{"cells":[{"cell_type":"code","execution_count":88,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{},"inputWidgets":{},"nuid":"0ea8b46b-839b-445b-8043-ccdf4e920ace","showTitle":false,"title":""},"id":"YLH80COBzi_F"},"outputs":[{"name":"stdout","output_type":"stream","text":["The autoreload extension is already loaded. To reload it, use:\n"," %reload_ext autoreload\n"]}],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":89,"metadata":{"id":"63B5exAuzq4M"},"outputs":[],"source":["from pathlib import Path\n","\n","if \"workding_dir\" not in locals():\n"," try:\n"," from google.colab import drive\n"," drive.mount('/content/drive')\n"," workding_dir = \"/content/drive/MyDrive/logical-reasoning/\"\n"," except ModuleNotFoundError:\n"," workding_dir = str(Path.cwd().parent)"]},{"cell_type":"code","execution_count":90,"metadata":{"executionInfo":{"elapsed":368,"status":"ok","timestamp":1719461634865,"user":{"displayName":"Donghao Huang","userId":"00463591218503521679"},"user_tz":-480},"id":"zFulf0bg0H-9","outputId":"debdd535-c828-40b9-efc0-8a180e5830dd"},"outputs":[{"name":"stdout","output_type":"stream","text":["workding dir: /Users/inflaton/code/engd/projects/logical-reasoning\n"]}],"source":["import os\n","import sys\n","\n","os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"workding dir:\", workding_dir)"]},{"cell_type":"code","execution_count":91,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{},"inputWidgets":{},"nuid":"9f67ec60-2f24-411c-84eb-0dd664b44775","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":589,"status":"ok","timestamp":1719462011879,"user":{"displayName":"Donghao Huang","userId":"00463591218503521679"},"user_tz":-480},"id":"DIUiweYYzi_I","outputId":"e16e9247-9077-4b0c-f8ea-17059f05a1c4"},"outputs":[{"name":"stdout","output_type":"stream","text":["loading env vars from: /Users/inflaton/code/engd/projects/logical-reasoning/.env\n"]},{"data":{"text/plain":["True"]},"execution_count":91,"metadata":{},"output_type":"execute_result"}],"source":["from dotenv import find_dotenv, load_dotenv\n","\n","found_dotenv = find_dotenv(\".env\")\n","\n","if len(found_dotenv) == 0:\n"," found_dotenv = find_dotenv(\".env.example\")\n","print(f\"loading env vars from: {found_dotenv}\")\n","load_dotenv(found_dotenv, override=True)"]},{"cell_type":"code","execution_count":92,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat datasets/mgtv data/open_source_models_few_shots_results.csv 2048\n"]}],"source":["import os\n","\n","model_name = os.getenv(\"MODEL_NAME\")\n","data_path = os.getenv(\"LOGICAL_REASONING_DATA_PATH\")\n","results_path = os.getenv(\"LOGICAL_REASONING_RESULTS_PATH\")\n","max_new_tokens = int(os.getenv(\"MAX_NEW_TOKENS\", 2048))\n","\n","print(model_name, data_path, results_path, max_new_tokens)"]},{"cell_type":"code","execution_count":93,"metadata":{},"outputs":[],"source":["from llm_toolkit.logical_reasoning_utils import *"]},{"cell_type":"code","execution_count":94,"metadata":{},"outputs":[],"source":["model_orders = {\n"," \"Mistral-7B-v0.3-Chinese-Chat\": 5,\n"," \"internlm2_5-7b-chat\": 9,\n"," \"internlm2_5-7b-chat-1m\": 10,\n"," \"Qwen2-7B-Instruct\": 20,\n"," \"Llama3.1-8B-Chinese-Chat\": 30,\n"," \"internlm2_5-20b-chat\": 35,\n"," \"Llama3.1-70B-Chinese-Chat\": 40,\n"," \"Qwen2-72B-Instruct\": 50,\n"," \"gpt-4o-mini\": 60,\n"," \"o1-mini\": 65,\n"," \"gpt-4o\": 70,\n"," \"o1-preview\": 80,\n","}"]},{"cell_type":"code","execution_count":99,"metadata":{},"outputs":[],"source":["markers = [\n"," \"o\",\n"," \"x\",\n"," \"^\",\n"," \"s\",\n"," \"d\",\n"," \"P\",\n"," \"X\",\n"," \"*\",\n"," \"v\",\n"," \">\",\n"," \"<\",\n"," \"p\",\n"," \"h\",\n"," \"H\",\n"," \"+\",\n"," \"|\",\n"," \"_\",\n","]\n","model_markers = {k: markers[i] for i, k in enumerate(model_orders.keys())}"]},{"cell_type":"code","execution_count":48,"metadata":{},"outputs":[{"data":{"text/html":["
\n","\n","
\n"," \n"," \n"," | \n"," shots | \n"," model | \n"," run | \n"," accuracy | \n"," precision | \n"," recall | \n"," f1 | \n"," ratio_valid_classifications | \n","
\n"," \n"," \n"," \n"," 0 | \n"," 0 | \n"," Mistral-7B-v0.3-Chinese-Chat | \n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/shot... | \n"," 0.694667 | \n"," 0.701136 | \n"," 0.694667 | \n"," 0.663408 | \n"," 0.011667 | \n","
\n"," \n"," 1 | \n"," 10 | \n"," Mistral-7B-v0.3-Chinese-Chat | \n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/shot... | \n"," 0.603667 | \n"," 0.733491 | \n"," 0.603667 | \n"," 0.649319 | \n"," 0.106333 | \n","
\n"," \n"," 0 | \n"," 0 | \n"," internlm2_5-7b-chat | \n"," internlm/internlm2_5-7b-chat/shots-00 | \n"," 0.705000 | \n"," 0.739804 | \n"," 0.705000 | \n"," 0.690636 | \n"," 1.000000 | \n","
\n"," \n"," 1 | \n"," 10 | \n"," internlm2_5-7b-chat | \n"," internlm/internlm2_5-7b-chat/shots-10 | \n"," 0.553333 | \n"," 0.730174 | \n"," 0.553333 | \n"," 0.625097 | \n"," 0.988333 | \n","
\n"," \n"," 0 | \n"," 0 | \n"," internlm2_5-7b-chat-1m | \n"," internlm/internlm2_5-7b-chat-1m/shots-00 | \n"," 0.481333 | \n"," 0.760525 | \n"," 0.481333 | \n"," 0.524452 | \n"," 0.998667 | \n","
\n"," \n"," 1 | \n"," 10 | \n"," internlm2_5-7b-chat-1m | \n"," internlm/internlm2_5-7b-chat-1m/shots-10 | \n"," 0.647333 | \n"," 0.728207 | \n"," 0.647333 | \n"," 0.665825 | \n"," 0.886667 | \n","
\n"," \n"," 0 | \n"," 0 | \n"," Qwen2-7B-Instruct | \n"," Qwen/Qwen2-7B-Instruct/shots-00 | \n"," 0.683000 | \n"," 0.749310 | \n"," 0.683000 | \n"," 0.710140 | \n"," 0.999667 | \n","
