtype=object)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_df.model.unique()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"open_models = {\n",
" \"Yi-34b\": \"01-ai/Yi-34B-Chat\",\n",
" \"Mistral-7b\": \"mistralai/Mistral-7B-Instruct-v0.1\",\n",
" \"Vicuna-13b\": \"lmsys/vicuna-13b-v1.3\",\n",
" \"Phi-1.5\": \"microsoft/phi-1_5\",\n",
" \"MPT-30b\": \"mosaicml/mpt-30b-instruct\",\n",
" \"Phi-2\": \"microsoft/phi-2\",\n",
" \"Qwen-14b\": \"Qwen/Qwen-14B-Chat\"\n",
"}\n",
"\n",
"model_params = {\n",
" 'Yi-34b': ('torch.bfloat16', 34.389),\n",
" 'Mistral-7b': ('torch.bfloat16', 7.242),\n",
" 'Vicuna-13b': ('torch.float16', 13.0),\n",
" 'Phi-1.5': ('torch.float16', 1.3),\n",
" 'MPT-30b': ('torch.bfloat16', 30.0),\n",
" 'Phi-2': ('torch.float16', 2.78),\n",
" 'Qwen-14b': ('torch.bfloat16', 14.167),\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def result_export(model_df, model_name):\n",
" model_df = model_df.set_index(\"problem\")\n",
" model_df = model_df.drop(columns=[\"model\"])\n",
" model_df = model_df.to_dict(orient=\"index\")\n",
" convert_problem_name = lambda x: x.replace(\"_Results\", \"\").replace(\"Results\", \"\").replace(\"bsp\", \"sas\").upper()\n",
" model_df = {convert_problem_name(k): v for k, v in model_df.items()}\n",
" return model_df"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"for model in new_df.model.unique(): \n",
" model_dir = open_models[model] if model in open_models else model.replace(\" \", \"-\")\n",
" # os.system(f\"rm -rf {model_dir.split('/')[0]}\")\n",
" os.makedirs(f\"{model_dir}\", exist_ok=True)\n",
" model_df = new_df[new_df[\"model\"] == model]\n",
" model_result = result_export(model_df, model)\n",
" model_result = {\n",
" \"config\": {\n",
" \"model_name\": model_dir, \n",
" \"model_type\": \"open-source\" if model in open_models else \"close-source\",\n",
" \"model_dtype\": model_params[model][0] if model in model_params else \"?\",\n",
" \"num_params\": model_params[model][1] if model in model_params else 0,\n",
" },\n",
" \"results\": model_result\n",
" }\n",
" with open(f\"{model_dir}/results_{time_now}.json\", \"w\") as f:\n",
" json.dump(model_result, f, indent=4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "llm_reason",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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