diff --git "a/competition/08_InterLM_finetuning_NV4080.ipynb" "b/competition/08_InterLM_finetuning_NV4080.ipynb"
new file mode 100644--- /dev/null
+++ "b/competition/08_InterLM_finetuning_NV4080.ipynb"
@@ -0,0 +1,7803 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "eb33b19f-1206-41ee-84e2-e6258a12eef7",
+     "showTitle": false,
+     "title": ""
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.11.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "if 'dbutils' in locals():\n",
+    "    dbutils.library.restartPython()\n",
+    "\n",
+    "!python --version"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "0ea8b46b-839b-445b-8043-ccdf4e920ace",
+     "showTitle": false,
+     "title": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "6d394937-6c99-4a7c-9d32-7600a280032f",
+     "showTitle": false,
+     "title": ""
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "workding dir: /home/inflaton/code/projects/courses/logical-reasoning\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "from pathlib import Path\n",
+    "\n",
+    "workding_dir = str(Path.cwd().parent)\n",
+    "os.chdir(workding_dir)\n",
+    "sys.path.append(workding_dir)\n",
+    "print(\"workding dir:\", workding_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "ac667aba-076e-4de6-9984-8f6a67cb09cd",
+     "showTitle": false,
+     "title": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "need_to_setup_env = False\n",
+    "need_to_setup_env"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "72f9cf79-7b0d-4d9e-90a0-1fa5251b947f",
+     "showTitle": false,
+     "title": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "if need_to_setup_env:\n",
+    "    %pip config set global.index-url https://artifacts.forge.mastercard.com/artifactory/api/pypi/python/simple\n",
+    "    %pip install tf-keras\n",
+    "    %pip install -q --upgrade accelerate einops xformers torchvision\n",
+    "    %pip install -r requirements.txt\n",
+    "    !cd ../LLaMA-Factory && pip install -e .[torch,bitsandbytes] && FLASH_ATTENTION_FORCE_BUILD=TRUE pip install --upgrade flash-attn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "c06c61fd-4c6f-4099-bd3b-46188ab835d7",
+     "showTitle": false,
+     "title": ""
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "workding dir: /home/inflaton/code/projects/courses/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": 7,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "9f67ec60-2f24-411c-84eb-0dd664b44775",
+     "showTitle": false,
+     "title": ""
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loading env vars from: /home/inflaton/code/projects/courses/logical-reasoning/.env\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 7,
+     "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": 8,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "f1597656-8042-4878-9d3b-9ebfb8dd86dc",
+     "showTitle": false,
+     "title": ""
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "internlm/internlm2_5-7b-chat-1m None True datasets/mgtv results/results/mgtv-results_temp.csv\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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "e3ab54ba-7b6d-4817-bf2e-c5d711508b58",
+     "showTitle": false,
+     "title": ""
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wed Jul 10 07:10:55 2024       \n",
+      "+---------------------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 545.23.07              Driver Version: 546.12       CUDA Version: 12.3     |\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 GeForce RTX 4080 ...    On  | 00000000:01:00.0 Off |                  N/A |\n",
+      "| N/A   50C    P0              30W / 150W |      0MiB / 12282MiB |      6%      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": 10,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "b2a43943-9324-4839-9a47-cfa72de2244b",
+     "showTitle": false,
+     "title": ""
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.11.9\n",