\n"," \n"," 1 | \n"," 10 | \n"," Qwen2-7B-Instruct | \n"," Qwen/Qwen2-7B-Instruct/shots-10 | \n"," 0.564667 | \n"," 0.739120 | \n"," 0.564667 | \n"," 0.606405 | \n"," 0.989667 | \n","
\n"," \n"," 0 | \n"," 0 | \n"," Llama3.1-8B-Chinese-Chat | \n"," shenzhi-wang/Llama3.1-8B-Chinese-Chat/shots-00 | \n"," 0.742000 | \n"," 0.747706 | \n"," 0.742000 | \n"," 0.737105 | \n"," 0.803333 | \n","
\n"," \n"," 1 | \n"," 10 | \n"," Llama3.1-8B-Chinese-Chat | \n"," shenzhi-wang/Llama3.1-8B-Chinese-Chat/shots-10 | \n"," 0.667667 | \n"," 0.783408 | \n"," 0.667667 | \n"," 0.708261 | \n"," 0.962333 | \n","
\n"," \n"," 0 | \n"," 0 | \n"," internlm2_5-20b-chat | \n"," internlm/internlm2_5-20b-chat/shots-00 | \n"," 0.564000 | \n"," 0.774526 | \n"," 0.564000 | \n"," 0.635219 | \n"," 0.672667 | \n","
\n"," \n"," 0 | \n"," 0 | \n"," Llama3.1-70B-Chinese-Chat | \n"," shenzhi-wang/Llama3.1-70B-Chinese-Chat/shots-00 | \n"," 0.763667 | \n"," 0.780665 | \n"," 0.763667 | \n"," 0.752581 | \n"," 0.009667 | \n","
\n"," \n"," 0 | \n"," 0 | \n"," Qwen2-72B-Instruct | \n"," Qwen/Qwen2-72B-Instruct_torch/shots-00 | \n"," 0.751667 | \n"," 0.794938 | \n"," 0.751667 | \n"," 0.757250 | \n"," 0.977333 | \n","
\n"," \n","
\n","
"],"text/plain":[" shots model \\\n","0 0 Mistral-7B-v0.3-Chinese-Chat \n","1 10 Mistral-7B-v0.3-Chinese-Chat \n","0 0 internlm2_5-7b-chat \n","1 10 internlm2_5-7b-chat \n","0 0 internlm2_5-7b-chat-1m \n","1 10 internlm2_5-7b-chat-1m \n","0 0 Qwen2-7B-Instruct \n","1 10 Qwen2-7B-Instruct \n","0 0 Llama3.1-8B-Chinese-Chat \n","1 10 Llama3.1-8B-Chinese-Chat \n","0 0 internlm2_5-20b-chat \n","0 0 Llama3.1-70B-Chinese-Chat \n","0 0 Qwen2-72B-Instruct \n","\n"," run accuracy precision \\\n","0 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/shot... 0.694667 0.701136 \n","1 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/shot... 0.603667 0.733491 \n","0 internlm/internlm2_5-7b-chat/shots-00 0.705000 0.739804 \n","1 internlm/internlm2_5-7b-chat/shots-10 0.553333 0.730174 \n","0 internlm/internlm2_5-7b-chat-1m/shots-00 0.481333 0.760525 \n","1 internlm/internlm2_5-7b-chat-1m/shots-10 0.647333 0.728207 \n","0 Qwen/Qwen2-7B-Instruct/shots-00 0.683000 0.749310 \n","1 Qwen/Qwen2-7B-Instruct/shots-10 0.564667 0.739120 \n","0 shenzhi-wang/Llama3.1-8B-Chinese-Chat/shots-00 0.742000 0.747706 \n","1 shenzhi-wang/Llama3.1-8B-Chinese-Chat/shots-10 0.667667 0.783408 \n","0 internlm/internlm2_5-20b-chat/shots-00 0.564000 0.774526 \n","0 shenzhi-wang/Llama3.1-70B-Chinese-Chat/shots-00 0.763667 0.780665 \n","0 Qwen/Qwen2-72B-Instruct_torch/shots-00 0.751667 0.794938 \n","\n"," recall f1 ratio_valid_classifications \n","0 0.694667 0.663408 0.011667 \n","1 0.603667 0.649319 0.106333 \n","0 0.705000 0.690636 1.000000 \n","1 0.553333 0.625097 0.988333 \n","0 0.481333 0.524452 0.998667 \n","1 0.647333 0.665825 0.886667 \n","0 0.683000 0.710140 0.999667 \n","1 0.564667 0.606405 0.989667 \n","0 0.742000 0.737105 0.803333 \n","1 0.667667 0.708261 0.962333 \n","0 0.564000 0.635219 0.672667 \n","0 0.763667 0.752581 0.009667 \n","0 0.751667 0.757250 0.977333 "]},"execution_count":48,"metadata":{},"output_type":"execute_result"}],"source":["import pandas as pd\n","\n","perf_df = None\n","model_perf_dfs = {}\n","for model_name in model_orders.keys():\n"," metrics_csv = f\"data/{model_name}_shots_metrics.csv\"\n"," if not Path(metrics_csv).exists():\n"," print(f\"metrics file not found: {metrics_csv}\")\n"," continue\n"," df = pd.read_csv(metrics_csv)\n"," model_perf_dfs[model_name] = df\n"," perf_df = df if perf_df is None else pd.concat([perf_df, df])\n","\n","perf_df"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[],"source":["import matplotlib.pyplot as plt\n","from matplotlib.ticker import MultipleLocator\n","\n","\n","def plot_perf(\n"," model_perf_dfs,\n"," model_markers,\n"," x_major_locator=5,\n"," y_offset=0.005,\n"," variant=\"shots\"\n","):\n"," fig, ax = plt.subplots(1, 1, figsize=(12, 6))\n","\n"," for model_name, perf_df in model_perf_dfs.items():\n"," # Ensure the lengths of perf_df[\"epoch\"], perf_df[\"accuracy\"], and perf_df[\"f1\"] are the same\n"," min_length = min(\n"," len(perf_df[variant]), len(perf_df[\"accuracy\"]), len(perf_df[\"f1\"])\n"," )\n"," perf_df = perf_df.iloc[:min_length]\n","\n"," (line,) = ax.plot(\n"," perf_df[variant],\n"," perf_df[\"f1\"],\n"," marker=model_markers[model_name],\n"," label=model_name,\n"," )\n","\n"," line_color = line.get_color()\n","\n"," best_f1 = perf_df[\"f1\"].idxmax()\n"," print(\n"," f\"Best F1 for {model_name} @ {perf_df[variant].iloc[best_f1]:.2f} {variant}: {perf_df['f1'].iloc[best_f1]}\"\n"," )\n"," ax.annotate(\n"," f\"{perf_df['f1'].iloc[best_f1]*100:.2f}%\",\n"," (perf_df[variant].iloc[best_f1], perf_df[\"f1\"].iloc[best_f1]),\n"," ha=\"center\",\n"," va=\"bottom\",\n"," xytext=(0, 0),\n"," textcoords=\"offset points\",\n"," fontsize=10,\n"," color=line_color,\n"," )\n","\n"," # Set y-axis limit\n"," y_scales = ax.get_ylim()\n"," ax.set_ylim(y_scales[0], y_scales[1] + y_offset)\n","\n"," # Add title and labels\n"," ax.set_xlabel(\"Epoch (0: base model, 0.2 - 2: fine-tuned models)\")\n"," ax.set_ylabel(\"F1 Score\")\n","\n"," # Set x-axis grid spacing to 0.2\n"," ax.xaxis.set_major_locator(MultipleLocator(x_major_locator))\n"," ax.set_title(\"Performance Analysis Across Checkpoints for Models\")\n","\n"," # Rotate x labels\n"," plt.xticks(rotation=0)\n"," plt.grid(True)\n"," # plt.tight_layout()\n","\n"," # Set