diff --git "a/competition/09a_InterLM_finetuning_colab_p2.ipynb" "b/competition/09a_InterLM_finetuning_colab_p2.ipynb"
new file mode 100644--- /dev/null
+++ "b/competition/09a_InterLM_finetuning_colab_p2.ipynb"
@@ -0,0 +1 @@
+{"cells":[{"cell_type":"code","source":["%load_ext autoreload\n","%autoreload 2"],"metadata":{"id":"uWKRSV6eZsCn","executionInfo":{"status":"ok","timestamp":1720711613956,"user_tz":-480,"elapsed":2,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"10de0285-c19b-4aff-b2cd-b89e259adc47"},"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["The autoreload extension is already loaded. To reload it, use:\n","  %reload_ext autoreload\n"]}]},{"cell_type":"code","execution_count":22,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"eb33b19f-1206-41ee-84e2-e6258a12eef7","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"id":"xwFh14uiZBrI","executionInfo":{"status":"ok","timestamp":1720711640125,"user_tz":-480,"elapsed":13209,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"}},"outputId":"a308789f-8ca4-46ca-b535-a662dfb45082"},"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":23,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"6d394937-6c99-4a7c-9d32-7600a280032f","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"id":"G5pNu3zgZBrL","executionInfo":{"status":"ok","timestamp":1720711640125,"user_tz":-480,"elapsed":9,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"}},"outputId":"e0f0ac3f-6bbb-4381-a527-5c679b67300f"},"outputs":[{"output_type":"stream","name":"stdout","text":["workding dir: /content/drive/MyDrive/logical-reasoning/\n"]}],"source":["import os\n","import sys\n","from pathlib import Path\n","\n","workding_dir = \"/content/drive/MyDrive/logical-reasoning/\"\n","os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"workding dir:\", workding_dir)"]},{"cell_type":"code","execution_count":24,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"ac667aba-076e-4de6-9984-8f6a67cb09cd","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"id":"0dVRAabNZBrL","executionInfo":{"status":"ok","timestamp":1720711640125,"user_tz":-480,"elapsed":7,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"}},"outputId":"d97dd29b-ab43-4fc3-8e38-571280e598de"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["False"]},"metadata":{},"execution_count":24}],"source":["need_to_setup_env = False\n","need_to_setup_env"]},{"cell_type":"code","execution_count":25,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"72f9cf79-7b0d-4d9e-90a0-1fa5251b947f","showTitle":false,"title":""},"id":"hKUOfP2HZBrL","executionInfo":{"status":"ok","timestamp":1720711640126,"user_tz":-480,"elapsed":7,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"}}},"outputs":[],"source":["if need_to_setup_env:\n","    %pip install -r requirements.txt\n","    %cd /content/\n","    %rm -rf LLaMA-Factory\n","    !git clone https://github.com/hiyouga/LLaMA-Factory.git\n","    %cd LLaMA-Factory\n","    %ls\n","    %pip install -e .[torch,bitsandbytes]"]},{"cell_type":"code","execution_count":26,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"c06c61fd-4c6f-4099-bd3b-46188ab835d7","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"id":"txOgnjwYZBrL","executionInfo":{"status":"ok","timestamp":1720711640126,"user_tz":-480,"elapsed":7,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"}},"outputId":"21f082c1-5cf8-4292-c18d-868f03fc5d8d"},"outputs":[{"output_type":"stream","name":"stdout","text":["workding dir: /content/drive/MyDrive/logical-reasoning/\n"]}],"source":["os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"workding dir:\", workding_dir)"]},{"cell_type":"code","execution_count":27,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"9f67ec60-2f24-411c-84eb-0dd664b44775","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"id":"hPCC-6m7ZBrM","executionInfo":{"status":"ok","timestamp":1720711640126,"user_tz":-480,"elapsed":7,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"}},"outputId":"96d5a6cf-988d-4f21-dc30-bfeb52d61dd1"},"outputs":[{"output_type":"stream","name":"stdout","text":["loading