{ "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/mgtv-results_nv4080_r2.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 23:11:43 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 49C P0 28W / 150W | 0MiB / 12282MiB | 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": 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 9.97 ms, sys: 1.61 ms, total: 11.6 ms\n", "Wall time: 491 ms\n" ] } ], "source": [ "%%time\n", "!python --version\n", "!pip show flash-attn" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": { "byteLimit": 2048000, "rowLimit": 10000 }, "inputWidgets": {}, "nuid": "4f51fafe-5527-49a2-8ccf-a0192e69493a", "showTitle": false, "title": "" } }, "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": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1\n", "loading env vars from: /home/inflaton/code/projects/courses/logical-reasoning/.env\n", "Adding /home/inflaton/code/projects/courses/logical-reasoning to sys.path\n", "loading /home/inflaton/code/projects/courses/logical-reasoning/llm_toolkit/logical_reasoning_utils.py\n", "internlm/internlm2_5-7b-chat-1m llama-factory/saves/internlm2_5_7b/lora/sft/checkpoint-562 True datasets/mgtv results/mgtv-results_nv4080_r2.csv\n", "(1) GPU = NVIDIA GeForce RTX 4080 Laptop GPU. Max memory = 11.994 GB.\n", "0.0 GB of memory reserved.\n", "loading model: internlm/internlm2_5-7b-chat-1m\n", "[INFO|tokenization_utils_base.py:2161] 2024-07-10 23:11:52,011 >> 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 23:11:52,011 >> loading file added_tokens.json from cache at None\n", "[INFO|tokenization_utils_base.py:2161] 2024-07-10 23:11:52,011 >> 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 23:11:52,011 >> 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 23:11:52,011 >> loading file tokenizer.json from cache at None\n", "07/10/2024 23:11:52 - INFO - llamafactory.data.template - Replace eos token: <|im_end|>\n", "07/10/2024 23:11:52 - INFO - llamafactory.data.template - Add <|im_start|> to stop words.\n", "[INFO|configuration_utils.py:733] 2024-07-10 23:11:53,228 >> 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 23:11:53,974 >> 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 23:11:53,974 >> 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 23:11:53 - INFO - llamafactory.model.model_utils.quantization - Quantizing model to 4 bit with bitsandbytes.\n", "07/10/2024 23:11:53 - INFO - llamafactory.model.patcher - Using KV cache for faster generation.\n", "[INFO|modeling_utils.py:3556] 2024-07-10 23:11:54,708 >> 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 23:11:55,106 >> Instantiating InternLM2ForCausalLM model under default dtype torch.bfloat16.\n", "[INFO|configuration_utils.py:1000] 2024-07-10 23:11:55,106 >> 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 [07:22<00:00, 55.37s/it]\n", "[INFO|modeling_utils.py:4364] 2024-07-10 23:19:18,153 >> All model checkpoint weights were used when initializing InternLM2ForCausalLM.\n", "\n", "[INFO|modeling_utils.py:4372] 2024-07-10 23:19:18,153 >> 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 23:19:19,129 >> 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 23:19:19,129 >> 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 23:19:19 - INFO - llamafactory.model.model_utils.attention - Using vanilla attention implementation.\n", "07/10/2024 23:19:19 - INFO - llamafactory.model.adapter - Loaded adapter(s): llama-factory/saves/internlm2_5_7b/lora/sft/checkpoint-562\n", "07/10/2024 23:19:19 - INFO - llamafactory.model.loader - all params: 7,756,582,912\n", "(2) GPU = NVIDIA GeForce RTX 4080 Laptop GPU. Max memory = 11.994 GB.\n", "4.895 GB of memory reserved.\n", "loading train/test data files\n", "Map: 100%|██████████████████████| 25000/25000 [00:01<00:00, 12664.68 examples/s]\n", "Map: 100%|████████████████████████| 3000/3000 [00:00<00:00, 10867.76 examples/s]\n", "DatasetDict({\n", " train: Dataset({\n", " features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n", " num_rows: 25000\n", " })\n", " test: Dataset({\n", " features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n", " num_rows: 3000\n", " })\n", "})\n", "--------------------------------------------------\n", "text: 甄加索是自杀吗\n", "--------------------------------------------------\n", "label: 不是\n", "--------------------------------------------------\n", "answer: nan\n", "--------------------------------------------------\n", "title: 海岸之谜\n", "--------------------------------------------------\n", "puzzle: 在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任何打斗的迹象。请问甄加索的死因是什么？\n", "--------------------------------------------------\n", "truth: 甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在创作一幅描绘海洋生物的画作。在画即将完成的前一天晚上，他骑着自行车外出，打算在海边观赏夜景。然而，他在沙滩上意外发现了一只搁浅的海豚，为了救助这只海豚，他耗费了极大的体力，最终成功将其送回海中。筋疲力尽的甄加索在沙滩上睡着了，由于他患有严重的心脏病，却未告知旁人，在寒冷的海风中，他的心脏停止了跳动。因此，警方在现场只发现了车轮痕迹和未完成的画作，而没有发现任何他杀的迹象。\n", "--------------------------------------------------\n", "train_text: <|im_start|>system\n", "You are an expert in logical reasoning.<|im_end|>\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: 在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任何打斗的迹象。请问甄加索的死因是什么？\n", "\n", "实际情况: 甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在创作一幅描绘海洋生物的画作。在画即将完成的前一天晚上，他骑着自行车外出，打算在海边观赏夜景。然而，他在沙滩上意外发现了一只搁浅的海豚，为了救助这只海豚，他耗费了极大的体力，最终成功将其送回海中。筋疲力尽的甄加索在沙滩上睡着了，由于他患有严重的心脏病，却未告知旁人，在寒冷的海风中，他的心脏停止了跳动。因此，警方在现场只发现了车轮痕迹和未完成的画作，而没有发现任何他杀的迹象。\n", "\n", "参与者提出的问题: 甄加索是自杀吗\n", "<|im_end|>\n", "<|im_start|>assistant\n", "不是<|im_end|>\n", "--------------------------------------------------\n", "prompt: <|im_start|>system\n", "You are an expert in logical reasoning.<|im_end|>\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: 在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任何打斗的迹象。