legend at the right to avoid overlapping with lines\n"," plt.legend(loc=\"center left\", bbox_to_anchor=(1.0, 0.5))\n","\n"," plt.show()"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Best F1 for Mistral-7B-v0.3-Chinese-Chat @ 0.00 shots: 0.6634078645357937\n","Best F1 for internlm2_5-7b-chat @ 0.00 shots: 0.6906357423169466\n","Best F1 for internlm2_5-7b-chat-1m @ 10.00 shots: 0.665824871588245\n","Best F1 for Qwen2-7B-Instruct @ 0.00 shots: 0.710140098232232\n","Best F1 for Llama3.1-8B-Chinese-Chat @ 0.00 shots: 0.7371050181385632\n","Best F1 for Llama3.1-70B-Chinese-Chat @ 0.00 shots: 0.7525813484548423\n","Best F1 for Qwen2-72B-Instruct @ 0.00 shots: 0.7572499605227642\n"]},{"data":{"image/png":"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model_markers)"]},{"cell_type":"code","execution_count":49,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Mistral-7B-v0.3-Chinese-Chat, Shots: 0\n","count 3000.000000\n","mean 799.354000\n","std 15.567385\n","min 694.000000\n","25% 798.000000\n","50% 802.000000\n","75% 806.000000\n","max 928.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: internlm2_5-7b-chat, Shots: 0\n","count 3000.000000\n","mean 461.917667\n","std 7.767732\n","min 426.000000\n","25% 459.000000\n","50% 461.000000\n","75% 463.000000\n","max 511.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: internlm2_5-7b-chat-1m, Shots: 0\n","count 3000.000000\n","mean 461.917667\n","std 7.767732\n","min 426.000000\n","25% 459.000000\n","50% 461.000000\n","75% 463.000000\n","max 511.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Qwen2-7B-Instruct, Shots: 0\n","count 3000.000000\n","mean 465.338667\n","std 8.617118\n","min 426.000000\n","25% 462.000000\n","50% 464.000000\n","75% 467.000000\n","max 517.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Llama3.1-8B-Chinese-Chat, Shots: 0\n","count 3000.000000\n","mean 571.091000\n","std 9.115687\n","min 512.000000\n","25% 570.000000\n","50% 571.000000\n","75% 574.000000\n","max 652.000000\n","Name: num_tokens, dtype: float64\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"95cebb50598c4e5280100c0292f32f2f","version_major":2,"version_minor":0},"text/plain":["tokenizer_config.json: 0%| | 0.00/2.51k [00:00, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"1dc31e1b64f246929a9188045678449b","version_major":2,"version_minor":0},"text/plain":["tokenization_internlm2_fast.py: 0%| | 0.00/7.80k [00:00, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"1177a403f5e5427184be86eafb0aa344","version_major":2,"version_minor":0},"text/plain":["tokenization_internlm2.py: 0%| | 0.00/8.81k [00:00, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["A new version of the following files was downloaded from https://huggingface.co/internlm/internlm2_5-20b-chat:\n","- tokenization_internlm2.py\n",". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n","A new version of the following files was downloaded from https://huggingface.co/internlm/internlm2_5-20b-chat:\n","- tokenization_internlm2_fast.py\n","- tokenization_internlm2.py\n",". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"20cd0a2f04f54e48a9f595ba318f4136","version_major":2,"version_minor":0},"text/plain":["tokenizer.model: 0%| | 0.00/1.48M [00:00, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"a6a69c9b68e646ce94d5d164d56e1abb","version_major":2,"version_minor":0},"text/plain":["special_tokens_map.json: 0%| | 0.00/713 [00:00, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["loading train/test data files\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"acf6b21fc63242989dc0caae43efec1d","version_major":2,"version_minor":0},"text/plain":["Map: 0%| | 0/25000 [00:00, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"e827aaa507e34d929add727994dbb12b","version_major":2,"version_minor":0},"text/plain":["Map: 0%| | 0/3000 [00:00, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: internlm2_5-20b-chat, Shots: 0\n","count 3000.000000\n","mean 461.917667\n","std 7.767732\n","min 426.000000\n","25% 459.000000\n","50% 461.000000\n","75% 463.000000\n","max 511.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Llama3.1-70B-Chinese-Chat, Shots: 0\n","count 3000.000000\n","mean 571.091000\n","std 9.115687\n","min 512.000000\n","25% 570.000000\n","50% 571.000000\n","75% 574.000000\n","max 652.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Qwen2-72B-Instruct, Shots: 0\n","count 3000.000000\n","mean 465.338667\n","std 8.617118\n","min 426.000000\n","25% 462.000000\n","50% 464.000000\n","75% 467.000000\n","max 517.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Mistral-7B-v0.3-Chinese-Chat, Shots: 5\n","count 3000.000000\n","mean 2444.354000\n","std 15.567385\n","min 2339.000000\n","25% 2443.000000\n","50% 2447.000000\n","75% 2451.000000\n","max 2573.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: internlm2_5-7b-chat, Shots: 5\n","count 3000.000000\n","mean 1301.917667\n","std 7.767732\n","min 1266.000000\n","25% 1299.000000\n","50% 1301.000000\n","75% 1303.000000\n","max 1351.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: internlm2_5-7b-chat-1m, Shots: 5\n","count 3000.000000\n","mean 1301.917667\n","std 7.767732\n","min 1266.000000\n","25% 1299.000000\n","50% 1301.000000\n","75% 1303.000000\n","max 1351.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Qwen2-7B-Instruct, Shots: 5\n","count 3000.000000\n","mean 1329.338667\n","std 8.617118\n","min 1290.000000\n","25% 1326.000000\n","50% 1328.000000\n","75% 1331.000000\n","max 1381.