+      "\u001b[33mWARNING: Package(s) not found: flash-attn\u001b[0m\u001b[33m\n",
+      "\u001b[0mCPU times: user 4.61 ms, sys: 7.63 ms, total: 12.2 ms\n",
+      "Wall time: 636 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "!python --version\n",
+    "!pip show flash-attn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loading /home/inflaton/code/projects/courses/logical-reasoning/llm_toolkit/logical_reasoning_utils.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "from llm_toolkit.logical_reasoning_utils import *\n",
+    "from llm_toolkit.llm_utils import *\n",
+    "\n",
+    "def load_data(alpaca_data_path=\"llama-factory/data/alpaca_mgtv_p1.json\"):\n",
+    "    if os.path.exists(alpaca_data_path):\n",
+    "        print(\"loading existing data from:\", alpaca_data_path)\n",
+    "        data = pd.read_json(alpaca_data_path, orient=\"records\", lines=False)\n",
+    "        return data\n",
+    "\n",
+    "    print(\"loading new data from:\", alpaca_data_path)\n",
+    "    datasets = load_logical_reasoning_dataset(\n",
+    "        data_path, chinese_prompt=not use_english_datasets\n",
+    "    )\n",
+    "    df_train = datasets[\"train\"].to_pandas()\n",
+    "    df_train[\"instruction\"] = df_train.apply(\n",
+    "        lambda x: P1.format(x[\"puzzle\"], x[\"truth\"], x[\"text\"]), axis=1\n",
+    "    )\n",
+    "\n",
+    "    df_alpaca = pd.DataFrame(\n",
+    "        {\"instruction\": [\"\"] * len(df_train), \"input\": [\"\"] * len(df_train)}\n",
+    "    )\n",
+    "    df_alpaca[\"instruction\"] = df_train[\"instruction\"]\n",
+    "    df_alpaca[\"output\"] = df_train[\"label\"]\n",
+    "    df_alpaca.to_json(alpaca_data_path, orient=\"records\", lines=False, indent=2)\n",
+    "    return df_alpaca"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loading existing data from: llama-factory/data/alpaca_mgtv_p1.json\n",
+      "--------------------------------------------------\n",
+      "instruction: 你是一个逻辑游戏的主持人。游戏规则如下：\n",
+      "\n",
+      "1. 参与者会得到一个谜题。\n",
+      "2. 参与者可以通过提问来获取线索，尝试解开谜题。\n",
+      "3. 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。\n",
+      "4. 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",
+      "5. 参与者需要根据回答来推理，并最终找出谜题的正确答案。\n",
+      "\n",
+      "请严格按照这些规则回答参与者提出的问题。\n",
+      "\n",
+      "谜题: 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",
+      "实际情况: 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",
+      "\n",
+      "--------------------------------------------------\n",
+      "input: \n",
+      "--------------------------------------------------\n",
+      "output: 不是\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_alpaca = load_data()\n",
+    "print_row_details(df_alpaca)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current Directory:\n",
+      "/home/inflaton/code/projects/courses/logical-reasoning/llama-factory\n",
+      "config/internlm2_5_7b_lora_sft_4bit.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",
+      "  \"quantization_bit\": 4,\n",
+      "  \"loraplus_lr_ratio\": 16.0,\n",
+      "  \"upcast_layernorm\": true,\n",
+      "  \"dataset\": \"alpaca_mgtv_p1\",\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\",\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\": 6.0,\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\": \"none\",\n",
+      "  \"run_name\": \"internlm2_5_7b\"\n",
+      "}\n",
+      "07/10/2024 07:11:11 - INFO - llamafactory.hparams.parser - Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: False, compute dtype: torch.bfloat16\n",
+      "[INFO|tokenization_utils_base.py:2161] 2024-07-10 07:11:13,981 >> loading file ./tokenizer.model from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/./tokenizer.model\n",
+      "[INFO|tokenization_utils_base.py:2161] 2024-07-10 07:11:13,981 >> loading file added_tokens.json from cache at None\n",
+      "[INFO|tokenization_utils_base.py:2161] 2024-07-10 07:11:13,981 >> loading file special_tokens_map.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/special_tokens_map.json\n",