请问甄加索的死因是什么？\n", "\n", "实际情况: 甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在创作一幅描绘海洋生物的画作。在画即将完成的前一天晚上，他骑着自行车外出，打算在海边观赏夜景。然而，他在沙滩上意外发现了一只搁浅的海豚，为了救助这只海豚，他耗费了极大的体力，最终成功将其送回海中。筋疲力尽的甄加索在沙滩上睡着了，由于他患有严重的心脏病，却未告知旁人，在寒冷的海风中，他的心脏停止了跳动。因此，警方在现场只发现了车轮痕迹和未完成的画作，而没有发现任何他杀的迹象。\n", "\n", "参与者提出的问题: 甄加索是自杀吗\n", "<|im_end|>\n", "<|im_start|>assistant\n", "\n", "--------------------------------------------------\n", "text: 死者受伤了吗\n", "--------------------------------------------------\n", "label: 不是\n", "--------------------------------------------------\n", "answer: nan\n", "--------------------------------------------------\n", "title: 甄庄哭声\n", "--------------------------------------------------\n", "puzzle: 在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着一顶破旧的帽子，但没有人知道这顶帽子是从哪里来的，哭泣声又是为何。请还原故事真相。\n", "--------------------------------------------------\n", "truth: 原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖中的海龟是他们的朋友。后来，小男孩随父母去了城市生活，但每年夏天都会回到村子探望爷爷。然而，去年夏天，爷爷因病去世，小男孩伤心欲绝。今年夏天，他回到村子，来到湖边，想起和爷爷的美好回忆，忍不住哭泣。他将爷爷的帽子放在湖边的石头上，希望能让爷爷的在天之灵得到安慰。那晚的哭泣声正是小男孩在祭莫他亲爱的爷爷。\n", "--------------------------------------------------\n", "train_text: <|im_start|>system\n", "You are an expert in logical reasoning.<|im_end|>\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: 在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着一顶破旧的帽子，但没有人知道这顶帽子是从哪里来的，哭泣声又是为何。请还原故事真相。\n", "\n", "实际情况: 原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖中的海龟是他们的朋友。后来，小男孩随父母去了城市生活，但每年夏天都会回到村子探望爷爷。然而，去年夏天，爷爷因病去世，小男孩伤心欲绝。今年夏天，他回到村子，来到湖边，想起和爷爷的美好回忆，忍不住哭泣。他将爷爷的帽子放在湖边的石头上，希望能让爷爷的在天之灵得到安慰。那晚的哭泣声正是小男孩在祭莫他亲爱的爷爷。\n", "\n", "参与者提出的问题: 死者受伤了吗\n", "<|im_end|>\n", "<|im_start|>assistant\n", "不是<|im_end|>\n", "--------------------------------------------------\n", "prompt: <|im_start|>system\n", "You are an expert in logical reasoning.<|im_end|>\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: 在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着一顶破旧的帽子，但没有人知道这顶帽子是从哪里来的，哭泣声又是为何。请还原故事真相。\n", "\n", "实际情况: 原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖中的海龟是他们的朋友。后来，小男孩随父母去了城市生活，但每年夏天都会回到村子探望爷爷。然而，去年夏天，爷爷因病去世，小男孩伤心欲绝。今年夏天，他回到村子，来到湖边，想起和爷爷的美好回忆，忍不住哭泣。他将爷爷的帽子放在湖边的石头上，希望能让爷爷的在天之灵得到安慰。那晚的哭泣声正是小男孩在祭莫他亲爱的爷爷。\n", "\n", "参与者提出的问题: 死者受伤了吗\n", "<|im_end|>\n", "<|im_start|>assistant\n", "\n", "Evaluating model: internlm/internlm2_5-7b-chat-1m\n", " 0%| | 0/3000 [00:00\n", "--------\n", "step 2: 不是\n", "--------\n", "step 3: 不是\n", "--------\n", "step 4: 不是\n", "--------\n", "step 5: 不是\n", "100%|███████████████████████████████████████| 3000/3000 [45:06<00:00, 1.11it/s]\n", "(3) GPU = NVIDIA GeForce RTX 4080 Laptop GPU. Max memory = 11.994 GB.\n", "5.887 GB of memory reserved.\n", " text ... internlm/internlm2_5-7b-chat-1m_checkpoint-562\n", "0 甄加索是自杀吗 ... 不是\n", "\n", "[1 rows x 7 columns]\n", "{'accuracy': 0.392, 'incorrect_ids': [4, 5, 7, 8, 9, 11, 12, 13, 17, 21, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 43, 44, 45, 51, 55, 56, 57, 58, 60, 61, 63, 65, 66, 67, 68, 69, 70, 77, 78, 82, 83, 86, 88, 93, 94, 95, 96, 97, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 112, 113, 114, 115, 118, 119, 120, 121, 122, 123, 124, 125, 128, 129, 135, 136, 137, 138, 139, 143, 147, 149, 150, 151, 152, 153, 155, 156, 160, 161, 163, 164, 168, 169, 170, 171, 172, 173, 176, 179, 180, 181, 184, 185, 186, 187, 190, 193, 194, 195, 199, 200, 202, 203, 207, 209, 216, 218, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 232, 234, 235, 236, 239, 240, 241, 243, 245, 248, 250, 252, 253, 254, 255, 256, 259, 260, 261, 262, 263, 265, 268, 269, 273, 275, 276, 278, 279, 280, 282, 283, 286, 287, 289, 292, 293, 294, 295, 299, 301, 304, 307, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 320, 321, 322, 323, 326, 328, 329, 330, 332, 333, 334, 335, 337, 338, 341, 342, 344, 345, 346, 347, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 364, 365, 367, 368, 369, 370, 371, 372, 373, 374, 376, 377, 380, 381, 382, 383, 386, 388, 389, 391, 392, 393, 395, 397, 402, 403, 404, 407, 408, 410, 414, 416, 419, 420, 422, 423, 425, 428, 429, 430, 432, 438, 440, 442, 443, 445, 447, 450, 452, 454, 455, 456, 458, 461, 467, 469, 470, 471, 472, 473, 474, 475, 476, 478, 479, 480, 482, 483, 484, 485, 486, 488, 489, 490, 492, 493, 494, 495, 496, 497, 499, 500, 501, 502, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 517, 518, 520, 521, 522, 524, 526, 528, 529, 530, 531, 533, 534, 536, 538, 540, 543, 549, 550, 551, 552, 555, 556, 558, 559, 560, 561, 566, 568, 570, 571, 572, 574, 577, 579, 580, 581, 582, 585, 591, 592, 593, 594, 596, 597, 598, 599, 600, 601, 603, 609, 610, 612, 613, 614, 619, 620, 621, 625, 626, 628, 629, 632, 635, 637, 640, 641, 642, 643, 644, 645, 646, 647, 649, 650, 653, 657, 658, 659, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 678, 680, 682, 683, 684, 686, 687, 689, 690, 695, 696, 697, 701, 702, 704, 705, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 729, 730, 731, 732, 733, 734, 735, 737, 739, 740, 741, 743, 744, 746, 750, 752, 755, 756, 757, 758, 759, 760, 763, 765, 766, 767, 768, 769, 770, 771, 772, 774, 775, 776, 777, 778, 779, 781, 783, 785, 786, 789, 790, 791, 792, 793, 794, 795, 797, 798, 799, 800, 802, 803, 804, 805, 808, 809, 810, 812, 814, 816, 817, 818, 819, 820, 821, 822, 823, 824, 826, 829, 831, 832, 833, 834, 835, 837, 838, 840, 841, 842, 844, 847, 848, 849, 851, 853, 855, 856, 857, 859, 861, 862, 865, 866, 867, 868, 869, 870, 872, 873, 876, 877, 878, 879, 880, 881, 884, 885, 887, 888, 889, 890, 891, 892, 894, 895, 897, 898, 899, 900, 901, 902, 903, 904, 906, 908, 910, 912, 913, 916, 917, 920, 925, 927, 928, 929, 930, 931, 932, 935, 937, 940, 941, 942, 943, 944, 945, 946, 948, 