env vars from: /content/drive/MyDrive/logical-reasoning/.env\n"]},{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":27}],"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":28,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"f1597656-8042-4878-9d3b-9ebfb8dd86dc","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"id":"1M3IraVtZBrM","executionInfo":{"status":"ok","timestamp":1720711640126,"user_tz":-480,"elapsed":6,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"}},"outputId":"9f129c9e-7293-4c25-cca5-7ae4fdcab979"},"outputs":[{"output_type":"stream","name":"stdout","text":["internlm/internlm2_5-7b-chat-1m None True datasets/mgtv results/mgtv-results_colab_p2.csv False\n"]}],"source":["import os\n","\n","model_name = os.getenv(\"MODEL_NAME\")\n","adapter_name_or_path = os.getenv(\"ADAPTER_NAME_OR_PATH\")\n","load_in_4bit = os.getenv(\"LOAD_IN_4BIT\") == \"true\"\n","data_path = os.getenv(\"LOGICAL_REASONING_DATA_PATH\")\n","results_path = os.getenv(\"LOGICAL_REASONING_RESULTS_PATH\")\n","use_english_datasets = os.getenv(\"USE_ENGLISH_DATASETS\") == \"true\"\n","\n","print(model_name, adapter_name_or_path, load_in_4bit, data_path, results_path, use_english_datasets)"]},{"cell_type":"code","execution_count":29,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"e3ab54ba-7b6d-4817-bf2e-c5d711508b58","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"id":"mrVEz6UsZBrM","executionInfo":{"status":"ok","timestamp":1720711640126,"user_tz":-480,"elapsed":6,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"}},"outputId":"57e472ec-cdee-47d3-b1da-5a5ca3e58131"},"outputs":[{"output_type":"stream","name":"stdout","text":["Thu Jul 11 15:27:19 2024       \n","+---------------------------------------------------------------------------------------+\n","| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n","|-----------------------------------------+----------------------+----------------------+\n","| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n","| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n","|                                         |                      |               MIG M. |\n","|=========================================+======================+======================|\n","|   0  NVIDIA L4                      Off | 00000000:00:03.0 Off |                    0 |\n","| N/A   39C    P8              12W /  72W |      1MiB / 23034MiB |      0%      Default |\n","|                                         |                      |                  N/A |\n","+-----------------------------------------+----------------------+----------------------+\n","                                                                                         \n","+---------------------------------------------------------------------------------------+\n","| Processes:                                                                            |\n","|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n","|        ID   ID                                                             Usage      |\n","|=======================================================================================|\n","|  No running processes found                                                           |\n","+---------------------------------------------------------------------------------------+\n"]}],"source":["!nvidia-smi"]},{"cell_type":"code","execution_count":30,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"b2a43943-9324-4839-9a47-cfa72de2244b","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"id":"UgMvt6dIZBrM","executionInfo":{"status":"ok","timestamp":1720711640126,"user_tz":-480,"elapsed":5,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"}},"outputId":"f7790506-b806-43ca-89eb-24c7f5471bfc"},"outputs":[{"output_type":"stream","name":"stdout","text":["Python 3.10.12\n","\u001b[33mWARNING: Package(s) not found: flash-attn\u001b[0m\u001b[33m\n","\u001b[0mCPU times: user 10.1 ms, sys: 1.94 ms, total: 12 ms\n","Wall time: 610 ms\n"]}],"source":["%%time\n","!python --version\n","!pip show flash-attn"]},{"cell_type":"code","execution_count":31,"metadata":{"id":"ZuS_FsLyZBrN","executionInfo":{"status":"ok","timestamp":1720711640126,"user_tz":-480,"elapsed":4,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"}}},"outputs":[],"source":["from llm_toolkit.logical_reasoning_utils import *"]},{"cell_type":"code","execution_count":32,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":224},"id":"muFDE9DpZBrN","executionInfo":{"status":"ok","timestamp":1720711642271,"user_tz":-480,"elapsed":2149,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"}},"outputId":"00ed5651-ae27-4292-a23c-f1c9952b9197"},"outputs":[{"output_type":"stream","name":"stdout","text":["loading existing data from: llama-factory/data/alpaca_mgtv_p2.json\n"]},{"output_type":"execute_result","data":{"text/plain":["                                         instruction input output\n","0  你是一个情景猜谜游戏的主持人。游戏规则如下：\\n\\n1. 参与者会得到一个谜面，谜面会描述一...           不是\n","1  你是一个情景猜谜游戏的主持人。游戏规则如下：\\n\\n1. 参与者会得到一个谜面，谜面会描述一...           不是\n","2  你是一个情景猜谜游戏的主持人。游戏规则如下：\\n\\n1. 参与者会得到一个谜面，谜面会描述一...          不重要\n","3  你是一个情景猜谜游戏的主持人。游戏规则如下：\\n\\n1. 参与者会得到一个谜面，谜面会描述一...           不是\n","4  你是一个情景猜谜游戏的主持人。游戏规则如下：\\n\\n1. 参与者会得到一个谜面，谜面会描述一...            是"],"text/html":["\n","  <div id=\"df-7131535b-9237-4312-8fe8-e50490ee1ed3\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>instruction</th>\n","      <th>input</th>\n","      <th>output</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>你是一个情景猜谜游戏的主持人。游戏规则如下：\\n\\n1. 参与者会得到一个谜面，谜面会描述一...</td>\n","      <td></td>\n","      <td>不是</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>你是一个情景猜谜游戏的主持人。游戏规则如下：\\n\\n1. 参与者会得到一个谜面，谜面会描述一...</td>\n","      <td></td>\n","      <td>不是</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>你是一个情景猜谜游戏的主持人。游戏规则如下：\\n\\n1. 参与者会得到一个谜面，谜面会描述一...</td>\n","      <td></td>\n","      <td>不重要</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>你是一个情景猜谜游戏的主持人。游戏规则如下：\\n\\n1. 参与者会得到一个谜面，谜面会描述一...</td>\n","      <td></td>\n","      <td>不是</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>你是一个情景猜谜游戏的主持人。游戏规则如下：\\n\\n1. 参与者会得到一个谜面，谜面会描述一...</td>\n","      <td></td>\n","      <td>是</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7131535b-9237-4312-8fe8-e50490ee1ed3')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert 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\\u82e5\\u53c2\\u4e0e\\u8005\\u63d0\\u95ee\\u4e0d\\u662f\\u4e00\\u4e2a\\u5c01\\u95ed\\u5f0f\\u95ee\\u9898\\u6216\\u8005\\u95ee\\u9898\\u96be\\u4ee5\\u7406\\u89e3\\uff0c\\u56de\\u7b54\\uff1a\\u95ee\\u6cd5\\u9519\\u8bef\\n   - \\u82e5\\u53c2\\u4e0e\\u8005\\u63d0\\u95ee\\u57fa\\u672c\\u8fd8\\u539f\\u4e86\\u8c1c\\u5e95\\u771f\\u76f8\\uff0c\\u56de\\u7b54\\uff1a\\u56de\\u7b54\\u6b63\\u786e\\n5. \\u56de\\u7b54\\u4e2d\\u4e0d\\u80fd\\u6dfb\\u52a0\\u4efb\\u4f55\\u5176\\u5b83\\u4fe1\\u606f\\uff0c\\u4e5f\\u4e0d\\u80fd\\u7701\\u7565\\u9009\\u9879\\u4e2d\\u7684\\u4efb\\u4f55\\u4e00\\u4e2a\\u5b57\\u3002\\u4f8b\\u5982\\uff0c\\u4e0d\\u53ef\\u4ee5\\u628a\\u201c\\u4e0d\\u662f\\u201d\\u7701\\u7565\\u6210\\u201c\\u4e0d\\u201d\\u3002\\n\\n\\u8bf7\\u4e25\\u683c\\u6309\\u7167\\u8fd9\\u4e9b\\u89c4\\u5219\\u56de\\u7b54\\u53c2\\u4e0e\\u8005\\u63d0\\u51fa\\u7684\\u95ee\\u9898\\u3002\\n\\n**\\u8c1c\\u9762:** 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 \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"output\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"\\u4e0d\\u91cd\\u8981\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":32}],"source":["df_alpaca = load_alpaca_data(data_path, using_p1=False)\n","df_alpaca.head()"]},{"source":["# @title output\n","\n","from matplotlib import pyplot as plt\n","import seaborn as sns\n","df_alpaca.groupby('output').size().plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'))\n","plt.gca().spines[['top', 'right',]].set_visible(False)"],"cell_type":"code","execution_count":33,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/IPython/core/events.py:89: UserWarning: Glyph 19981 (\\N{CJK UNIFIED IDEOGRAPH-4E0D}) missing from current font.