+      "[INFO|tokenization_utils_base.py:2161] 2024-07-10 07:11:13,981 >> loading file tokenizer_config.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/tokenizer_config.json\n",
+      "[INFO|tokenization_utils_base.py:2161] 2024-07-10 07:11:13,981 >> loading file tokenizer.json from cache at None\n",
+      "07/10/2024 07:11:15 - INFO - llamafactory.data.template - Replace eos token: <|im_end|>\n",
+      "07/10/2024 07:11:15 - INFO - llamafactory.data.template - Add <|im_start|> to stop words.\n",
+      "07/10/2024 07:11:15 - INFO - llamafactory.data.loader - Loading dataset alpaca_mgtv_p1.json...\n",
+      "Converting format of dataset (num_proc=16): 100%|█| 5000/5000 [00:00<00:00, 1401\n",
+      "Running tokenizer on dataset (num_proc=16): 100%|█| 5000/5000 [00:01<00:00, 3231\n",
+      "input_ids:\n",
+      "[92543, 1008, 364, 60403, 68625, 70503, 68309, 69323, 60687, 60364, 60355, 68309, 69776, 68411, 60387, 402, 312, 281, 262, 69102, 60497, 60382, 89428, 63352, 60582, 60355, 364, 314, 281, 262, 69102, 60497, 70357, 73912, 60383, 69464, 75687, 60353, 69702, 84931, 63352, 60582, 60355, 364, 308, 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, 364, 319, 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, 364, 317, 281, 262, 69102, 60497, 68266, 68420, 68855, 60383, 76681, 60353, 60573, 68985, 75326, 63352, 80931, 69077, 69059, 60355, 402, 60836, 86910, 68374, 69776, 68855, 69102, 60497, 74743, 68287, 60355, 402, 63352, 60582, 334, 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, 74740, 334, 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, 69102, 60497, 74743, 68287, 334, 262, 61882, 68279, 60548, 60780, 61076, 364, 92542, 364, 92543, 525, 11353, 364, 68278, 92542]\n",
+      "inputs:\n",
+      "<|im_start|>user\n",
+      "你是一个逻辑游戏的主持人。游戏规则如下：\n",
+      "\n",
+      "1. 参与者会得到一个谜题。\n",
+      "2. 参与者可以通过提问来获取线索，尝试解开谜题。\n",
+      "3. 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。\n",
+      "4. 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",
+      "5. 参与者需要根据回答来推理，并最终找出谜题的正确答案。\n",
+      "\n",
+      "请严格按照这些规则回答参与者提出的问题。\n",
+      "\n",
+      "谜题: 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",
+      "实际情况: 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, 68278, 92542]\n",
+      "labels:\n",
+      "不是<|im_end|>\n",
+      "[INFO|configuration_utils.py:733] 2024-07-10 07:11:19,307 >> loading configuration file config.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/config.json\n",
+      "[INFO|configuration_utils.py:733] 2024-07-10 07:11:20,449 >> loading configuration file config.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/config.json\n",
+      "[INFO|configuration_utils.py:800] 2024-07-10 07:11:20,449 >> 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\": \"4.42.3\",\n",
+      "  \"use_cache\": true,\n",
+      "  \"vocab_size\": 92544\n",
+      "}\n",
+      "\n",
+      "07/10/2024 07:11:20 - INFO - llamafactory.model.model_utils.quantization - Quantizing model to 4 bit with bitsandbytes.\n",
+      "[INFO|modeling_utils.py:3556] 2024-07-10 07:11:21,272 >> loading weights file model.safetensors from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/model.safetensors.index.json\n",
+      "[INFO|modeling_utils.py:1531] 2024-07-10 07:11:21,521 >> Instantiating InternLM2ForCausalLM model under default dtype torch.bfloat16.\n",
+      "[INFO|configuration_utils.py:1000] 2024-07-10 07:11:21,522 >> 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 [14:13<00:00, 106.68s/it]\n",
+      "[INFO|modeling_utils.py:4364] 2024-07-10 07:25:35,234 >> All model checkpoint weights were used when initializing InternLM2ForCausalLM.\n",
+      "\n",
+      "[INFO|modeling_utils.py:4372] 2024-07-10 07:25:35,234 >> 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",
+      "[INFO|configuration_utils.py:955] 2024-07-10 07:25:36,115 >> loading configuration file generation_config.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/generation_config.json\n",