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Llama3.1-8B-Chinese-Chat, Shots: 5\n","count 3000.000000\n","mean 1737.091000\n","std 9.115687\n","min 1678.000000\n","25% 1736.000000\n","50% 1737.000000\n","75% 1740.000000\n","max 1818.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"f438280369d14963abf73ccca294b5ba","version_major":2,"version_minor":0},"text/plain":["Map: 0%| | 0/25000 [00:00, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"8ea6194cfdfa40f79c1ec36b6d3baa35","version_major":2,"version_minor":0},"text/plain":["Map: 0%| | 0/3000 [00:00, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: internlm2_5-20b-chat, Shots: 5\n","count 3000.000000\n","mean 1301.917667\n","std 7.767732\n","min 1266.000000\n","25% 1299.000000\n","50% 1301.000000\n","75% 1303.000000\n","max 1351.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Llama3.1-70B-Chinese-Chat, Shots: 5\n","count 3000.000000\n","mean 1737.091000\n","std 9.115687\n","min 1678.000000\n","25% 1736.000000\n","50% 1737.000000\n","75% 1740.000000\n","max 1818.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Qwen2-72B-Instruct, Shots: 5\n","count 3000.000000\n","mean 1329.338667\n","std 8.617118\n","min 1290.000000\n","25% 1326.000000\n","50% 1328.000000\n","75% 1331.000000\n","max 1381.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Mistral-7B-v0.3-Chinese-Chat, Shots: 10\n","count 3000.000000\n","mean 3990.354000\n","std 15.567385\n","min 3885.000000\n","25% 3989.000000\n","50% 3993.000000\n","75% 3997.000000\n","max 4119.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: internlm2_5-7b-chat, Shots: 10\n","count 3000.000000\n","mean 2195.917667\n","std 7.767732\n","min 2160.000000\n","25% 2193.000000\n","50% 2195.000000\n","75% 2197.000000\n","max 2245.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: internlm2_5-7b-chat-1m, Shots: 10\n","count 3000.000000\n","mean 2195.917667\n","std 7.767732\n","min 2160.000000\n","25% 2193.000000\n","50% 2195.000000\n","75% 2197.000000\n","max 2245.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Qwen2-7B-Instruct, Shots: 10\n","count 3000.000000\n","mean 2237.338667\n","std 8.617118\n","min 2198.000000\n","25% 2234.000000\n","50% 2236.000000\n","75% 2239.000000\n","max 2289.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Llama3.1-8B-Chinese-Chat, Shots: 10\n","count 3000.000000\n","mean 2833.091000\n","std 9.115687\n","min 2774.000000\n","25% 2832.000000\n","50% 2833.000000\n","75% 2836.000000\n","max 2914.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"9374a565fd2d4091be210b9ef704ee89","version_major":2,"version_minor":0},"text/plain":["Map: 0%| | 0/25000 [00:00, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"b70cf276a8954804af2ad9a73d378cb7","version_major":2,"version_minor":0},"text/plain":["Map: 0%| | 0/3000 [00:00, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: internlm2_5-20b-chat, Shots: 10\n","count 3000.000000\n","mean 2195.917667\n","std 7.767732\n","min 2160.000000\n","25% 2193.000000\n","50% 2195.000000\n","75% 2197.000000\n","max 2245.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Llama3.1-70B-Chinese-Chat, Shots: 10\n","count 3000.000000\n","mean 2833.091000\n","std 9.115687\n","min 2774.000000\n","25% 2832.000000\n","50% 2833.000000\n","75% 2836.000000\n","max 2914.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Qwen2-72B-Instruct, Shots: 10\n","count 3000.000000\n","mean 2237.338667\n","std 8.617118\n","min 2198.000000\n","25% 2234.000000\n","50% 2236.000000\n","75% 2239.000000\n","max 2289.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Mistral-7B-v0.3-Chinese-Chat, Shots: 20\n","count 3000.000000\n","mean 7263.354000\n","std 15.567385\n","min 7158.000000\n","25% 7262.000000\n","50% 7266.000000\n","75% 7270.000000\n","max 7392.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: internlm2_5-7b-chat, Shots: 20\n","count 3000.000000\n","mean 4015.917667\n","std 7.767732\n","min 3980.000000\n","25% 4013.000000\n","50% 4015.000000\n","75% 4017.000000\n","max 4065.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: internlm2_5-7b-chat-1m, Shots: 20\n","count 3000.000000\n","mean 4015.917667\n","std 7.767732\n","min 3980.000000\n","25% 4013.000000\n","50% 4015.000000\n","75% 4017.000000\n","max 4065.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Qwen2-7B-Instruct, Shots: 20\n","count 3000.000000\n","mean 4124.338667\n","std 8.617118\n","min 4085.000000\n","25% 4121.000000\n","50% 4123.000000\n","75% 4126.000000\n","max 4176.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Llama3.1-8B-Chinese-Chat, Shots: 20\n","count 3000.000000\n","mean 5202.091000\n","std 9.115687\n","min 5143.000000\n","25% 5201.000000\n","50% 5202.000000\n","75% 5205.000000\n","max 5283.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"9b2c63e0399e4ccd876d1a2ee42f06f3","version_major":2,"version_minor":0},"text/plain":["Map: 0%| | 0/25000 [00:00, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"3bcce7960ec14ea091e52bfaaaab9665","version_major":2,"version_minor":0},"text/plain":["Map: 0%| | 0/3000 [00:00, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: internlm2_5-20b-chat, Shots: 20\n","count 3000.000000\n","mean 4015.917667\n","std 7.767732\n","min 3980.000000\n","25% 4013.000000\n","50% 4015.000000\n","75% 4017.000000\n","max 4065.