\n","  func(*args, **kwargs)\n","/usr/local/lib/python3.10/dist-packages/IPython/core/events.py:89: UserWarning: Glyph 26159 (\\N{CJK UNIFIED IDEOGRAPH-662F}) missing from current font.\n","  func(*args, **kwargs)\n","/usr/local/lib/python3.10/dist-packages/IPython/core/events.py:89: UserWarning: Glyph 37325 (\\N{CJK UNIFIED IDEOGRAPH-91CD}) missing from current font.\n","  func(*args, **kwargs)\n","/usr/local/lib/python3.10/dist-packages/IPython/core/events.py:89: UserWarning: Glyph 35201 (\\N{CJK UNIFIED IDEOGRAPH-8981}) missing from current font.\n","  func(*args, **kwargs)\n","/usr/local/lib/python3.10/dist-packages/IPython/core/events.py:89: UserWarning: Glyph 22238 (\\N{CJK UNIFIED IDEOGRAPH-56DE}) missing from current font.\n","  func(*args, **kwargs)\n","/usr/local/lib/python3.10/dist-packages/IPython/core/events.py:89: UserWarning: Glyph 31572 (\\N{CJK UNIFIED IDEOGRAPH-7B54}) missing from current font.\n","  func(*args, **kwargs)\n","/usr/local/lib/python3.10/dist-packages/IPython/core/events.py:89: UserWarning: Glyph 27491 (\\N{CJK UNIFIED IDEOGRAPH-6B63}) missing from current font.\n","  func(*args, **kwargs)\n","/usr/local/lib/python3.10/dist-packages/IPython/core/events.py:89: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n","  func(*args, **kwargs)\n","/usr/local/lib/python3.10/dist-packages/IPython/core/events.py:89: UserWarning: Glyph 38382 (\\N{CJK UNIFIED IDEOGRAPH-95EE}) missing from current font.\n","  func(*args, **kwargs)\n","/usr/local/lib/python3.10/dist-packages/IPython/core/events.py:89: UserWarning: Glyph 27861 (\\N{CJK UNIFIED IDEOGRAPH-6CD5}) missing from current font.\n","  func(*args, **kwargs)\n","/usr/local/lib/python3.10/dist-packages/IPython/core/events.py:89: UserWarning: Glyph 38169 (\\N{CJK UNIFIED IDEOGRAPH-9519}) missing from current font.\n","  func(*args, **kwargs)\n","/usr/local/lib/python3.10/dist-packages/IPython/core/events.py:89: UserWarning: Glyph 35823 (\\N{CJK UNIFIED IDEOGRAPH-8BEF}) missing from current font.\n","  func(*args, **kwargs)\n","/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 19981 (\\N{CJK UNIFIED IDEOGRAPH-4E0D}) missing from current font.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 26159 (\\N{CJK UNIFIED IDEOGRAPH-662F}) missing from current font.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 37325 (\\N{CJK UNIFIED IDEOGRAPH-91CD}) missing from current font.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 35201 (\\N{CJK UNIFIED IDEOGRAPH-8981}) missing from current font.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 22238 (\\N{CJK UNIFIED IDEOGRAPH-56DE}) missing from current font.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 31572 (\\N{CJK UNIFIED IDEOGRAPH-7B54}) missing from current font.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 27491 (\\N{CJK UNIFIED IDEOGRAPH-6B63}) missing from current font.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 38382 (\\N{CJK UNIFIED IDEOGRAPH-95EE}) missing from current font.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 27861 (\\N{CJK UNIFIED IDEOGRAPH-6CD5}) missing from current font.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 38169 (\\N{CJK UNIFIED IDEOGRAPH-9519}) missing from current font.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 35823 (\\N{CJK UNIFIED IDEOGRAPH-8BEF}) missing from current font.