+      "[INFO|configuration_utils.py:1000] 2024-07-10 07:25:36,115 >> 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",
+      "07/10/2024 07:25:36 - INFO - llamafactory.model.model_utils.checkpointing - Upcasting layernorm weights in float32.\n",
+      "07/10/2024 07:25:36 - INFO - llamafactory.model.model_utils.checkpointing - Gradient checkpointing enabled.\n",
+      "07/10/2024 07:25:36 - INFO - llamafactory.model.model_utils.attention - Using vanilla attention implementation.\n",
+      "07/10/2024 07:25:36 - INFO - llamafactory.model.adapter - Upcasting trainable params to float32.\n",
+      "07/10/2024 07:25:36 - INFO - llamafactory.model.adapter - Fine-tuning method: LoRA\n",
+      "07/10/2024 07:25:36 - INFO - llamafactory.model.model_utils.misc - Found linear modules: w3,wo,w1,wqkv,w2\n",
+      "07/10/2024 07:25:36 - INFO - llamafactory.model.loader - trainable params: 18,874,368 || all params: 7,756,582,912 || trainable%: 0.2433\n",
+      "[INFO|trainer.py:642] 2024-07-10 07:25:36,803 >> Using auto half precision backend\n",
+      "07/10/2024 07:25:36 - WARNING - llamafactory.train.callbacks - Previous trainer log in this folder will be deleted.\n",
+      "07/10/2024 07:25:36 - INFO - llamafactory.train.trainer_utils - Using LoRA+ optimizer with loraplus lr ratio 16.00.\n",
+      "[INFO|trainer.py:2128] 2024-07-10 07:25:36,937 >> ***** Running training *****\n",
+      "[INFO|trainer.py:2129] 2024-07-10 07:25:36,937 >>   Num examples = 4,500\n",
+      "[INFO|trainer.py:2130] 2024-07-10 07:25:36,937 >>   Num Epochs = 6\n",
+      "[INFO|trainer.py:2131] 2024-07-10 07:25:36,937 >>   Instantaneous batch size per device = 1\n",
+      "[INFO|trainer.py:2134] 2024-07-10 07:25:36,938 >>   Total train batch size (w. parallel, distributed & accumulation) = 8\n",
+      "[INFO|trainer.py:2135] 2024-07-10 07:25:36,938 >>   Gradient Accumulation steps = 8\n",
+      "[INFO|trainer.py:2136] 2024-07-10 07:25:36,938 >>   Total optimization steps = 3,372\n",
+      "[INFO|trainer.py:2137] 2024-07-10 07:25:36,939 >>   Number of trainable parameters = 18,874,368\n",
+      "{'loss': 0.4641, 'grad_norm': 4.040336608886719, 'learning_rate': 2.958579881656805e-05, 'epoch': 0.18}\n",
+      "{'loss': 0.3704, 'grad_norm': 3.9950191974639893, 'learning_rate': 5.91715976331361e-05, 'epoch': 0.36}\n",
+      "{'loss': 0.3751, 'grad_norm': 3.9038829803466797, 'learning_rate': 8.875739644970414e-05, 'epoch': 0.53}\n",
+      "{'loss': 0.3928, 'grad_norm': 3.4998044967651367, 'learning_rate': 9.989699867437137e-05, 'epoch': 0.71}\n",
+      "{'loss': 0.3497, 'grad_norm': 9.73261833190918, 'learning_rate': 9.92981892269398e-05, 'epoch': 0.89}\n",
+      " 17%|█████▊                             | 562/3372 [2:19:51<11:40:35, 14.96s/it][INFO|trainer.py:3788] 2024-07-10 09:45:28,548 >> \n",
+      "***** Running Evaluation *****\n",
+      "[INFO|trainer.py:3790] 2024-07-10 09:45:28,548 >>   Num examples = 500\n",
+      "[INFO|trainer.py:3793] 2024-07-10 09:45:28,548 >>   Batch size = 1\n",
+      "\n",
+      "  0%|                                                   | 0/500 [00:00<?, ?it/s]\u001b[A\n",
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+      "                                                                                \u001b[A\n",
+      "\u001b[A{'eval_loss': 0.6006748080253601, 'eval_runtime': 411.5999, 'eval_samples_per_second': 1.215, 'eval_steps_per_second': 1.215, 'epoch': 1.0}\n",
+      " 17%|█████▊                             | 562/3372 [2:26:43<11:40:35, 14.96s/it]\n",
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+      "                                                                                \u001b[A[INFO|trainer.py:3478] 2024-07-10 09:52:20,145 >> Saving model checkpoint to saves/internlm2_5_7b/lora/sft/checkpoint-562\n",
+      "[INFO|configuration_utils.py:733] 2024-07-10 09:52:21,618 >> loading configuration file config.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/config.json\n",