951, 952, 954, 957, 962, 963, 964, 967, 968, 969, 970, 971, 972, 978, 980, 982, 986, 988, 989, 990, 991, 992, 993, 994, 995, 998, 1001, 1002, 1003, 1005, 1006, 1007, 1010, 1011, 1012, 1013, 1015, 1017, 1020, 1021, 1022, 1023, 1025, 1031, 1036, 1038, 1040, 1041, 1043, 1045, 1046, 1050, 1051, 1053, 1054, 1055, 1056, 1057, 1060, 1061, 1066, 1067, 1068, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1082, 1083, 1084, 1085, 1087, 1089, 1091, 1092, 1094, 1095, 1096, 1097, 1098, 1099, 1105, 1106, 1107, 1108, 1109, 1111, 1114, 1115, 1116, 1118, 1120, 1121, 1122, 1123, 1124, 1126, 1129, 1131, 1133, 1135, 1138, 1139, 1144, 1145, 1146, 1148, 1149, 1150, 1151, 1153, 1155, 1156, 1157, 1159, 1160, 1161, 1163, 1164, 1167, 1168, 1169, 1170, 1172, 1173, 1175, 1177, 1178, 1182, 1184, 1185, 1189, 1190, 1191, 1196, 1197, 1199, 1200, 1202, 1203, 1204, 1207, 1209, 1212, 1213, 1216, 1218, 1225, 1231, 1232, 1233, 1234, 1237, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1248, 1249, 1250, 1253, 1254, 1255, 1256, 1258, 1260, 1261, 1262, 1263, 1266, 1267, 1269, 1274, 1277, 1285, 1288, 1289, 1291, 1294, 1296, 1300, 1301, 1305, 1306, 1307, 1308, 1310, 1311, 1314, 1315, 1316, 1317, 1321, 1323, 1326, 1329, 1331, 1333, 1334, 1335, 1336, 1337, 1338, 1340, 1342, 1344, 1345, 1347, 1348, 1349, 1350, 1353, 1354, 1355, 1356, 1357, 1362, 1363, 1366, 1367, 1368, 1369, 1370, 1374, 1376, 1379, 1380, 1382, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1399, 1400, 1407, 1409, 1412, 1413, 1416, 1417, 1418, 1420, 1422, 1424, 1425, 1427, 1428, 1429, 1430, 1431, 1435, 1437, 1438, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1461, 1462, 1463, 1465, 1467, 1468, 1469, 1470, 1472, 1473, 1475, 1476, 1478, 1479, 1481, 1482, 1485, 1486, 1487, 1488, 1490, 1491, 1493, 1494, 1496, 1498, 1500, 1501, 1504, 1510, 1512, 1513, 1514, 1517, 1518, 1519, 1523, 1525, 1526, 1527, 1528, 1529, 1531, 1532, 1533, 1534, 1536, 1538, 1540, 1544, 1545, 1546, 1547, 1556, 1557, 1558, 1559, 1560, 1562, 1563, 1564, 1565, 1566, 1569, 1570, 1572, 1573, 1579, 1581, 1583, 1585, 1586, 1587, 1588, 1590, 1591, 1593, 1594, 1595, 1596, 1598, 1601, 1602, 1603, 1604, 1605, 1606, 1608, 1616, 1617, 1620, 1621, 1622, 1624, 1625, 1626, 1627, 1631, 1632, 1633, 1636, 1637, 1638, 1640, 1641, 1644, 1645, 1647, 1648, 1650, 1653, 1654, 1655, 1656, 1658, 1659, 1660, 1663, 1664, 1666, 1668, 1669, 1670, 1671, 1672, 1673, 1674, 1676, 1677, 1678, 1679, 1681, 1683, 1685, 1686, 1687, 1688, 1689, 1690, 1691, 1692, 1694, 1695, 1696, 1698, 1700, 1701, 1703, 1704, 1707, 1713, 1715, 1716, 1717, 1718, 1720, 1723, 1726, 1727, 1734, 1736, 1738, 1739, 1740, 1741, 1743, 1744, 1745, 1748, 1749, 1750, 1751, 1753, 1754, 1755, 1756, 1757, 1758, 1760, 1763, 1765, 1766, 1767, 1768, 1769, 1770, 1772, 1773, 1774, 1775, 1776, 1780, 1784, 1785, 1786, 1787, 1789, 1790, 1792, 1794, 1795, 1796, 1797, 1798, 1799, 1800, 1803, 1804, 1805, 1807, 1808, 1812, 1814, 1816, 1818, 1819, 1820, 1823, 1825, 1826, 1827, 1828, 1830, 1831, 1832, 1834, 1835, 1836, 1837, 1838, 1839, 1840, 1841, 1845, 1846, 1848, 1849, 1851, 1852, 1854, 1858, 1860, 1862, 1867, 1869, 1870, 1871, 1872, 1873, 1878, 1879, 1881, 1882, 1883, 1888, 1890, 1895, 1897, 1904, 1905, 1909, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1922, 1924, 1925, 1926, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1939, 1941, 1943, 1944, 1945, 1947, 1949, 1950, 1952, 1953, 1954, 1955, 1957, 1958, 1959, 1960, 1961, 1963, 1964, 1966, 1967, 1968, 1969, 1973, 1978, 1979, 1981, 1982, 1984, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 2001, 2002, 2004, 2005, 2006, 2007, 2008, 2010, 2011, 2014, 2015, 2017, 2020, 2021, 2022, 2023, 2025, 2027, 2029, 2030, 2032, 2035, 2037, 2038, 2039, 2042, 2046, 2047, 2050, 2051, 2052, 2053, 2054, 2055, 2058, 2059, 2061, 2062, 2063, 2064, 2066, 2070, 2072, 2073, 2075, 2077, 2080, 2081, 2083, 2086, 2089, 2090, 2091, 2092, 2094, 2095, 2096, 2097, 2098, 2100, 2101, 2102, 2103, 2104, 2106, 2107, 2109, 2110, 2111, 2112, 2114, 2117, 2118, 2119, 2121, 2122, 2123, 2126, 2127, 2128, 2129, 2130, 2131, 2133, 2134, 2135, 2138, 2139, 2140, 2143, 2144, 2145, 2147, 2148, 2149, 2150, 2151, 2154, 2155, 2156, 2158, 2160, 2161, 2162, 2163, 2164, 2166, 2168, 2169, 2171, 2173, 2176, 2177, 2178, 2179, 2180, 2181, 2184, 2185, 2186, 2188, 2189, 2191, 2192, 2193, 2194, 2195, 2196, 2197, 2198, 2199, 2205, 2207, 2208, 2209, 2211, 2212, 2215, 2217, 2219, 2220, 2221, 2222, 2223, 2224, 2226, 2230, 2231, 2233, 2234, 2236, 2239, 2240, 2243, 2245, 2246, 2247, 2248, 2249, 2250, 2251, 2253, 2254, 2255, 2256, 2257, 2258, 2260, 2261, 2262, 2264, 2265, 2266, 2267, 2269, 2270, 2273, 2274, 2276, 2280, 2282, 2283, 2285, 2286, 2287, 2290, 2292, 2293, 2297, 2301, 2304, 2305, 2307, 2308, 2309, 2311, 2312, 2313, 2314, 2316, 2317, 2319, 2320, 2321, 2322, 2323, 2324, 2325, 2326, 2330, 2332, 2333, 2334, 2335, 2336, 2337, 2338, 2339, 2340, 2341, 2343, 2344, 2345, 2346, 2347, 2348, 2350, 2352, 2356, 2359, 2362, 2363, 2364, 2366, 2367, 2368, 2369, 2370, 2372, 2375, 2376, 2378, 2380, 2383, 2388, 2391, 2393, 2394, 2395, 2397, 2398, 2399, 2400, 2402, 2403, 2404, 2405, 2407, 2408, 2410, 2411, 2412, 2415, 2416, 2418, 2423, 2424, 2425, 2429, 2431, 2435, 2437, 2439, 2440, 2441, 2442, 2444, 2445, 2446, 2447, 2448, 2449, 2451, 2457, 2460, 2461, 2463, 2464, 2465, 2466, 2469, 2471, 2474, 2475, 2476, 2477, 2478, 2479, 2481, 2482, 2484, 2488, 2489, 2491, 2492, 2493, 2494, 2495, 2496, 2498, 2499, 2500, 2501, 2508, 2511, 2512, 2514, 2515, 2516, 2517, 2518, 2519, 2521, 2522, 2523, 2525, 2526, 2527, 2528, 2529, 2530, 2531, 2532, 2533, 2534, 2535, 2537, 2538, 2539, 2540, 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2554, 2555, 2556, 2557, 2558, 2559, 2560, 2562, 2563, 2564, 2565, 2566, 2567, 2568, 2569, 2572, 