\n","  fig.canvas.print_figure(bytes_io, **kw)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}}],"metadata":{"cellView":"form","colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"_ofVQITbZiTI","executionInfo":{"status":"ok","timestamp":1720711642271,"user_tz":-480,"elapsed":8,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"}},"outputId":"9c08e305-5e54-4a62-8ed7-4bdf78e1866f"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xPVpy6WpZBrN","outputId":"b5dc593a-8da5-4dfb-e056-192d0325f299"},"outputs":[{"output_type":"stream","name":"stdout","text":["Current Directory:\n","/content/drive/MyDrive/logical-reasoning/llama-factory\n","config/internlm2_5_7b_lora_sft_bf16_p2.yaml:\n"," {\n","  \"model_name_or_path\": \"internlm/internlm2_5-7b-chat-1m\",\n","  \"stage\": \"sft\",\n","  \"do_train\": true,\n","  \"finetuning_type\": \"lora\",\n","  \"lora_target\": \"all\",\n","  \"loraplus_lr_ratio\": 16.0,\n","  \"upcast_layernorm\": true,\n","  \"dataset\": \"alpaca_mgtv_p2\",\n","  \"template\": \"chatml\",\n","  \"cutoff_len\": 1024,\n","  \"max_samples\": 5000,\n","  \"overwrite_cache\": true,\n","  \"preprocessing_num_workers\": 16,\n","  \"output_dir\": \"saves/internlm2_5_7b/lora/sft_bf16_p2\",\n","  \"logging_steps\": 100,\n","  \"save_steps\": 562,\n","  \"plot_loss\": true,\n","  \"overwrite_output_dir\": true,\n","  \"per_device_train_batch_size\": 1,\n","  \"gradient_accumulation_steps\": 8,\n","  \"learning_rate\": 0.0001,\n","  \"num_train_epochs\": 4.0,\n","  \"lr_scheduler_type\": \"cosine\",\n","  \"warmup_ratio\": 0.1,\n","  \"bf16\": true,\n","  \"ddp_timeout\": 180000000,\n","  \"val_size\": 0.1,\n","  \"per_device_eval_batch_size\": 1,\n","  \"eval_strategy\": \"steps\",\n","  \"eval_steps\": 562,\n","  \"report_to\": \"wandb\",\n","  \"run_name\": \"internlm2_5_7b\"\n","}\n","2024-07-11 15:27:28.654916: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n","2024-07-11 15:27:28.706836: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n","2024-07-11 15:27:28.706879: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n","2024-07-11 15:27:28.708441: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n","2024-07-11 15:27:28.716261: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n","To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n","2024-07-11 15:27:29.959219: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n","07/11/2024 15:27:37 - INFO - llamafactory.hparams.parser - Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: False, compute dtype: torch.bfloat16\n","tokenizer_config.json: 100% 2.51k/2.51k [00:00<00:00, 17.1MB/s]\n","tokenization_internlm2_fast.py: 100% 7.80k/7.80k [00:00<00:00, 37.5MB/s]\n","tokenization_internlm2.py: 100% 8.81k/8.81k [00:00<00:00, 42.6MB/s]\n","tokenizer.model: 100% 1.48M/1.48M [00:00<00:00, 123MB/s]\n","special_tokens_map.json: 100% 713/713 [00:00<00:00, 4.85MB/s]\n","[INFO|tokenization_utils_base.py:2108] 2024-07-11 15:27:41,196 >> loading file ./tokenizer.model from cache at /root/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/./tokenizer.model\n","[INFO|tokenization_utils_base.py:2108] 2024-07-11 15:27:41,196 >> loading file added_tokens.json from cache at None\n","[INFO|tokenization_utils_base.py:2108] 2024-07-11 15:27:41,196 >> loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/special_tokens_map.json\n","[INFO|tokenization_utils_base.py:2108] 2024-07-11 15:27:41,196 >> loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/tokenizer_config.json\n","[INFO|tokenization_utils_base.py:2108] 2024-07-11 15:27:41,196 >> loading file tokenizer.json from cache at None\n","07/11/2024 15:27:42 - INFO - llamafactory.data.template - Replace eos token: <|im_end|>\n","07/11/2024 15:27:42 - INFO - llamafactory.data.template - Add <|im_start|> to stop words.\n","07/11/2024 15:27:43 - INFO - llamafactory.data.loader - Loading dataset alpaca_mgtv_p2.json...