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Llama3.1-70B-Chinese-Chat, Shots: 20\n","count 3000.000000\n","mean 5202.091000\n","std 9.115687\n","min 5143.000000\n","25% 5201.000000\n","50% 5202.000000\n","75% 5205.000000\n","max 5283.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Qwen2-72B-Instruct, Shots: 20\n","count 3000.000000\n","mean 4124.338667\n","std 8.617118\n","min 4085.000000\n","25% 4121.000000\n","50% 4123.000000\n","75% 4126.000000\n","max 4176.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Mistral-7B-v0.3-Chinese-Chat, Shots: 30\n","count 3000.000000\n","mean 10675.354000\n","std 15.567385\n","min 10570.000000\n","25% 10674.000000\n","50% 10678.000000\n","75% 10682.000000\n","max 10804.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: internlm2_5-7b-chat, Shots: 30\n","count 3000.000000\n","mean 5853.917667\n","std 7.767732\n","min 5818.000000\n","25% 5851.000000\n","50% 5853.000000\n","75% 5855.000000\n","max 5903.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: internlm2_5-7b-chat-1m, Shots: 30\n","count 3000.000000\n","mean 5853.917667\n","std 7.767732\n","min 5818.000000\n","25% 5851.000000\n","50% 5853.000000\n","75% 5855.000000\n","max 5903.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Qwen2-7B-Instruct, Shots: 30\n","count 3000.000000\n","mean 6055.338667\n","std 8.617118\n","min 6016.000000\n","25% 6052.000000\n","50% 6054.000000\n","75% 6057.000000\n","max 6107.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Llama3.1-8B-Chinese-Chat, Shots: 30\n","count 3000.000000\n","mean 7687.091000\n","std 9.115687\n","min 7628.000000\n","25% 7686.000000\n","50% 7687.000000\n","75% 7690.000000\n","max 7768.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"a88779d830ba4a60a2f0c9bca0a86af9","version_major":2,"version_minor":0},"text/plain":["Map: 0%| | 0/25000 [00:00, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"1665070c6cf941a58cadca018aa04013","version_major":2,"version_minor":0},"text/plain":["Map: 0%| | 0/3000 [00:00, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: internlm2_5-20b-chat, Shots: 30\n","count 3000.000000\n","mean 5853.917667\n","std 7.767732\n","min 5818.000000\n","25% 5851.000000\n","50% 5853.000000\n","75% 5855.000000\n","max 5903.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Llama3.1-70B-Chinese-Chat, Shots: 30\n","count 3000.000000\n","mean 7687.091000\n","std 9.115687\n","min 7628.000000\n","25% 7686.000000\n","50% 7687.000000\n","75% 7690.000000\n","max 7768.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Qwen2-72B-Instruct, Shots: 30\n","count 3000.000000\n","mean 6055.338667\n","std 8.617118\n","min 6016.000000\n","25% 6052.000000\n","50% 6054.000000\n","75% 6057.000000\n","max 6107.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Mistral-7B-v0.3-Chinese-Chat, Shots: 40\n","count 3000.000000\n","mean 14023.354000\n","std 15.567385\n","min 13918.000000\n","25% 14022.000000\n","50% 14026.000000\n","75% 14030.000000\n","max 14152.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: internlm2_5-7b-chat, Shots: 40\n","count 3000.000000\n","mean 7659.917667\n","std 7.767732\n","min 7624.000000\n","25% 7657.000000\n","50% 7659.000000\n","75% 7661.000000\n","max 7709.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: internlm2_5-7b-chat-1m, Shots: 40\n","count 3000.000000\n","mean 7659.917667\n","std 7.767732\n","min 7624.000000\n","25% 7657.000000\n","50% 7659.000000\n","75% 7661.000000\n","max 7709.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Qwen2-7B-Instruct, Shots: 40\n","count 3000.000000\n","mean 7958.338667\n","std 8.617118\n","min 7919.000000\n","25% 7955.000000\n","50% 7957.000000\n","75% 7960.000000\n","max 8010.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Llama3.1-8B-Chinese-Chat, Shots: 40\n","count 3000.000000\n","mean 10136.091000\n","std 9.115687\n","min 10077.000000\n","25% 10135.000000\n","50% 10136.000000\n","75% 10139.000000\n","max 10217.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"e287c19e9ab84d008265954d5967731a","version_major":2,"version_minor":0},"text/plain":["Map: 0%| | 0/25000 [00:00, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"419a97c7cb9a402ba5955702c8b87c84","version_major":2,"version_minor":0},"text/plain":["Map: 0%| | 0/3000 [00:00, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: internlm2_5-20b-chat, Shots: 40\n","count 3000.000000\n","mean 7659.917667\n","std 7.767732\n","min 7624.000000\n","25% 7657.000000\n","50% 7659.000000\n","75% 7661.000000\n","max 7709.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Llama3.1-70B-Chinese-Chat, Shots: 40\n","count 3000.000000\n","mean 10136.091000\n","std 9.115687\n","min 10077.000000\n","25% 10135.000000\n","50% 10136.000000\n","75% 10139.000000\n","max 10217.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Qwen2-72B-Instruct, Shots: 40\n","count 3000.000000\n","mean 7958.338667\n","std 8.617118\n","min 7919.000000\n","25% 7955.000000\n","50% 7957.000000\n","75% 7960.000000\n","max 8010.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Mistral-7B-v0.3-Chinese-Chat, Shots: 50\n","count 3000.000000\n","mean 17459.354000\n","std 15.567385\n","min 17354.000000\n","25% 17458.000000\n","50% 17462.000000\n","75% 17466.000000\n","max 17588.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: internlm2_5-7b-chat, Shots: 50\n","count 3000.000000\n","mean 9511.917667\n","std 7.767732\n","min 9476.000000\n","25% 9509.000000\n","50% 9511.000000\n","75% 9513.000000\n","max 9561.