2574, 2575, 2577, 2578, 2580, 2581, 2583, 2584, 2586, 2588, 2589, 2590, 2591, 2592, 2594, 2595, 2597, 2598, 2599, 2600, 2605, 2606, 2607, 2610, 2611, 2612, 2613, 2615, 2620, 2621, 2622, 2623, 2624, 2625, 2626, 2629, 2630, 2632, 2634, 2635, 2636, 2643, 2644, 2646, 2648, 2649, 2651, 2652, 2654, 2655, 2657, 2659, 2660, 2661, 2663, 2664, 2665, 2667, 2668, 2669, 2670, 2671, 2672, 2673, 2676, 2678, 2679, 2680, 2681, 2682, 2683, 2684, 2685, 2686, 2687, 2691, 2694, 2695, 2697, 2698, 2699, 2701, 2702, 2703, 2704, 2705, 2707, 2708, 2710, 2711, 2714, 2715, 2716, 2718, 2719, 2723, 2724, 2725, 2727, 2728, 2730, 2731, 2733, 2735, 2736, 2737, 2739, 2742, 2743, 2744, 2746, 2747, 2748, 2749, 2750, 2751, 2754, 2755, 2756, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2772, 2777, 2778, 2779, 2781, 2782, 2788, 2795, 2797, 2798, 2801, 2802, 2803, 2805, 2807, 2809, 2811, 2815, 2816, 2817, 2818, 2820, 2821, 2823, 2825, 2829, 2831, 2832, 2833, 2834, 2835, 2837, 2838, 2839, 2840, 2841, 2842, 2844, 2845, 2846, 2849, 2850, 2853, 2856, 2857, 2858, 2859, 2860, 2861, 2864, 2866, 2867, 2874, 2876, 2877, 2878, 2880, 2881, 2882, 2883, 2884, 2885, 2886, 2887, 2888, 2889, 2890, 2891, 2893, 2895, 2896, 2898, 2899, 2900, 2902, 2905, 2909, 2910, 2912, 2913, 2914, 2915, 2916, 2917, 2918, 2919, 2920, 2921, 2923, 2925, 2927, 2928, 2932, 2933, 2935, 2936, 2937, 2938, 2939, 2940, 2942, 2943, 2944, 2949, 2950, 2953, 2955, 2957, 2962, 2963, 2964, 2965, 2970, 2972, 2973, 2975, 2976, 2977, 2978, 2979, 2980, 2981, 2983, 2985, 2986, 2990, 2991, 2995, 2996, 2998, 2999]}\n", "Epoch 2\n", "loading env vars from: /home/inflaton/code/projects/courses/logical-reasoning/.env\n", "Adding /home/inflaton/code/projects/courses/logical-reasoning to sys.path\n", "loading /home/inflaton/code/projects/courses/logical-reasoning/llm_toolkit/logical_reasoning_utils.py\n", "internlm/internlm2_5-7b-chat-1m llama-factory/saves/internlm2_5_7b/lora/sft/checkpoint-1124 True datasets/mgtv results/mgtv-results_nv4080_r2.csv\n", "(1) GPU = NVIDIA GeForce RTX 4080 Laptop GPU. Max memory = 11.994 GB.\n", "0.0 GB of memory reserved.\n", "loading model: internlm/internlm2_5-7b-chat-1m\n", "[INFO|tokenization_utils_base.py:2161] 2024-07-11 00:04:39,775 >> 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] 2024-07-11 00:04:39,776 >> loading file added_tokens.json from cache at None\n", "[INFO|tokenization_utils_base.py:2161] 2024-07-11 00:04:39,776 >> 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] 2024-07-11 00:04:39,776 >> 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] 2024-07-11 00:04:39,776 >> loading file tokenizer.json from cache at None\n", "07/11/2024 00:04:40 - INFO - llamafactory.data.template - Replace eos token: <|im_end|>\n", "07/11/2024 00:04:40 - INFO - llamafactory.data.template - Add <|im_start|> to stop words.\n", "[INFO|configuration_utils.py:733] 2024-07-11 00:04:41,392 >> 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] 2024-07-11 00:04:42,376 >> 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] 2024-07-11 00:04:42,376 >> 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", "07/11/2024 00:04:42 - INFO - llamafactory.model.model_utils.quantization - Quantizing model to 4 bit with bitsandbytes.\n", "07/11/2024 00:04:42 - INFO - llamafactory.model.patcher - Using KV cache for faster generation.\n", "[INFO|modeling_utils.py:3556] 2024-07-11 00:04:43,026 >> 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] 2024-07-11 00:04:43,186 >> Instantiating InternLM2ForCausalLM model under default dtype torch.bfloat16.\n", "[INFO|configuration_utils.py:1000] 2024-07-11 00:04:43,186 >> 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 [06:53<00:00, 51.74s/it]\n", "[INFO|modeling_utils.py:4364] 2024-07-11 00:11:37,295 >> All model checkpoint weights were used when initializing InternLM2ForCausalLM.\n", "\n", "[INFO|modeling_utils.py:4372] 2024-07-11 00:11:37,295 >> 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] 2024-07-11 00:11:37,835 >> 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] 2024-07-11 00:11:37,835 >> 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 00:11:37 - INFO - llamafactory.model.model_utils.attention - Using vanilla attention implementation.\n", "07/11/2024 00:11:38 - INFO - llamafactory.model.adapter - Loaded adapter(s): llama-factory/saves/internlm2_5_7b/lora/sft/checkpoint-1124\n", "07/11/2024 00:11:38 - INFO - llamafactory.model.loader - all params: 7,756,582,912\n", "(2) GPU = NVIDIA GeForce RTX 4080 Laptop GPU. Max memory = 11.994 GB.\n", "4.895 GB of memory reserved.\n", "loading train/test data files\n", "DatasetDict({\n", " train: Dataset({\n", " features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n", " num_rows: 25000\n", " })\n", " test: Dataset({\n", " features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n", " num_rows: 3000\n", " })\n", "})\n", "--------------------------------------------------\n", "text: 甄加索是自杀吗\n", "--------------------------------------------------\n", "label: 不是\n", "--------------------------------------------------\n", "answer: nan\n", "--------------------------------------------------\n", "title: 海岸之谜\n", "--------------------------------------------------\n", "puzzle: 在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任何打斗的迹象。请问甄加索的死因是什么？\n", "--------------------------------------------------\n", "truth: 甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在创作一幅描绘海洋生物的画作。在画即将完成的前一天晚上，他骑着自行车外出，打算在海边观赏夜景。然而，他在沙滩上意外发现了一只搁浅的海豚，为了救助这只海豚，他耗费了极大的体力，最终成功将其送回海中。筋疲力尽的甄加索在沙滩上睡着了，由于他患有严重的心脏病，却未告知旁人，在寒冷的海风中，他的心脏停止了跳动。因此，警方在现场只发现了车轮痕迹和未完成的画作，而没有发现任何他杀的迹象。\n", "--------------------------------------------------\n", "train_text: <|im_start|>system\n", "You are an expert in logical reasoning.