\n","Generating train split: 25000 examples [00:00, 35807.97 examples/s]\n","/usr/local/lib/python3.10/dist-packages/multiprocess/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n","  self.pid = os.fork()\n","Converting format of dataset (num_proc=16): 100% 5000/5000 [00:00<00:00, 20104.28 examples/s]\n","Running tokenizer on dataset (num_proc=16): 100% 5000/5000 [00:02<00:00, 1757.57 examples/s]\n","input_ids:\n","[92543, 1008, 364, 60403, 68625, 77794, 62591, 63352, 68309, 69323, 60687, 60364, 60355, 68309, 69776, 68411, 60387, 402, 312, 281, 262, 69102, 60497, 60382, 89428, 63352, 60388, 60353, 63352, 60388, 60382, 69401, 68252, 87114, 70436, 68865, 82168, 60355, 364, 314, 281, 262, 74243, 68290, 63352, 60930, 60353, 63352, 60930, 60357, 63352, 68421, 69059, 60355, 364, 308, 281, 262, 69102, 60497, 68251, 73477, 68574, 74004, 60550, 68287, 89214, 61683, 88840, 73687, 60355, 364, 319, 281, 262, 68390, 68772, 68287, 60353, 74243, 60530, 68420, 74740, 68855, 68544, 72719, 68423, 68538, 60387, 60357, 60359, 68278, 60359, 82568, 60359, 68855, 69077, 60359, 60593, 60408, 69583, 60355, 60684, 68855, 60354, 69844, 68559, 68411, 60387, 364, 393, 285, 262, 61369, 63352, 81953, 63352, 60930, 91085, 70670, 69059, 60353, 68855, 60387, 60357, 68319, 68278, 364, 393, 285, 262, 61369, 63352, 81953, 63352, 60930, 68336, 68376, 68319, 80078, 60876, 61015, 60389, 70670, 69059, 60353, 68855, 60387, 82568, 364, 393, 285, 262, 61369, 69102, 60497, 73912, 79865, 74004, 60550, 68287, 68319, 68287, 70436, 68865, 60353, 68855, 60387, 60593, 60408, 69583, 364, 393, 285, 262, 61369, 69102, 60497, 73912, 68406, 71940, 60362, 63352, 60930, 73687, 60353, 68855, 60387, 68855, 69077, 364, 317, 281, 262, 68855, 60366, 68336, 68535, 68574, 69344, 68347, 60353, 71452, 81256, 68423, 68322, 78818, 60666, 60355, 69192, 60353, 73263, 60581, 60419, 68278, 60420, 81256, 60397, 60419, 60358, 60420, 60355, 402, 60836, 86910, 68374, 69776, 68855, 69102, 60497, 74743, 68287, 60355, 402, 465, 63352, 60388, 334, 465, 262, 60361, 63840, 60396, 78165, 60353, 68935, 79406, 70952, 60387, 69731, 71150, 88982, 82620, 60353, 71150, 61329, 60425, 60649, 68935, 69410, 71150, 60382, 60358, 62273, 60458, 61217, 60353, 71479, 60400, 72593, 69380, 79594, 90209, 60355, 60836, 75326, 71150, 82066, 79202, 68540, 60355, 402, 465, 63352, 60930, 334, 465, 262, 73687, 69607, 60510, 70226, 60372, 62650, 60354, 61044, 61066, 69045, 60355, 71389, 61044, 61066, 89463, 60353, 61002, 60510, 70226, 73027, 70134, 60544, 61422, 60355, 68310, 74907, 60361, 71150, 88982, 82620, 68980, 60355, 69104, 60353, 71062, 61976, 60364, 60353, 70134, 60361, 72325, 60463, 68294, 60612, 70623, 60366, 60877, 60668, 60355, 74726, 60354, 61044, 61066, 68394, 70367, 60447, 69126, 70134, 60353, 69731, 68549, 60530, 69410, 71150, 61882, 60825, 60353, 70395, 70134, 60354, 62296, 60463, 60353, 72069, 86407, 68304, 63024, 60880, 60355, 68597, 68891, 73936, 60362, 69372, 60353, 71093, 72276, 60425, 68252, 82569, 70952, 60355, 402, 465, 69102, 60497, 74743, 68287, 334, 465, 262, 61882, 68279, 60548, 60780, 61076, 364, 92542, 364, 92543, 525, 11353, 364, 68278, 92542]\n","inputs:\n","<|im_start|>user\n","你是一个情景猜谜游戏的主持人。游戏规则如下：\n","\n","1. 参与者会得到一个谜面，谜面会描述一个简单又难以理解的事件。\n","2. 主持人知道谜底，谜底是谜面的答案。\n","3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n","4. 对于每个问题，主持人将根据实际情况回答以下五个选项之一：是、不是、不重要、回答正确、问法错误。各回答的判断标准如下：\n","   - 若谜面和谜底能找到问题的答案，回答：是或者不是\n","   - 若谜面和谜底不能直接或者间接推断出问题的答案，回答：不重要\n","   - 若参与者提问不是一个封闭式问题或者问题难以理解，回答：问法错误\n","   - 若参与者提问基本还原了谜底真相，回答：回答正确\n","5. 回答中不能添加任何其它信息，也不能省略选项中的任何一个字。例如，不可以把“不是”省略成“不”。\n","\n","请严格按照这些规则回答参与者提出的问题。\n","\n","**谜面:** 在甄家村里，有一个古老的传说：每年南瓜丰收的季节，南瓜田里总有一个最大的南瓜会不翼而飞，村民们对此现象困惑不解。请找出南瓜失踪背后的原因。\n","\n","**谜底:** 真相原来与一位年迈的农夫有关。这位农夫年轻时，曾与一位美丽的姑娘相恋。他们约定在南瓜丰收的季节结婚。然而，命运弄人，姑娘在婚礼前的一场意外中离世。悲伤的农夫为了纪念心爱的姑娘，每年都会将最大的南瓜偷走，放到姑娘的墓前，以此寄托自己的哀思。这一行为延续了多年，成为了乡村里一个神秘的传说。\n","\n","**参与者提出的问题:** 偷的人信神吗\n","<|im_end|>\n","<|im_start|>assistant\n","不是<|im_end|>\n","label_ids:\n","[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 68278, 92542]\n","labels:\n","不是<|im_end|>\n","/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n","config.json: 100% 895/895 [00:00<00:00, 6.35MB/s]\n","[INFO|configuration_utils.py:733] 2024-07-11 15:27:48,939 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/config.json\n","configuration_internlm2.py: 100% 8.84k/8.84k [00:00<00:00, 39.3MB/s]\n","[INFO|configuration_utils.py:733] 2024-07-11 15:27:49,809 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/config.json\n","[INFO|configuration_utils.py:796] 