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: internlm2_5-7b-chat-1m, Shots: 50\n","count 3000.000000\n","mean 9511.917667\n","std 7.767732\n","min 9476.000000\n","25% 9509.000000\n","50% 9511.000000\n","75% 9513.000000\n","max 9561.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Qwen2-7B-Instruct, Shots: 50\n","count 3000.000000\n","mean 9909.338667\n","std 8.617118\n","min 9870.000000\n","25% 9906.000000\n","50% 9908.000000\n","75% 9911.000000\n","max 9961.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Llama3.1-8B-Chinese-Chat, Shots: 50\n","count 3000.000000\n","mean 12638.091000\n","std 9.115687\n","min 12579.000000\n","25% 12637.000000\n","50% 12638.000000\n","75% 12641.000000\n","max 12719.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ef7387742ff2428b8df70f93739dbdc7","version_major":2,"version_minor":0},"text/plain":["Map: 0%| | 0/25000 [00:00, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"1e9703bf2ca24460909dc8f36ef49f28","version_major":2,"version_minor":0},"text/plain":["Map: 0%| | 0/3000 [00:00, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: internlm2_5-20b-chat, Shots: 50\n","count 3000.000000\n","mean 9511.917667\n","std 7.767732\n","min 9476.000000\n","25% 9509.000000\n","50% 9511.000000\n","75% 9513.000000\n","max 9561.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Llama3.1-70B-Chinese-Chat, Shots: 50\n","count 3000.000000\n","mean 12638.091000\n","std 9.115687\n","min 12579.000000\n","25% 12637.000000\n","50% 12638.000000\n","75% 12641.000000\n","max 12719.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n"," num_rows: 3000\n"," })\n","})\n","Model: Qwen2-72B-Instruct, Shots: 50\n","count 3000.000000\n","mean 9909.338667\n","std 8.617118\n","min 9870.000000\n","25% 9906.000000\n","50% 9908.000000\n","75% 9911.000000\n","max 9961.000000\n","Name: num_tokens, dtype: float64\n"]}],"source":["from transformers import (\n"," AutoTokenizer,\n",")\n","\n","from llm_toolkit.llm_utils import print_row_details\n","\n","model_test_dfs = {}\n","\n","for num_shots in [0, 5, 10, 20, 30, 40, 50]:\n"," for model_name in model_orders.keys():\n"," model_id = (\n"," model_perf_dfs[model_name][\"run\"].unique()[0].split(model_name)[0]\n"," + model_name\n"," )\n"," tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)\n","\n"," datasets = load_logical_reasoning_dataset(\n"," data_path,\n"," tokenizer=tokenizer,\n"," chinese_prompt=True,\n"," using_p1=False,\n"," num_shots=num_shots,\n"," )\n"," print(f\"Model: {model_name}, Shots: {num_shots}\")\n"," test_df = datasets[\"test\"].to_pandas()\n"," test_df[\"num_tokens\"] = test_df[\"prompt\"].apply(\n"," lambda x: len(tokenizer(x)[\"input_ids\"])\n"," )\n"," \n"," print(test_df[\"num_tokens\"].describe())\n","\n"," model_test_dfs[(model_name, num_shots)] = test_df"]},{"cell_type":"code","execution_count":76,"metadata":{},"outputs":[],"source":["import tiktoken\n","\n","\n","def num_tokens_from_text(text, model=\"gpt-4o\"):\n"," \"\"\"Return the number of tokens used by a list of messages.\"\"\"\n"," try:\n"," encoding = tiktoken.encoding_for_model(model)\n"," except KeyError:\n"," # print(\"Warning: model not found. Using cl100k_base encoding.\")\n"," encoding = tiktoken.get_encoding(\"cl100k_base\")\n","\n"," return len(encoding.encode(text))\n","\n","\n","def num_tokens_from_row(row, num_shots, train_dataset, model=\"gpt-4o\"):\n"," prompt = get_few_shot_prompt_template(\n"," num_shots, train_dataset\n"," )\n"," text = prompt.format(row[\"puzzle\"], row[\"truth\"], row[\"text\"])\n"," return num_tokens_from_text(text, model=model)"]},{"cell_type":"code","execution_count":77,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: gpt-4o, Shots: 0\n","count 3000.000000\n","mean 524.806333\n","std 10.057595\n","min 464.000000\n","25% 522.000000\n","50% 525.000000\n","75% 528.250000\n","max 606.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: gpt-4o-mini, Shots: 0\n","count 3000.000000\n","mean 524.806333\n","std 10.057595\n","min 464.000000\n","25% 522.000000\n","50% 525.000000\n","75% 528.250000\n","max 606.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: o1-preview, Shots: 0\n","count 3000.000000\n","mean 797.595333\n","std 16.417250\n","min 682.000000\n","25% 797.000000\n","50% 799.000000\n","75% 803.000000\n","max 925.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: o1-mini, Shots: 0\n","count 3000.000000\n","mean 797.595333\n","std 16.417250\n","min 682.000000\n","25% 797.000000\n","50% 799.000000\n","75% 803.000000\n","max 925.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: gpt-4o, Shots: 5\n","count 3000.000000\n","mean 1629.806333\n","std 10.057595\n","min 1569.000000\n","25% 1627.000000\n","50% 1630.000000\n","75% 1633.250000\n","max 1711.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: gpt-4o-mini, Shots: 5\n","count 3000.000000\n","mean 1629.806333\n","std 10.057595\n","min 1569.000000\n","25% 1627.000000\n","50% 1630.000000\n","75% 1633.250000\n","max 1711.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: o1-preview, Shots: 5\n","count 3000.000000\n","mean 2521.595333\n","std 16.417250\n","min 2406.000000\n","25% 2521.000000\n","50% 2523.000000\n","75% 2527.000000\n","max 2649.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: o1-mini, Shots: 5\n","count 3000.000000\n","mean 2521.595333\n","std 16.417250\n","min 2406.000000\n","25% 2521.000000\n","50% 2523.000000\n","75% 2527.000000\n","max 2649.