<|im_end|>\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: 在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任何打斗的迹象。请问甄加索的死因是什么？\n", "\n", "实际情况: 甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在创作一幅描绘海洋生物的画作。在画即将完成的前一天晚上，他骑着自行车外出，打算在海边观赏夜景。然而，他在沙滩上意外发现了一只搁浅的海豚，为了救助这只海豚，他耗费了极大的体力，最终成功将其送回海中。筋疲力尽的甄加索在沙滩上睡着了，由于他患有严重的心脏病，却未告知旁人，在寒冷的海风中，他的心脏停止了跳动。因此，警方在现场只发现了车轮痕迹和未完成的画作，而没有发现任何他杀的迹象。\n", "\n", "参与者提出的问题: 甄加索是自杀吗\n", "<|im_end|>\n", "<|im_start|>assistant\n", "不是<|im_end|>\n", "--------------------------------------------------\n", "prompt: <|im_start|>system\n", "You are an expert in logical reasoning.<|im_end|>\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: 在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任何打斗的迹象。请问甄加索的死因是什么？\n", "\n", "实际情况: 甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在创作一幅描绘海洋生物的画作。在画即将完成的前一天晚上，他骑着自行车外出，打算在海边观赏夜景。然而，他在沙滩上意外发现了一只搁浅的海豚，为了救助这只海豚，他耗费了极大的体力，最终成功将其送回海中。筋疲力尽的甄加索在沙滩上睡着了，由于他患有严重的心脏病，却未告知旁人，在寒冷的海风中，他的心脏停止了跳动。因此，警方在现场只发现了车轮痕迹和未完成的画作，而没有发现任何他杀的迹象。\n", "\n", "参与者提出的问题: 甄加索是自杀吗\n", "<|im_end|>\n", "<|im_start|>assistant\n", "\n", "--------------------------------------------------\n", "text: 死者受伤了吗\n", "--------------------------------------------------\n", "label: 不是\n", "--------------------------------------------------\n", "answer: nan\n", "--------------------------------------------------\n", "title: 甄庄哭声\n", "--------------------------------------------------\n", "puzzle: 在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着一顶破旧的帽子，但没有人知道这顶帽子是从哪里来的，哭泣声又是为何。请还原故事真相。\n", "--------------------------------------------------\n", "truth: 原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖中的海龟是他们的朋友。后来，小男孩随父母去了城市生活，但每年夏天都会回到村子探望爷爷。然而，去年夏天，爷爷因病去世，小男孩伤心欲绝。今年夏天，他回到村子，来到湖边，想起和爷爷的美好回忆，忍不住哭泣。他将爷爷的帽子放在湖边的石头上，希望能让爷爷的在天之灵得到安慰。那晚的哭泣声正是小男孩在祭莫他亲爱的爷爷。\n", "--------------------------------------------------\n", "train_text: <|im_start|>system\n", "You are an expert in logical reasoning.<|im_end|>\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: 在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着一顶破旧的帽子，但没有人知道这顶帽子是从哪里来的，哭泣声又是为何。请还原故事真相。\n", "\n", "实际情况: 原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖中的海龟是他们的朋友。后来，小男孩随父母去了城市生活，但每年夏天都会回到村子探望爷爷。然而，去年夏天，爷爷因病去世，小男孩伤心欲绝。今年夏天，他回到村子，来到湖边，想起和爷爷的美好回忆，忍不住哭泣。他将爷爷的帽子放在湖边的石头上，希望能让爷爷的在天之灵得到安慰。那晚的哭泣声正是小男孩在祭莫他亲爱的爷爷。\n", "\n", "参与者提出的问题: 死者受伤了吗\n", "<|im_end|>\n", "<|im_start|>assistant\n", "不是<|im_end|>\n", "--------------------------------------------------\n", "prompt: <|im_start|>system\n", "You are an expert in logical reasoning.<|im_end|>\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: 在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着一顶破旧的帽子，但没有人知道这顶帽子是从哪里来的，哭泣声又是为何。请还原故事真相。\n", "\n", "实际情况: 原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖中的海龟是他们的朋友。后来，小男孩随父母去了城市生活，但每年夏天都会回到村子探望爷爷。然而，去年夏天，爷爷因病去世，小男孩伤心欲绝。今年夏天，他回到村子，来到湖边，想起和爷爷的美好回忆，忍不住哭泣。他将爷爷的帽子放在湖边的石头上，希望能让爷爷的在天之灵得到安慰。那晚的哭泣声正是小男孩在祭莫他亲爱的爷爷。\n", "\n", "参与者提出的问题: 死者受伤了吗\n", "<|im_end|>\n", "<|im_start|>assistant\n", "\n", "Evaluating model: internlm/internlm2_5-7b-chat-1m\n", " 0%| | 0/3000 [00:00\n", "--------\n", "step 2: 不是\n", "--------\n", "step 3: 不是\n", "--------\n", "step 4: 不是\n", "--------\n", "step 5: 不是\n", "100%|███████████████████████████████████████| 3000/3000 [46:44<00:00, 1.07it/s]\n", "(3) GPU = NVIDIA GeForce RTX 4080 Laptop GPU. Max memory = 11.994 GB.\n", "5.887 GB of memory reserved.\n", " text ... internlm/internlm2_5-7b-chat-1m_checkpoint-1124\n", "0 甄加索是自杀吗 ... 不是\n", "\n", "[1 rows x 8 columns]\n", "{'accuracy': 0.611, 'incorrect_ids': [6, 8, 9, 10, 12, 15, 16, 17, 24, 25, 26, 27, 28, 31, 35, 36, 37, 38, 48, 52, 55, 57, 58, 60, 61, 65, 66, 67, 78, 82, 83, 88, 90, 93, 95, 96, 97, 100, 103, 104, 106, 108, 109, 112, 113, 114, 115, 119, 120, 121, 123, 126, 128, 131, 135, 136, 137, 138, 139, 141, 143, 150, 152, 153, 155, 156, 160, 161, 162, 163, 164, 170, 179, 180, 181, 184, 185, 186, 190, 193, 199, 200, 202, 209, 216, 218, 224, 225, 227, 228, 234, 236, 240, 243, 245, 248, 250, 251, 253, 254, 259, 260, 261, 263, 275, 276, 278, 282, 284, 289, 292, 293, 294, 295, 299, 301, 304, 309, 311, 314, 317, 320, 321, 323, 325, 328, 332, 334, 335, 337, 338, 342, 346, 349, 350, 352, 353, 354, 355, 356, 359, 360, 361, 362, 368, 370, 371, 372, 373, 374, 376, 377, 386, 388, 389, 393, 395, 397, 401, 402, 403, 404, 407, 409, 410, 414, 416, 420, 421, 422, 423, 428, 429, 430, 432, 438, 445, 447, 450, 452, 454, 455, 456, 458, 469, 470, 471, 472, 473, 476, 477, 480, 486, 488, 490, 492, 493, 494, 495, 501, 502, 504, 506, 507, 508, 509, 510, 511, 512, 513, 514, 517, 519, 520, 526, 529, 530, 533, 538, 540, 549, 550, 555, 556, 560, 566, 568, 570, 571, 579, 581, 589, 591, 592, 593, 594, 596, 597, 601, 603, 609, 612, 613, 614, 621, 625, 626, 628, 629, 632, 636, 641, 643, 644, 645, 647, 649, 650, 651, 653, 663, 665, 666, 668, 671, 678, 680, 682, 684, 686, 690, 695, 697, 701, 702, 704, 705, 708, 709, 711, 716, 718, 719, 720, 721, 722, 725, 729, 730, 732, 734, 735, 739, 740, 741, 743, 747, 752, 754, 757, 758, 760, 763, 765, 769, 770, 771, 772, 774, 776, 778, 779, 781, 785, 798, 799, 801, 803, 805, 808, 809, 810, 812, 813, 814, 816, 817, 818, 819, 820, 821, 822, 823, 824, 826, 828, 832, 833, 835, 837, 840, 841, 842, 847, 848, 851, 853, 856, 857, 861, 866, 869, 870, 873, 876, 877, 878, 884, 886, 888, 889, 890, 891, 894, 897, 898, 899, 901, 904, 906, 910, 912, 913, 917, 925, 927, 929, 930, 932, 935, 937, 940, 941, 943, 945, 948, 951, 952, 962, 964, 966, 968, 969, 970, 972, 980, 986, 988, 989, 991, 992, 993, 994, 995, 998, 1001, 1003, 1006, 1007, 1011, 1012, 1014, 1015, 1020, 1022, 1025, 1028, 1031, 1036, 1038, 1039, 1040, 1041, 1043, 1045, 1046, 1051, 1053, 