2024-07-11 15:27:49,810 >> Model config InternLM2Config {\n","  \"_name_or_path\": \"internlm/internlm2_5-7b-chat-1m\",\n","  \"architectures\": [\n","    \"InternLM2ForCausalLM\"\n","  ],\n","  \"attn_implementation\": \"eager\",\n","  \"auto_map\": {\n","    \"AutoConfig\": \"internlm/internlm2_5-7b-chat-1m--configuration_internlm2.InternLM2Config\",\n","    \"AutoModel\": \"internlm/internlm2_5-7b-chat-1m--modeling_internlm2.InternLM2ForCausalLM\",\n","    \"AutoModelForCausalLM\": \"internlm/internlm2_5-7b-chat-1m--modeling_internlm2.InternLM2ForCausalLM\"\n","  },\n","  \"bias\": false,\n","  \"bos_token_id\": 1,\n","  \"eos_token_id\": 2,\n","  \"hidden_act\": \"silu\",\n","  \"hidden_size\": 4096,\n","  \"initializer_range\": 0.02,\n","  \"intermediate_size\": 14336,\n","  \"max_position_embeddings\": 262144,\n","  \"model_type\": \"internlm2\",\n","  \"num_attention_heads\": 32,\n","  \"num_hidden_layers\": 32,\n","  \"num_key_value_heads\": 8,\n","  \"pad_token_id\": 2,\n","  \"pretraining_tp\": 1,\n","  \"rms_norm_eps\": 1e-05,\n","  \"rope_scaling\": {\n","    \"factor\": 2.5,\n","    \"type\": \"dynamic\"\n","  },\n","  \"rope_theta\": 50000000,\n","  \"tie_word_embeddings\": false,\n","  \"torch_dtype\": \"bfloat16\",\n","  \"transformers_version\": \"4.41.2\",\n","  \"use_cache\": true,\n","  \"vocab_size\": 92544\n","}\n","\n","modeling_internlm2.py: 100% 80.7k/80.7k [00:00<00:00, 363kB/s]\n","model.safetensors.index.json: 100% 18.2k/18.2k [00:00<00:00, 76.9MB/s]\n","[INFO|modeling_utils.py:3474] 2024-07-11 15:27:51,751 >> loading weights file model.safetensors from cache at /root/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/model.safetensors.index.json\n","Downloading shards:   0% 0/8 [00:00<?, ?it/s]\n","model-00001-of-00008.safetensors:   0% 0.00/1.95G [00:00<?, ?B/s]\u001b[A\n","model-00001-of-00008.safetensors:   1% 10.5M/1.95G [00:00<00:43, 44.4MB/s]\u001b[A\n","model-00001-of-00008.safetensors:   3% 52.4M/1.95G [00:00<00:10, 174MB/s] \u001b[A\n","model-00001-of-00008.safetensors:   5% 94.4M/1.95G [00:00<00:07, 239MB/s]\u001b[A\n","model-00001-of-00008.safetensors:   6% 126M/1.95G [00:00<00:07, 258MB/s] 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torch.bfloat16.\n","[INFO|configuration_utils.py:962] 2024-07-11 15:28:57,307 >> Generate config GenerationConfig {\n","  \"bos_token_id\": 1,\n","  \"eos_token_id\": 2,\n","  \"pad_token_id\": 2\n","}\n","\n","Loading checkpoint shards: 100% 8/8 [00:07<00:00,  1.12it/s]\n","[INFO|modeling_utils.py:4280] 2024-07-11 15:29:04,587 >> All model checkpoint weights were used when initializing InternLM2ForCausalLM.\n","\n","[INFO|modeling_utils.py:4288] 2024-07-11 15:29:04,587 >> All the weights of InternLM2ForCausalLM were initialized from the model checkpoint at internlm/internlm2_5-7b-chat-1m.\n","If your task is similar to the task the model of the checkpoint was trained on, you can already use InternLM2ForCausalLM for predictions without further training.\n","generation_config.json: 100% 123/123 [00:00<00:00, 902kB/s]\n","[INFO|configuration_utils.py:917] 2024-07-11 15:29:05,100 >> loading configuration file generation_config.json from cache at /root/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/generation_config.json\n","[INFO|configuration_utils.py:962] 2024-07-11 15:29:05,101 >> Generate config GenerationConfig {\n","  \"bos_token_id\": 1,\n","  \"eos_token_id\": [\n","    2,\n","    92542\n","  ],\n","  \"pad_token_id\": 2\n","}\n","\n","07/11/2024 15:29:05 - INFO - llamafactory.model.model_utils.checkpointing - Upcasting layernorm weights in float32.\n","07/11/2024 15:29:05 - INFO - llamafactory.model.model_utils.checkpointing - Gradient checkpointing enabled.\n","07/11/2024 15:29:05 - INFO - llamafactory.model.model_utils.attention - Using vanilla attention implementation.\n","07/11/2024 15:29:05 - INFO - llamafactory.model.adapter - Upcasting trainable params to float32.