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: gpt-4o, Shots: 10\n","count 3000.000000\n","mean 2699.806333\n","std 10.057595\n","min 2639.000000\n","25% 2697.000000\n","50% 2700.000000\n","75% 2703.250000\n","max 2781.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: gpt-4o-mini, Shots: 10\n","count 3000.000000\n","mean 2699.806333\n","std 10.057595\n","min 2639.000000\n","25% 2697.000000\n","50% 2700.000000\n","75% 2703.250000\n","max 2781.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: o1-preview, Shots: 10\n","count 3000.000000\n","mean 4154.595333\n","std 16.417250\n","min 4039.000000\n","25% 4154.000000\n","50% 4156.000000\n","75% 4160.000000\n","max 4282.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: o1-mini, Shots: 10\n","count 3000.000000\n","mean 4154.595333\n","std 16.417250\n","min 4039.000000\n","25% 4154.000000\n","50% 4156.000000\n","75% 4160.000000\n","max 4282.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: gpt-4o, Shots: 20\n","count 3000.000000\n","mean 5028.806333\n","std 10.057595\n","min 4968.000000\n","25% 5026.000000\n","50% 5029.000000\n","75% 5032.250000\n","max 5110.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: gpt-4o-mini, Shots: 20\n","count 3000.000000\n","mean 5028.806333\n","std 10.057595\n","min 4968.000000\n","25% 5026.000000\n","50% 5029.000000\n","75% 5032.250000\n","max 5110.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: o1-preview, Shots: 20\n","count 3000.000000\n","mean 7653.595333\n","std 16.417250\n","min 7538.000000\n","25% 7653.000000\n","50% 7655.000000\n","75% 7659.000000\n","max 7781.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: o1-mini, Shots: 20\n","count 3000.000000\n","mean 7653.595333\n","std 16.417250\n","min 7538.000000\n","25% 7653.000000\n","50% 7655.000000\n","75% 7659.000000\n","max 7781.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: gpt-4o, Shots: 30\n","count 3000.000000\n","mean 7490.806333\n","std 10.057595\n","min 7430.000000\n","25% 7488.000000\n","50% 7491.000000\n","75% 7494.250000\n","max 7572.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: gpt-4o-mini, Shots: 30\n","count 3000.000000\n","mean 7490.806333\n","std 10.057595\n","min 7430.000000\n","25% 7488.000000\n","50% 7491.000000\n","75% 7494.250000\n","max 7572.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: o1-preview, Shots: 30\n","count 3000.000000\n","mean 11331.595333\n","std 16.417250\n","min 11216.000000\n","25% 11331.000000\n","50% 11333.000000\n","75% 11337.000000\n","max 11459.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: o1-mini, Shots: 30\n","count 3000.000000\n","mean 11331.595333\n","std 16.417250\n","min 11216.000000\n","25% 11331.000000\n","50% 11333.000000\n","75% 11337.000000\n","max 11459.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: gpt-4o, Shots: 40\n","count 3000.000000\n","mean 9916.806333\n","std 10.057595\n","min 9856.000000\n","25% 9914.000000\n","50% 9917.000000\n","75% 9920.250000\n","max 9998.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: gpt-4o-mini, Shots: 40\n","count 3000.000000\n","mean 9916.806333\n","std 10.057595\n","min 9856.000000\n","25% 9914.000000\n","50% 9917.000000\n","75% 9920.250000\n","max 9998.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: o1-preview, Shots: 40\n","count 3000.000000\n","mean 14933.595333\n","std 16.417250\n","min 14818.000000\n","25% 14933.000000\n","50% 14935.000000\n","75% 14939.000000\n","max 15061.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: o1-mini, Shots: 40\n","count 3000.000000\n","mean 14933.595333\n","std 16.417250\n","min 14818.000000\n","25% 14933.000000\n","50% 14935.000000\n","75% 14939.000000\n","max 15061.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: gpt-4o, Shots: 50\n","count 3000.000000\n","mean 12394.806333\n","std 10.057595\n","min 12334.000000\n","25% 12392.000000\n","50% 12395.000000\n","75% 12398.250000\n","max 12476.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: gpt-4o-mini, Shots: 50\n","count 3000.000000\n","mean 12394.806333\n","std 10.057595\n","min 12334.000000\n","25% 12392.000000\n","50% 12395.000000\n","75% 12398.250000\n","max 12476.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: o1-preview, Shots: 50\n","count 3000.000000\n","mean 18632.595333\n","std 16.417250\n","min 18517.000000\n","25% 18632.000000\n","50% 18634.000000\n","75% 18638.000000\n","max 18760.000000\n","Name: num_tokens, dtype: float64\n","loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","Model: o1-mini, Shots: 50\n","count 3000.000000\n","mean 18632.595333\n","std 16.417250\n","min 18517.000000\n","25% 18632.000000\n","50% 18634.000000\n","75% 18638.000000\n","max 18760.000000\n","Name: num_tokens, dtype: float64\n"]}],"source":["for num_shots in [0, 5, 10, 20, 30, 40, 50]:\n"," for model_name in [\"gpt-4o\", \"gpt-4o-mini\", \"o1-preview\", \"o1-mini\"]:\n","\n"," datasets = load_logical_reasoning_dataset(\n"," data_path,\n"," )\n"," print(f\"Model: {model_name}, Shots: {num_shots}\")\n"," test_df = datasets[\"test\"].to_pandas()\n"," # print_row_details(test_df)\n"," test_df[\"num_tokens\"] = test_df.apply(\n"," lambda x: num_tokens_from_row(x, num_shots, datasets[\"train\"].to_pandas(), model=model_name), axis=1\n"," )\n"," print(test_df[\"num_tokens\"].describe())\n","\n"," model_test_dfs[(model_name, num_shots)] = test_df"]},{"cell_type":"code","execution_count":79,"metadata":{},"outputs":[{"data":{"text/html":["\n","\n","