+      "[INFO|configuration_utils.py:800] 2024-07-10 09:52:21,620 >> Model config InternLM2Config {\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\": \"4.42.3\",\n",
+      "  \"use_cache\": true,\n",
+      "  \"vocab_size\": 92544\n",
+      "}\n",
+      "\n",
+      "[INFO|tokenization_utils_base.py:2574] 2024-07-10 09:52:22,273 >> tokenizer config file saved in saves/internlm2_5_7b/lora/sft/checkpoint-562/tokenizer_config.json\n",
+      "[INFO|tokenization_utils_base.py:2583] 2024-07-10 09:52:22,274 >> Special tokens file saved in saves/internlm2_5_7b/lora/sft/checkpoint-562/special_tokens_map.json\n",
+      "{'loss': 0.3383, 'grad_norm': 2.8831710815429688, 'learning_rate': 9.817128546774103e-05, 'epoch': 1.07}\n",
+      "{'loss': 0.3167, 'grad_norm': 6.632827281951904, 'learning_rate': 9.652835906663704e-05, 'epoch': 1.24}\n",
+      "{'loss': 0.3165, 'grad_norm': 6.977548122406006, 'learning_rate': 9.438700945477697e-05, 'epoch': 1.42}\n",
+      "{'loss': 0.2927, 'grad_norm': 10.037060737609863, 'learning_rate': 9.177017529516772e-05, 'epoch': 1.6}\n",
+      "{'loss': 0.3062, 'grad_norm': 6.976019859313965, 'learning_rate': 8.870588875808164e-05, 'epoch': 1.78}\n",
+      "{'loss': 0.2678, 'grad_norm': 2.106227159500122, 'learning_rate': 8.522697523356319e-05, 'epoch': 1.96}\n",
+      " 33%|███████████▋                       | 1124/3372 [4:47:58<9:28:17, 15.17s/it][INFO|trainer.py:3788] 2024-07-10 12:13:35,932 >> \n",
+      "***** Running Evaluation *****\n",
+      "[INFO|trainer.py:3790] 2024-07-10 12:13:35,933 >>   Num examples = 500\n",
+      "[INFO|trainer.py:3793] 2024-07-10 12:13:35,933 >>   Batch size = 1\n",
+      "\n",
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+      "                                                                                \u001b[A\n",
+      "\u001b[A{'eval_loss': 0.3569962680339813, 'eval_runtime': 367.3155, 'eval_samples_per_second': 1.361, 'eval_steps_per_second': 1.361, 'epoch': 2.0}\n",
+      " 33%|███████████▋                       | 1124/3372 [4:54:06<9:28:17, 15.17s/it]\n",
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+      "                                                                                \u001b[A[INFO|trainer.py:3478] 2024-07-10 12:19:43,245 >> Saving model checkpoint to saves/internlm2_5_7b/lora/sft/checkpoint-1124\n",
+      "[INFO|configuration_utils.py:733] 2024-07-10 12:19:44,485 >> loading configuration file config.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/config.json\n",
+      "[INFO|configuration_utils.py:800] 2024-07-10 12:19:44,486 >> Model config InternLM2Config {\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\": \"4.42.3\",\n",
+      "  \"use_cache\": true,\n",
+      "  \"vocab_size\": 92544\n",
+      "}\n",
+      "\n",
+      "[INFO|tokenization_utils_base.py:2574] 2024-07-10 12:19:44,925 >> tokenizer config file saved in saves/internlm2_5_7b/lora/sft/checkpoint-1124/tokenizer_config.json\n",
+      "[INFO|tokenization_utils_base.py:2583] 2024-07-10 12:19:44,925 >> Special tokens file saved in saves/internlm2_5_7b/lora/sft/checkpoint-1124/special_tokens_map.json\n",
+      "{'loss': 0.2198, 'grad_norm': 2.639159679412842, 'learning_rate': 8.137070169778812e-05, 'epoch': 2.13}\n",
+      "{'loss': 0.2412, 'grad_norm': 3.6480841636657715, 'learning_rate': 7.717837750006106e-05, 'epoch': 2.31}\n",
+      "{'loss': 0.1952, 'grad_norm': 2.240994930267334, 'learning_rate': 7.269491184691924e-05, 'epoch': 2.49}\n",
+      "{'loss': 0.2262, 'grad_norm': 13.413070678710938, 'learning_rate': 6.79683327236813e-05, 'epoch': 2.67}\n",
+      "{'loss': 0.1949, 'grad_norm': 0.8566445112228394, 'learning_rate': 6.304927240687181e-05, 'epoch': 2.84}\n",
+      " 50%|█████████████████▌                 | 1686/3372 [7:16:27<7:04:08, 15.09s/it][INFO|trainer.py:3788] 2024-07-10 14:42:04,180 >> \n",
+      "***** Running Evaluation *****\n",
+      "[INFO|trainer.py:3790] 2024-07-10 14:42:04,180 >>   Num examples = 500\n",