\n","07/11/2024 15:29:05 - INFO - llamafactory.model.adapter - Fine-tuning method: LoRA\n","07/11/2024 15:29:05 - INFO - llamafactory.model.model_utils.misc - Found linear modules: wo,w3,wqkv,w2,w1\n","07/11/2024 15:29:05 - INFO - llamafactory.model.loader - trainable params: 18,874,368 || all params: 7,756,582,912 || trainable%: 0.2433\n","[INFO|trainer.py:641] 2024-07-11 15:29:05,979 >> Using auto half precision backend\n","07/11/2024 15:29:07 - INFO - llamafactory.train.trainer_utils - Using LoRA+ optimizer with loraplus lr ratio 16.00.\n","[INFO|trainer.py:2078] 2024-07-11 15:29:07,222 >> ***** Running training *****\n","[INFO|trainer.py:2079] 2024-07-11 15:29:07,222 >>   Num examples = 4,500\n","[INFO|trainer.py:2080] 2024-07-11 15:29:07,222 >>   Num Epochs = 4\n","[INFO|trainer.py:2081] 2024-07-11 15:29:07,222 >>   Instantaneous batch size per device = 1\n","[INFO|trainer.py:2084] 2024-07-11 15:29:07,222 >>   Total train batch size (w. parallel, distributed & accumulation) = 8\n","[INFO|trainer.py:2085] 2024-07-11 15:29:07,222 >>   Gradient Accumulation steps = 8\n","[INFO|trainer.py:2086] 2024-07-11 15:29:07,222 >>   Total optimization steps = 2,248\n","[INFO|trainer.py:2087] 2024-07-11 15:29:07,226 >>   Number of trainable parameters = 18,874,368\n","[INFO|integration_utils.py:723] 2024-07-11 15:29:07,230 >> Automatic Weights & Biases logging enabled, to disable set os.environ[\"WANDB_DISABLED\"] = \"true\"\n","\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33minflaton-sg\u001b[0m (\u001b[33minflaton-ai\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n","\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.17.4\n","\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/content/drive/MyDrive/logical-reasoning/llama-factory/wandb/run-20240711_152908-qhb0out3\u001b[0m\n","\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n","\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33minternlm2_5_7b\u001b[0m\n","\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/inflaton-ai/huggingface\u001b[0m\n","\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/inflaton-ai/huggingface/runs/qhb0out3\u001b[0m\n","{'loss': 0.4683, 'grad_norm': 3.4465599060058594, 'learning_rate': 4.4444444444444447e-05, 'epoch': 0.18}\n","  8% 174/2248 [17:06<3:24:19,  5.91s/it]"]}],"source":["%%time\n","\n","!sh ./scripts/tune-lf.sh config/internlm2_5_7b_lora_sft_bf16_p2.yaml"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"L370pvGTZBrN"},"outputs":[],"source":["def evaluate_model_all_epochs(model_name, adapter_path_base, num_train_epochs, start_epoch=0, load_in_4bit=True, num_of_entries=-1):\n","    os.environ[\"MODEL_NAME\"] = model_name\n","    os.environ[\"LOAD_IN_4BIT\"] = \"true\" if load_in_4bit else \"false\"\n","    for i in range(start_epoch, num_train_epochs + 1):\n","        print(f\"Epoch {i}\")\n","        if i == 0:\n","            os.unsetenv(\"ADAPTER_NAME_OR_PATH\")\n","        else:\n","            adapter_path = f\"{adapter_path_base}/checkpoint-{562 * i}\"\n","            os.environ[\"ADAPTER_NAME_OR_PATH\"] = adapter_path\n","\n","        !python llm_toolkit/eval_logical_reasoning.py {num_of_entries}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WUFjhxmiZBrN"},"outputs":[],"source":["%%time\n","\n","evaluate_model_all_epochs(\"internlm/internlm2_5-7b-chat-1m\", \"llama-factory/saves/internlm2_5_7b/lora/sft_bf16_p2\", 4, start_epoch=0, load_in_4bit=False, num_of_entries=-1)"]}],"metadata":{"accelerator":"GPU","application/vnd.databricks.v1+notebook":{"dashboards":[],"environmentMetadata":null,"language":"python","notebookMetadata":{"mostRecentlyExecutedCommandWithImplicitDF":{"commandId":-1,"dataframes":["_sqldf"]},"pythonIndentUnit":4},"notebookName":"10_eval-lf-medium-py3.11","widgets":{}},"colab":{"gpuType":"L4","provenance":[]},"kernelspec":{"display_name":"Python 3","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":"3.11.9"}},"nbformat":4,"nbformat_minor":0}
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