1054, 1056, 1062, 1066, 1070, 1071, 1074, 1075, 1077, 1078, 1079, 1080, 1082, 1084, 1085, 1086, 1087, 1089, 1096, 1108, 1111, 1114, 1116, 1120, 1121, 1126, 1129, 1133, 1135, 1151, 1153, 1159, 1161, 1163, 1166, 1167, 1170, 1172, 1173, 1174, 1177, 1178, 1180, 1181, 1185, 1190, 1196, 1197, 1203, 1212, 1216, 1218, 1228, 1231, 1232, 1239, 1240, 1241, 1242, 1243, 1244, 1246, 1252, 1254, 1256, 1258, 1260, 1261, 1262, 1266, 1269, 1274, 1282, 1289, 1300, 1305, 1306, 1308, 1311, 1313, 1314, 1315, 1317, 1326, 1331, 1336, 1338, 1342, 1345, 1349, 1353, 1357, 1362, 1363, 1367, 1370, 1382, 1385, 1386, 1387, 1388, 1389, 1392, 1393, 1395, 1406, 1407, 1413, 1417, 1420, 1424, 1425, 1426, 1428, 1430, 1431, 1435, 1437, 1438, 1440, 1441, 1443, 1444, 1445, 1446, 1448, 1450, 1451, 1452, 1453, 1455, 1456, 1457, 1458, 1459, 1462, 1463, 1467, 1468, 1469, 1473, 1475, 1476, 1478, 1481, 1484, 1487, 1491, 1493, 1494, 1496, 1499, 1504, 1510, 1512, 1517, 1525, 1526, 1528, 1533, 1537, 1544, 1545, 1547, 1551, 1556, 1560, 1562, 1565, 1569, 1572, 1573, 1577, 1579, 1580, 1581, 1585, 1586, 1587, 1588, 1590, 1591, 1593, 1594, 1595, 1596, 1602, 1603, 1604, 1605, 1606, 1608, 1612, 1613, 1620, 1622, 1623, 1627, 1631, 1632, 1633, 1636, 1637, 1641, 1645, 1647, 1648, 1650, 1652, 1653, 1654, 1655, 1656, 1658, 1659, 1666, 1668, 1669, 1670, 1671, 1672, 1673, 1674, 1676, 1677, 1678, 1679, 1680, 1686, 1688, 1689, 1690, 1691, 1692, 1694, 1695, 1696, 1700, 1701, 1704, 1713, 1716, 1717, 1718, 1720, 1723, 1726, 1727, 1731, 1735, 1736, 1737, 1740, 1743, 1745, 1751, 1753, 1754, 1755, 1756, 1758, 1760, 1763, 1765, 1766, 1767, 1768, 1769, 1770, 1772, 1773, 1774, 1780, 1784, 1786, 1787, 1789, 1790, 1791, 1792, 1794, 1796, 1797, 1798, 1799, 1803, 1804, 1809, 1812, 1814, 1816, 1818, 1820, 1823, 1827, 1830, 1834, 1835, 1837, 1840, 1841, 1848, 1849, 1852, 1854, 1855, 1858, 1860, 1867, 1869, 1872, 1873, 1879, 1882, 1890, 1897, 1903, 1904, 1909, 1912, 1913, 1914, 1915, 1919, 1922, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1947, 1949, 1950, 1951, 1953, 1955, 1958, 1963, 1964, 1973, 1978, 1981, 1984, 1985, 1986, 1987, 1989, 1990, 1992, 1993, 1994, 1995, 1996, 1998, 1999, 2001, 2002, 2004, 2005, 2007, 2010, 2011, 2014, 2017, 2021, 2022, 2025, 2029, 2030, 2035, 2038, 2039, 2043, 2046, 2048, 2052, 2053, 2054, 2058, 2059, 2062, 2063, 2064, 2072, 2075, 2077, 2081, 2082, 2086, 2089, 2092, 2097, 2099, 2100, 2101, 2102, 2107, 2109, 2112, 2114, 2118, 2119, 2121, 2122, 2124, 2126, 2128, 2129, 2130, 2133, 2135, 2138, 2139, 2143, 2144, 2145, 2147, 2155, 2160, 2161, 2162, 2164, 2168, 2169, 2173, 2176, 2177, 2178, 2179, 2180, 2185, 2186, 2188, 2189, 2192, 2193, 2194, 2195, 2196, 2197, 2199, 2203, 2209, 2212, 2215, 2221, 2223, 2226, 2230, 2231, 2240, 2243, 2245, 2246, 2247, 2248, 2249, 2250, 2252, 2253, 2254, 2256, 2260, 2261, 2262, 2264, 2265, 2272, 2273, 2274, 2276, 2278, 2279, 2280, 2285, 2290, 2293, 2297, 2301, 2302, 2304, 2311, 2312, 2313, 2317, 2320, 2322, 2323, 2324, 2329, 2330, 2333, 2334, 2335, 2337, 2339, 2340, 2344, 2348, 2349, 2355, 2359, 2360, 2362, 2364, 2366, 2368, 2372, 2376, 2378, 2388, 2389, 2393, 2395, 2398, 2400, 2404, 2405, 2406, 2410, 2412, 2416, 2420, 2421, 2423, 2424, 2425, 2426, 2429, 2437, 2439, 2440, 2441, 2442, 2444, 2445, 2446, 2448, 2460, 2461, 2463, 2464, 2465, 2469, 2471, 2475, 2477, 2484, 2486, 2488, 2491, 2493, 2498, 2499, 2501, 2512, 2515, 2517, 2518, 2522, 2524, 2525, 2526, 2528, 2529, 2530, 2532, 2534, 2535, 2538, 2539, 2546, 2547, 2548, 2549, 2554, 2555, 2556, 2557, 2559, 2560, 2562, 2563, 2564, 2565, 2566, 2567, 2569, 2574, 2575, 2577, 2581, 2589, 2590, 2597, 2598, 2605, 2606, 2607, 2610, 2620, 2622, 2623, 2624, 2626, 2628, 2629, 2630, 2635, 2636, 2660, 2663, 2667, 2670, 2671, 2672, 2676, 2678, 2680, 2681, 2682, 2695, 2697, 2701, 2707, 2708, 2710, 2713, 2714, 2715, 2716, 2718, 2721, 2725, 2727, 2730, 2731, 2733, 2735, 2736, 2737, 2738, 2744, 2746, 2747, 2749, 2751, 2754, 2756, 2757, 2760, 2761, 2762, 2764, 2766, 2767, 2770, 2776, 2777, 2781, 2786, 2788, 2791, 2793, 2795, 2797, 2798, 2801, 2803, 2805, 2806, 2807, 2811, 2815, 2816, 2818, 2820, 2822, 2833, 2837, 2841, 2842, 2843, 2845, 2846, 2854, 2857, 2858, 2860, 2861, 2864, 2867, 2870, 2877, 2878, 2882, 2884, 2888, 2889, 2896, 2899, 2902, 2905, 2912, 2913, 2915, 2916, 2919, 2921, 2933, 2937, 2939, 2942, 2944, 2949, 2953, 2955, 2957, 2963, 2965, 2966, 2968, 2975, 2976, 2977, 2979, 2980, 2981, 2983, 2985, 2986, 2990, 2991, 2995, 2996, 2998, 2999]}\n", "Epoch 3\n", "loading env vars from: /home/inflaton/code/projects/courses/logical-reasoning/.env\n", "Adding /home/inflaton/code/projects/courses/logical-reasoning to sys.path\n", "loading /home/inflaton/code/projects/courses/logical-reasoning/llm_toolkit/logical_reasoning_utils.py\n", "internlm/internlm2_5-7b-chat-1m llama-factory/saves/internlm2_5_7b/lora/sft/checkpoint-1686 True datasets/mgtv results/mgtv-results_nv4080_r2.csv\n", "(1) GPU = NVIDIA GeForce RTX 4080 Laptop GPU. Max memory = 11.994 GB.\n", "0.0 GB of memory reserved.\n", "loading model: internlm/internlm2_5-7b-chat-1m\n", "[INFO|tokenization_utils_base.py:2161] 2024-07-11 00:58:33,476 >> 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] 2024-07-11 00:58:33,476 >> loading file added_tokens.json from cache at None\n", "[INFO|tokenization_utils_base.py:2161] 2024-07-11 00:58:33,476 >> 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] 2024-07-11 00:58:33,476 >> 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] 2024-07-11 00:58:33,476 >> loading file tokenizer.json from cache at None\n", "07/11/2024 00:58:34 - INFO - llamafactory.data.template - Replace eos token: <|im_end|>\n", "07/11/2024 00:58:34 - INFO - llamafactory.data.template - Add <|im_start|> to stop words.