+      "[INFO|trainer.py:3793] 2024-07-10 14:42:04,181 >>   Batch size = 1\n",
+      "\n",
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+      "                                                                                \u001b[A\n",
+      "\u001b[A{'eval_loss': 0.42686474323272705, 'eval_runtime': 365.7769, 'eval_samples_per_second': 1.367, 'eval_steps_per_second': 1.367, 'epoch': 3.0}\n",
+      " 50%|█████████████████▌                 | 1686/3372 [7:22:33<7:04:08, 15.09s/it]\n",
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+      "                                                                                \u001b[A[INFO|trainer.py:3478] 2024-07-10 14:48:09,956 >> Saving model checkpoint to saves/internlm2_5_7b/lora/sft/checkpoint-1686\n",
+      "[INFO|configuration_utils.py:733] 2024-07-10 14:48:11,059 >> loading configuration file config.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/config.json\n",
+      "[INFO|configuration_utils.py:800] 2024-07-10 14:48:11,061 >> Model config InternLM2Config {\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\": \"4.42.3\",\n",
+      "  \"use_cache\": true,\n",
+      "  \"vocab_size\": 92544\n",
+      "}\n",
+      "\n",
+      "[INFO|tokenization_utils_base.py:2574] 2024-07-10 14:48:11,454 >> tokenizer config file saved in saves/internlm2_5_7b/lora/sft/checkpoint-1686/tokenizer_config.json\n",
+      "[INFO|tokenization_utils_base.py:2583] 2024-07-10 14:48:11,455 >> Special tokens file saved in saves/internlm2_5_7b/lora/sft/checkpoint-1686/special_tokens_map.json\n",
+      "{'loss': 0.2061, 'grad_norm': 0.11057106405496597, 'learning_rate': 5.799042507883874e-05, 'epoch': 3.02}\n",
+      "{'loss': 0.1207, 'grad_norm': 4.739655017852783, 'learning_rate': 5.284598235472912e-05, 'epoch': 3.2}\n",
+      "{'loss': 0.1683, 'grad_norm': 0.25179940462112427, 'learning_rate': 4.7671052768596945e-05, 'epoch': 3.38}\n",
+      "{'loss': 0.1325, 'grad_norm': 0.13251110911369324, 'learning_rate': 4.2521071437250546e-05, 'epoch': 3.56}\n",
+      "{'loss': 0.1328, 'grad_norm': 2.5385780334472656, 'learning_rate': 3.7451206225665035e-05, 'epoch': 3.73}\n",
+      "{'loss': 0.1184, 'grad_norm': 0.5059184432029724, 'learning_rate': 3.251576677526236e-05, 'epoch': 3.91}\n",
+      " 67%|███████████████████████▎           | 2248/3372 [9:45:02<4:41:49, 15.04s/it][INFO|trainer.py:3788] 2024-07-10 17:10:39,185 >> \n",
+      "***** Running Evaluation *****\n",
+      "[INFO|trainer.py:3790] 2024-07-10 17:10:39,185 >>   Num examples = 500\n",
+      "[INFO|trainer.py:3793] 2024-07-10 17:10:39,185 >>   Batch size = 1\n",
+      "\n",
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+      "                                                                                \u001b[A\n",
+      "\u001b[A{'eval_loss': 0.448859840631485, 'eval_runtime': 364.0973, 'eval_samples_per_second': 1.373, 'eval_steps_per_second': 1.373, 'epoch': 4.0}\n",
+      " 67%|███████████████████████▎           | 2248/3372 [9:51:06<4:41:49, 15.04s/it]\n",
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+      "                                                                                \u001b[A[INFO|trainer.py:3478] 2024-07-10 17:16:43,283 >> Saving model checkpoint to saves/internlm2_5_7b/lora/sft/checkpoint-2248\n",
+      "[INFO|configuration_utils.py:733] 2024-07-10 17:16:44,412 >> loading configuration file config.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/config.json\n",
+      "[INFO|configuration_utils.py:800] 2024-07-10 17:16:44,413 >> Model config InternLM2Config {\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\": \"4.42.3\",\n",
+      "  \"use_cache\": true,\n",
+      "  \"vocab_size\": 92544\n",
+      "}\n",
+      "\n",
+      "[INFO|tokenization_utils_base.py:2574] 2024-07-10 17:16:44,737 >> tokenizer config file saved in saves/internlm2_5_7b/lora/sft/checkpoint-2248/tokenizer_config.json\n",
+      "[INFO|tokenization_utils_base.py:2583] 2024-07-10 17:16:44,737 >> Special tokens file saved in saves/internlm2_5_7b/lora/sft/checkpoint-2248/special_tokens_map.json\n",
