File size: 4,827 Bytes
1e5e2ae a24bc73 e09c49f a24bc73 1e5e2ae e09c49f a24bc73 e09c49f 1e5e2ae a24bc73 1e5e2ae a24bc73 1e5e2ae a24bc73 1e5e2ae |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 |
{
"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": "markdown",
"metadata": {},
"source": [
"- https://grabon.com/blog/claude-users-statistics/\n",
"- https://medium.com/@seanbetts/peering-inside-gpt-4-understanding-its-mixture-of-experts-moe-architecture-2a42eb8bdcb3\n",
"- https://www.cnbc.com/2023/05/16/googles-palm-2-uses-nearly-five-times-more-text-data-than-predecessor.html"
]
},
{
"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",
" 'Claude 2': ('?', 176),\n",
" 'Claude Instant': ('?', 60),\n",
" \"GPT 3.5 Turbo\": ('?', 175),\n",
" \"GPT 4 Turbo\": ('?', 1760),\n",
" 'PaLM 2': ('?', 340),\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
}
|