\n", "[INFO|configuration_utils.py:733] 2024-07-11 00:58:35,595 >> 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] 2024-07-11 00:58:36,357 >> 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] 2024-07-11 00:58:36,357 >> 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", "07/11/2024 00:58:36 - INFO - llamafactory.model.model_utils.quantization - Quantizing model to 4 bit with bitsandbytes.\n", "07/11/2024 00:58:36 - INFO - llamafactory.model.patcher - Using KV cache for faster generation.\n", "[INFO|modeling_utils.py:3556] 2024-07-11 00:58:36,963 >> 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] 2024-07-11 00:58:37,118 >> Instantiating InternLM2ForCausalLM model under default dtype torch.bfloat16.\n", "[INFO|configuration_utils.py:1000] 2024-07-11 00:58:37,119 >> 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 [06:58<00:00, 52.36s/it]\n", "[INFO|modeling_utils.py:4364] 2024-07-11 01:05:36,153 >> All model checkpoint weights were used when initializing InternLM2ForCausalLM.\n", "\n", "[INFO|modeling_utils.py:4372] 2024-07-11 01:05:36,153 >> 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] 2024-07-11 01:05:37,032 >> 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] 2024-07-11 01:05:37,033 >> 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 01:05:37 - INFO - llamafactory.model.model_utils.attention - Using vanilla attention implementation.\n", "07/11/2024 01:05:37 - INFO - llamafactory.model.adapter - Loaded adapter(s): llama-factory/saves/internlm2_5_7b/lora/sft/checkpoint-1686\n", "07/11/2024 01:05:37 - INFO - llamafactory.model.loader - all params: 7,756,582,912\n", "(2) GPU = NVIDIA GeForce RTX 4080 Laptop GPU. Max memory = 11.994 GB.\n", "4.895 GB of memory reserved.\n", "loading train/test data files\n", "DatasetDict({\n", " train: Dataset({\n", " features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n", " num_rows: 25000\n", " })\n", " test: Dataset({\n", " features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n", " num_rows: 3000\n", " })\n", "})\n", "--------------------------------------------------\n", "text: 甄加索是自杀吗\n", "--------------------------------------------------\n", "label: 不是\n", "--------------------------------------------------\n", "answer: nan\n", "--------------------------------------------------\n", "title: 海岸之谜\n", "--------------------------------------------------\n", "puzzle: 在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任何打斗的迹象。请问甄加索的死因是什么？\n", "--------------------------------------------------\n", "truth: 甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在创作一幅描绘海洋生物的画作。在画即将完成的前一天晚上，他骑着自行车外出，打算在海边观赏夜景。然而，他在沙滩上意外发现了一只搁浅的海豚，为了救助这只海豚，他耗费了极大的体力，最终成功将其送回海中。筋疲力尽的甄加索在沙滩上睡着了，由于他患有严重的心脏病，却未告知旁人，在寒冷的海风中，他的心脏停止了跳动。因此，警方在现场只发现了车轮痕迹和未完成的画作，而没有发现任何他杀的迹象。\n", "--------------------------------------------------\n", "train_text: <|im_start|>system\n", "You are an expert in logical reasoning.<|im_end|>\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: 在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任何打斗的迹象。请问甄加索的死因是什么？\n", "\n", "实际情况: 甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在创作一幅描绘海洋生物的画作。在画即将完成的前一天晚上，他骑着自行车外出，打算在海边观赏夜景。然而，他在沙滩上意外发现了一只搁浅的海豚，为了救助这只海豚，他耗费了极大的体力，最终成功将其送回海中。筋疲力尽的甄加索在沙滩上睡着了，由于他患有严重的心脏病，却未告知旁人，在寒冷的海风中，他的心脏停止了跳动。因此，警方在现场只发现了车轮痕迹和未完成的画作，而没有发现任何他杀的迹象。\n", "\n", "参与者提出的问题: 甄加索是自杀吗\n", "<|im_end|>\n", "<|im_start|>assistant\n", "不是<|im_end|>\n", "--------------------------------------------------\n", "prompt: <|im_start|>system\n", "You are an expert in logical reasoning.<|im_end|>\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: 在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任何打斗的迹象。请问甄加索的死因是什么？\n", "\n", "实际情况: 甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在创作一幅描绘海洋生物的画作。在画即将完成的前一天晚上，他骑着自行车外出，打算在海边观赏夜景。然而，他在沙滩上意外发现了一只搁浅的海豚，为了救助这只海豚，他耗费了极大的体力，最终成功将其送回海中。筋疲力尽的甄加索在沙滩上睡着了，由于他患有严重的心脏病，却未告知旁人，在寒冷的海风中，他的心脏停止了跳动。因此，警方在现场只发现了车轮痕迹和未完成的画作，而没有发现任何他杀的迹象。\n", "\n", "参与者提出的问题: 甄加索是自杀吗\n", "<|im_end|>\n", "<|im_start|>assistant\n", "\n", "--------------------------------------------------\n", "text: 死者受伤了吗\n", "--------------------------------------------------\n", "label: 不是\n", "--------------------------------------------------\n", "answer: nan\n", "--------------------------------------------------\n", "title: 甄庄哭声\n", "--------------------------------------------------\n", "puzzle: 在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着一顶破旧的帽子，但没有人知道这顶帽子是从哪里来的，哭泣声又是为何。请还原故事真相。\n", "--------------------------------------------------\n", "truth: 原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖中的海龟是他们的朋友。后来，小男孩随父母去了城市生活，但每年夏天都会回到村子探望爷爷。然而，去年夏天，爷爷因病去世，小男孩伤心欲绝。今年夏天，他回到村子，来到湖边，想起和爷爷的美好回忆，忍不住哭泣。他将爷爷的帽子放在湖边的石头上，希望能让爷爷的在天之灵得到安慰。那晚的哭泣声正是小男孩在祭莫他亲爱的爷爷。\n", "--------------------------------------------------\n", "train_text: <|im_start|>system\n", "You are an expert in logical reasoning.<|im_end|>\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: 在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着一顶破旧的帽子，但没有人知道这顶帽子是从哪里来的，哭泣声又是为何。请还原故事真相。\n", "\n", "实际情况: 原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖中的海龟是他们的朋友。后来，小男孩随父母去了城市生活，但每年夏天都会回到村子探望爷爷。然而，去年夏天，爷爷因病去世，小男孩伤心欲绝。今年夏天，他回到村子，来到湖边，想起和爷爷的美好回忆，忍不住哭泣。他将爷爷的帽子放在湖边的石头上，希望能让爷爷的在天之灵得到安慰。那晚的哭泣声正是小男孩在祭莫他亲爱的爷爷。\n", "\n", "参与者提出的问题: 死者受伤了吗\n", "<|im_end|>\n", "<|im_start|>assistant\n", "不是<|im_end|>\n", "--------------------------------------------------\n", "prompt: <|im_start|>system\n", "You are an expert in logical reasoning.<|im_end|>\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: 在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着一顶破旧的帽子，但没有人知道这顶帽子是从哪里来的，哭泣声又是为何。请还原故事真相。\n", "\n", "实际情况: 原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖中的海龟是他们的朋友。