\n"," \n"," \n"," | \n"," model_name | \n"," num_shots | \n"," max | \n"," min | \n"," mean | \n"," std | \n","
\n"," \n"," \n"," \n"," 0 | \n"," Mistral-7B-v0.3-Chinese-Chat | \n"," 0 | \n"," 928 | \n"," 694 | \n"," 799.354000 | \n"," 15.567385 | \n","
\n"," \n"," 1 | \n"," internlm2_5-7b-chat | \n"," 0 | \n"," 511 | \n"," 426 | \n"," 461.917667 | \n"," 7.767732 | \n","
\n"," \n"," 2 | \n"," internlm2_5-7b-chat-1m | \n"," 0 | \n"," 511 | \n"," 426 | \n"," 461.917667 | \n"," 7.767732 | \n","
\n"," \n"," 3 | \n"," Qwen2-7B-Instruct | \n"," 0 | \n"," 517 | \n"," 426 | \n"," 465.338667 | \n"," 8.617118 | \n","
\n"," \n"," 4 | \n"," Llama3.1-8B-Chinese-Chat | \n"," 0 | \n"," 652 | \n"," 512 | \n"," 571.091000 | \n"," 9.115687 | \n","
\n"," \n"," 5 | \n"," internlm2_5-20b-chat | \n"," 0 | \n"," 511 | \n"," 426 | \n"," 461.917667 | \n"," 7.767732 | \n","
\n"," \n"," 6 | \n"," Llama3.1-70B-Chinese-Chat | \n"," 0 | \n"," 652 | \n"," 512 | \n"," 571.091000 | \n"," 9.115687 | \n","
\n"," \n"," 7 | \n"," Qwen2-72B-Instruct | \n"," 0 | \n"," 517 | \n"," 426 | \n"," 465.338667 | \n"," 8.617118 | \n","
\n"," \n"," 8 | \n"," Mistral-7B-v0.3-Chinese-Chat | \n"," 5 | \n"," 2573 | \n"," 2339 | \n"," 2444.354000 | \n"," 15.567385 | \n","
\n"," \n"," 9 | \n"," internlm2_5-7b-chat | \n"," 5 | \n"," 1351 | \n"," 1266 | \n"," 1301.917667 | \n"," 7.767732 | \n","
\n"," \n","
\n","
"],"text/plain":[" model_name num_shots max min mean std\n","0 Mistral-7B-v0.3-Chinese-Chat 0 928 694 799.354000 15.567385\n","1 internlm2_5-7b-chat 0 511 426 461.917667 7.767732\n","2 internlm2_5-7b-chat-1m 0 511 426 461.917667 7.767732\n","3 Qwen2-7B-Instruct 0 517 426 465.338667 8.617118\n","4 Llama3.1-8B-Chinese-Chat 0 652 512 571.091000 9.115687\n","5 internlm2_5-20b-chat 0 511 426 461.917667 7.767732\n","6 Llama3.1-70B-Chinese-Chat 0 652 512 571.091000 9.115687\n","7 Qwen2-72B-Instruct 0 517 426 465.338667 8.617118\n","8 Mistral-7B-v0.3-Chinese-Chat 5 2573 2339 2444.354000 15.567385\n","9 internlm2_5-7b-chat 5 1351 1266 1301.917667 7.767732"]},"execution_count":79,"metadata":{},"output_type":"execute_result"}],"source":["df_token_counts = pd.DataFrame(\n"," model_test_dfs.keys(), columns=[\"model_name\", \"num_shots\"]\n",")\n","\n","max = []\n","min = []\n","mean = []\n","std = []\n","\n","for model_name, num_shots in model_test_dfs.keys():\n"," test_df = model_test_dfs[(model_name, num_shots)]\n"," max.append(test_df[\"num_tokens\"].max())\n"," min.append(test_df[\"num_tokens\"].min())\n"," mean.append(test_df[\"num_tokens\"].mean())\n"," std.append(test_df[\"num_tokens\"].std())\n","\n","df_token_counts[\"max\"] = max\n","df_token_counts[\"min\"] = min\n","df_token_counts[\"mean\"] = mean\n","df_token_counts[\"std\"] = std\n","\n","df_token_counts.head(10)"]},{"cell_type":"code","execution_count":98,"metadata":{},"outputs":[{"data":{"text/html":["\n","\n","
\n"," \n"," \n"," | \n"," model_name | \n"," num_shots | \n"," max | \n"," min | \n"," mean | \n"," std | \n","
\n"," \n"," \n"," \n"," 0 | \n"," Mistral-7B-v0.3-Chinese-Chat | \n"," 0 | \n"," 928 | \n"," 694 | \n"," 799.354000 | \n"," 15.567385 | \n","
\n"," \n"," 1 | \n"," internlm2_5-7b-chat | \n"," 0 | \n"," 511 | \n"," 426 | \n"," 461.917667 | \n"," 7.767732 | \n","
\n"," \n"," 2 | \n"," internlm2_5-7b-chat-1m | \n"," 0 | \n"," 511 | \n"," 426 | \n"," 461.917667 | \n"," 7.767732 | \n","
\n"," \n"," 3 | \n"," Qwen2-7B-Instruct | \n"," 0 | \n"," 517 | \n"," 426 | \n"," 465.338667 | \n"," 8.617118 | \n","
\n"," \n"," 4 | \n"," Llama3.1-8B-Chinese-Chat | \n"," 0 | \n"," 652 | \n"," 512 | \n"," 571.091000 | \n"," 9.115687 | \n","
\n"," \n"," ... | \n"," ... | \n"," ... | \n"," ... | \n"," ... | \n"," ... | \n"," ... | \n","
\n"," \n"," 79 | \n"," o1-mini | \n"," 40 | \n"," 15061 | \n"," 14818 | \n"," 14933.595333 | \n"," 16.417250 | \n","
\n"," \n"," 80 | \n"," gpt-4o | \n"," 50 | \n"," 12476 | \n"," 12334 | \n"," 12394.806333 | \n"," 10.057595 | \n","
\n"," \n"," 81 | \n"," gpt-4o-mini | \n"," 50 | \n"," 12476 | \n"," 12334 | \n"," 12394.806333 | \n"," 10.057595 | \n","
\n"," \n"," 82 | \n"," o1-preview | \n"," 50 | \n"," 18760 | \n"," 18517 | \n"," 18632.595333 | \n"," 16.417250 | \n","
\n"," \n"," 83 | \n"," o1-mini | \n"," 50 | \n"," 18760 | \n"," 18517 | \n"," 18632.595333 | \n"," 16.417250 | \n","
\n"," \n","
\n","
84 rows × 6 columns
\n","
"],"text/plain":[" model_name num_shots max min mean \\\n","0 Mistral-7B-v0.3-Chinese-Chat 0 928 694 799.354000 \n","1 internlm2_5-7b-chat 0 511 426 461.917667 \n","2 internlm2_5-7b-chat-1m 0 511 426 461.917667 \n","3 Qwen2-7B-Instruct 0 517 426 465.338667 \n","4 Llama3.1-8B-Chinese-Chat 0 652 512 571.091000 \n",".. ... ... ... ... ... \n","79 o1-mini 40 15061 14818 14933.595333 \n","80 gpt-4o 50 12476 12334 12394.806333 \n","81 gpt-4o-mini 50 12476 12334 12394.806333 \n","82 o1-preview 50 18760 18517 18632.595333 \n","83 o1-mini 50 18760 18517 18632.595333 \n","\n"," std \n","0 15.567385 \n","1 7.767732 \n","2 7.767732 \n","3 8.617118 \n","4 9.115687 \n",".. ... \n","79 16.417250 \n","80 10.057595 \n","81 10.057595 \n","82 16.417250 \n","83 16.417250 \n","\n","[84 rows x 6 columns]"]},"execution_count":98,"metadata":{},"output_type":"execute_result"}],"source":["df_token_counts"]},{"cell_type":"code","execution_count":100,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["