+      "{'loss': 0.1099, 'grad_norm': 10.636441230773926, 'learning_rate': 2.776762272569255e-05, 'epoch': 4.09}\n",
+      "{'loss': 0.0686, 'grad_norm': 0.021777508780360222, 'learning_rate': 2.325763736226793e-05, 'epoch': 4.27}\n",
+      "{'loss': 0.0548, 'grad_norm': 0.0820293128490448, 'learning_rate': 1.9034122755957523e-05, 'epoch': 4.44}\n",
+      "{'loss': 0.0788, 'grad_norm': 0.9096071720123291, 'learning_rate': 1.5142322232616412e-05, 'epoch': 4.62}\n",
+      "{'loss': 0.0496, 'grad_norm': 0.8319850564002991, 'learning_rate': 1.1623925715366247e-05, 'epoch': 4.8}\n",
+      "{'loss': 0.0684, 'grad_norm': 0.6420482397079468, 'learning_rate': 8.516623131898871e-06, 'epoch': 4.98}\n",
+      " 83%|████████████████████████████▎     | 2810/3372 [12:15:15<2:19:23, 14.88s/it][INFO|trainer.py:3788] 2024-07-10 19:40:52,836 >> \n",
+      "***** Running Evaluation *****\n",
+      "[INFO|trainer.py:3790] 2024-07-10 19:40:52,836 >>   Num examples = 500\n",
+      "[INFO|trainer.py:3793] 2024-07-10 19:40:52,836 >>   Batch size = 1\n",
+      "\n",
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+      "                                                                                \u001b[A\n",
+      "\u001b[A{'eval_loss': 0.6155796051025391, 'eval_runtime': 363.8165, 'eval_samples_per_second': 1.374, 'eval_steps_per_second': 1.374, 'epoch': 5.0}\n",
+      " 83%|████████████████████████████▎     | 2810/3372 [12:21:19<2:19:23, 14.88s/it]\n",
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+      "                                                                                \u001b[A[INFO|trainer.py:3478] 2024-07-10 19:46:56,652 >> Saving model checkpoint to saves/internlm2_5_7b/lora/sft/checkpoint-2810\n",
+      "[INFO|configuration_utils.py:733] 2024-07-10 19:46:58,097 >> loading configuration file config.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/config.json\n",
+      "[INFO|configuration_utils.py:800] 2024-07-10 19:46:58,099 >> Model config InternLM2Config {\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\": \"4.42.3\",\n",
+      "  \"use_cache\": true,\n",
+      "  \"vocab_size\": 92544\n",
+      "}\n",
+      "\n",
+      "[INFO|tokenization_utils_base.py:2574] 2024-07-10 19:46:58,543 >> tokenizer config file saved in saves/internlm2_5_7b/lora/sft/checkpoint-2810/tokenizer_config.json\n",
+      "[INFO|tokenization_utils_base.py:2583] 2024-07-10 19:46:58,544 >> Special tokens file saved in saves/internlm2_5_7b/lora/sft/checkpoint-2810/special_tokens_map.json\n",
+      "{'loss': 0.0302, 'grad_norm': 0.020216332748532295, 'learning_rate': 5.853700670713297e-06, 'epoch': 5.16}\n",
+      "{'loss': 0.036, 'grad_norm': 0.00992613285779953, 'learning_rate': 3.6636842112887148e-06, 'epoch': 5.33}\n",
+      "{'loss': 0.0383, 'grad_norm': 0.8236948251724243, 'learning_rate': 1.9700337478568364e-06, 'epoch': 5.51}\n",
+      "{'loss': 0.0255, 'grad_norm': 0.10125435143709183, 'learning_rate': 7.908920801814434e-07, 'epoch': 5.69}\n",
+      "{'loss': 0.029, 'grad_norm': 0.011783115565776825, 'learning_rate': 1.3889046343094626e-07, 'epoch': 5.87}\n",
+      "100%|████████████████████████████████████| 3372/3372 [14:42:13<00:00, 15.02s/it][INFO|trainer.py:3788] 2024-07-10 22:07:50,066 >> \n",
+      "***** Running Evaluation *****\n",
+      "[INFO|trainer.py:3790] 2024-07-10 22:07:50,066 >>   Num examples = 500\n",
+      "[INFO|trainer.py:3793] 2024-07-10 22:07:50,066 >>   Batch size = 1\n",
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+      "                                                                                \u001b[A\n",
+      "\u001b[A{'eval_loss': 0.7247006893157959, 'eval_runtime': 363.4081, 'eval_samples_per_second': 1.376, 'eval_steps_per_second': 1.376, 'epoch': 5.99}\n",
+      "100%|████████████████████████████████████| 3372/3372 [14:48:16<00:00, 15.02s/it]\n",
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+      "                                                                                \u001b[A[INFO|trainer.py:3478] 2024-07-10 22:13:53,474 >> Saving model checkpoint to saves/internlm2_5_7b/lora/sft/checkpoint-3372\n",
+      "[INFO|configuration_utils.py:733] 2024-07-10 22:13:54,525 >> loading configuration file config.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/config.json\n",
+      "[INFO|configuration_utils.py:800] 2024-07-10 22:13:54,527 >> Model config InternLM2Config {\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\": \"4.42.3\",\n",
+      "  \"use_cache\": true,\n",
+      "  \"vocab_size\": 92544\n",
+      "}\n",
+      "\n",
+      "[INFO|tokenization_utils_base.py:2574] 2024-07-10 22:13:55,120 >> tokenizer config file saved in saves/internlm2_5_7b/lora/sft/checkpoint-3372/tokenizer_config.json\n",
+      "[INFO|tokenization_utils_base.py:2583] 2024-07-10 22:13:55,121 >> Special tokens file saved in saves/internlm2_5_7b/lora/sft/checkpoint-3372/special_tokens_map.json\n",
+      "[INFO|trainer.py:2383] 2024-07-10 22:13:55,986 >> \n",
+      "\n",
+      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
+      "\n",
+      "\n",
+      "{'train_runtime': 53299.0481, 'train_samples_per_second': 0.507, 'train_steps_per_second': 0.063, 'train_loss': 0.1883302003604803, 'epoch': 5.99}\n",
+      "100%|████████████████████████████████████| 3372/3372 [14:48:19<00:00, 15.81s/it]\n",
+      "[INFO|trainer.py:3478] 2024-07-10 22:13:55,991 >> Saving model checkpoint to saves/internlm2_5_7b/lora/sft\n",
+      "[INFO|configuration_utils.py:733] 2024-07-10 22:13:56,880 >> loading configuration file config.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/8d1a709a04d71440ef3df6ebbe204672f411c8b6/config.json\n",
+      "[INFO|configuration_utils.py:800] 2024-07-10 22:13:56,881 >> Model config InternLM2Config {\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\": \"4.42.3\",\n",
+      "  \"use_cache\": true,\n",
+      "  \"vocab_size\": 92544\n",
+      "}\n",
+      "\n",
+      "[INFO|tokenization_utils_base.py:2574] 2024-07-10 22:13:57,208 >> tokenizer config file saved in saves/internlm2_5_7b/lora/sft/tokenizer_config.json\n",
+      "[INFO|tokenization_utils_base.py:2583] 2024-07-10 22:13:57,209 >> Special tokens file saved in saves/internlm2_5_7b/lora/sft/special_tokens_map.json\n",
+      "***** train metrics *****\n",
+      "  epoch                    =      5.9947\n",
+      "  total_flos               = 378783663GF\n",
+      "  train_loss               =      0.1883\n",
+      "  train_runtime            = 14:48:19.04\n",
+      "  train_samples_per_second =       0.507\n",
+      "  train_steps_per_second   =       0.063\n",
+      "Figure saved at: saves/internlm2_5_7b/lora/sft/training_loss.png\n",
+      "Figure saved at: saves/internlm2_5_7b/lora/sft/training_eval_loss.png\n",
+      "[INFO|trainer.py:3788] 2024-07-10 22:13:57,824 >> \n",
+      "***** Running Evaluation *****\n",
+      "[INFO|trainer.py:3790] 2024-07-10 22:13:57,824 >>   Num examples = 500\n",
+      "[INFO|trainer.py:3793] 2024-07-10 22:13:57,824 >>   Batch size = 1\n",
+      "100%|█████████████████████████████████████████| 500/500 [06:05<00:00,  1.37it/s]\n",
+      "***** eval metrics *****\n",
+      "  epoch                   =     5.9947\n",
+      "  eval_loss               =     0.7247\n",
+      "  eval_runtime            = 0:06:06.24\n",
+      "  eval_samples_per_second =      1.365\n",
+      "  eval_steps_per_second   =      1.365\n",
+      "[INFO|modelcard.py:449] 2024-07-10 22:20:04,068 >> Dropping the following result as it does not have all the necessary fields:\n",
+      "{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}}\n",
+      "CPU times: user 21min 4s, sys: 8min 4s, total: 29min 8s\n",
+      "Wall time: 15h 8min 59s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "!./scripts/tune-lf.sh config/internlm2_5_7b_lora_sft_4bit.yaml"
+   ]
+  }
+ ],
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