后来，小男孩随父母去了城市生活，但每年夏天都会回到村子探望爷爷。然而，去年夏天，爷爷因病去世，小男孩伤心欲绝。今年夏天，他回到村子，来到湖边，想起和爷爷的美好回忆，忍不住哭泣。他将爷爷的帽子放在湖边的石头上，希望能让爷爷的在天之灵得到安慰。那晚的哭泣声正是小男孩在祭莫他亲爱的爷爷。\n", "\n", "参与者提出的问题: 死者受伤了吗\n", "<|im_end|>\n", "<|im_start|>assistant\n", "\n", "Evaluating model: internlm/internlm2_5-7b-chat-1m\n", " 0%| | 0/3000 [00:00\n", "--------\n", "step 2: 不是\n", "--------\n", "step 3: 不是\n", "--------\n", "step 4: 不是\n", "--------\n", "step 5: 不是\n", "100%|███████████████████████████████████████| 3000/3000 [47:21<00:00, 1.06it/s]\n", "(3) GPU = NVIDIA GeForce RTX 4080 Laptop GPU. Max memory = 11.994 GB.\n", "5.887 GB of memory reserved.\n", " text ... internlm/internlm2_5-7b-chat-1m_checkpoint-1686\n", "0 甄加索是自杀吗 ... 不是\n", "\n", "[1 rows x 9 columns]\n", "{'accuracy': 0.7136666666666667, 'incorrect_ids': [6, 10, 12, 15, 16, 17, 24, 25, 29, 31, 34, 35, 36, 55, 58, 61, 65, 66, 67, 77, 78, 79, 81, 83, 88, 91, 93, 102, 103, 108, 109, 112, 115, 120, 121, 123, 128, 135, 136, 138, 139, 143, 150, 153, 155, 159, 160, 161, 164, 173, 174, 179, 180, 190, 193, 199, 200, 202, 207, 218, 224, 225, 234, 236, 240, 243, 245, 248, 250, 251, 252, 259, 260, 263, 265, 266, 271, 275, 276, 279, 283, 286, 292, 295, 296, 301, 304, 308, 314, 317, 318, 320, 321, 323, 328, 330, 334, 335, 337, 342, 349, 350, 352, 353, 356, 358, 360, 361, 362, 364, 368, 370, 373, 376, 377, 383, 386, 389, 395, 397, 398, 404, 409, 410, 411, 414, 416, 427, 428, 429, 430, 438, 442, 445, 447, 450, 452, 454, 456, 457, 467, 468, 471, 472, 473, 476, 486, 488, 492, 494, 495, 501, 502, 504, 506, 507, 508, 510, 511, 514, 515, 517, 519, 520, 525, 530, 531, 533, 536, 537, 538, 539, 540, 546, 555, 557, 560, 561, 565, 566, 568, 569, 570, 571, 579, 581, 585, 589, 591, 592, 593, 596, 597, 601, 605, 609, 612, 613, 621, 625, 626, 628, 632, 633, 634, 636, 638, 643, 644, 647, 651, 657, 665, 666, 671, 674, 678, 682, 690, 695, 701, 702, 708, 709, 718, 720, 721, 726, 727, 729, 730, 734, 739, 745, 747, 752, 754, 758, 763, 766, 772, 774, 778, 785, 791, 798, 801, 805, 809, 812, 813, 815, 819, 820, 821, 822, 823, 824, 826, 827, 828, 833, 834, 837, 840, 843, 847, 848, 851, 854, 856, 861, 866, 869, 870, 875, 876, 884, 886, 889, 890, 891, 894, 897, 899, 901, 904, 906, 910, 913, 917, 927, 932, 937, 940, 943, 945, 953, 956, 964, 965, 966, 969, 970, 972, 980, 981, 982, 986, 991, 993, 994, 998, 1001, 1003, 1006, 1011, 1012, 1014, 1022, 1028, 1031, 1036, 1038, 1039, 1040, 1041, 1051, 1053, 1061, 1062, 1066, 1073, 1075, 1077, 1080, 1084, 1086, 1087, 1089, 1096, 1108, 1111, 1114, 1120, 1121, 1125, 1129, 1141, 1143, 1153, 1158, 1161, 1164, 1166, 1172, 1173, 1174, 1177, 1178, 1180, 1181, 1183, 1185, 1196, 1198, 1203, 1209, 1210, 1212, 1216, 1220, 1228, 1231, 1232, 1239, 1240, 1241, 1242, 1251, 1252, 1254, 1256, 1258, 1259, 1264, 1267, 1270, 1274, 1282, 1289, 1292, 1299, 1305, 1306, 1308, 1313, 1315, 1317, 1319, 1322, 1323, 1331, 1332, 1339, 1342, 1349, 1353, 1358, 1363, 1364, 1367, 1370, 1373, 1385, 1387, 1388, 1389, 1392, 1393, 1395, 1406, 1407, 1409, 1424, 1425, 1426, 1430, 1432, 1440, 1441, 1444, 1445, 1446, 1451, 1452, 1453, 1454, 1459, 1462, 1469, 1473, 1476, 1477, 1478, 1490, 1493, 1494, 1495, 1496, 1499, 1504, 1512, 1516, 1517, 1518, 1525, 1526, 1527, 1537, 1542, 1543, 1545, 1547, 1554, 1558, 1560, 1561, 1562, 1568, 1581, 1585, 1588, 1590, 1591, 1593, 1594, 1596, 1601, 1603, 1604, 1605, 1612, 1613, 1622, 1627, 1631, 1633, 1636, 1637, 1638, 1641, 1643, 1645, 1647, 1650, 1652, 1654, 1655, 1656, 1672, 1673, 1674, 1676, 1677, 1678, 1679, 1686, 1691, 1694, 1695, 1718, 1726, 1727, 1731, 1734, 1736, 1737, 1738, 1739, 1745, 1751, 1753, 1755, 1756, 1762, 1765, 1766, 1767, 1769, 1773, 1774, 1778, 1780, 1786, 1791, 1796, 1798, 1799, 1804, 1809, 1810, 1811, 1812, 1816, 1818, 1820, 1824, 1827, 1833, 1834, 1835, 1840, 1849, 1852, 1855, 1858, 1870, 1879, 1888, 1890, 1894, 1903, 1904, 1912, 1928, 1931, 1932, 1934, 1949, 1951, 1953, 1958, 1963, 1964, 1973, 1978, 1979, 1981, 1983, 1984, 1986, 1989, 1994, 1995, 1998, 2007, 2014, 2017, 2019, 2021, 2025, 2028, 2029, 2030, 2031, 2035, 2036, 2038, 2048, 2049, 2054, 2059, 2064, 2068, 2077, 2081, 2088, 2097, 2109, 2110, 2114, 2119, 2121, 2124, 2126, 2133, 2135, 2139, 2143, 2145, 2147, 2160, 2161, 2162, 2164, 2169, 2173, 2177, 2185, 2186, 2189, 2192, 2193, 2194, 2196, 2197, 2199, 2203, 2212, 2215, 2223, 2226, 2230, 2231, 2238, 2240, 2243, 2245, 2246, 2247, 2250, 2253, 2254, 2256, 2261, 2262, 2265, 2274, 2276, 2278, 2279, 2285, 2290, 2297, 2311, 2312, 2313, 2317, 2322, 2323, 2324, 2330, 2333, 2337, 2339, 2344, 2348, 2349, 2352, 2353, 2354, 2355, 2359, 2360, 2361, 2364, 2366, 2370, 2372, 2378, 2388, 2389, 2395, 2400, 2404, 2406, 2409, 2410, 2416, 2420, 2423, 2425, 2426, 2429, 2437, 2440, 2442, 2449, 2463, 2464, 2469, 2472, 2488, 2493, 2498, 2503, 2512, 2517, 2520, 2522, 2529, 2530, 2532, 2535, 2540, 2546, 2547, 2548, 2549, 2553, 2555, 2556, 2558, 2559, 2560, 2562, 2575, 2577, 2589, 2590, 2606, 2607, 2610, 2616, 2623, 2624, 2626, 2628, 2629, 2630, 2636, 2638, 2640, 2644, 2645, 2652, 2660, 2663, 2667, 2672, 2676, 2682, 2709, 2711, 2714, 2716, 2719, 2721, 2730, 2731, 2736, 2742, 2744, 2746, 2754, 2757, 2760, 2761, 2762, 2764, 2766, 2769, 2770, 2776, 2777, 2781, 2788, 2791, 2793, 2795, 2798, 2801, 2803, 2805, 2806, 2807, 2810, 2814, 2815, 2816, 2819, 2820, 2822, 2824, 2829, 2833, 2837, 2842, 2843, 2846, 2850, 2851, 2854, 2857, 2860, 2865, 2866, 2867, 2870, 2875, 2877, 2880, 2882, 2884, 2888, 2899, 2902, 2903, 2905, 2912, 2913, 2915, 2916, 2921, 2926, 2933, 2937, 2938, 2942, 2944, 2949, 2953, 2955, 2957, 2960, 2963, 2964, 2968, 2975, 2976, 2977, 2979, 2980, 2985, 2990, 2991, 2995, 2998]}\n", "Epoch 4\n", "loading env vars from: /home/inflaton/code/projects/courses/logical-reasoning/.env\n", "Adding /home/inflaton/code/projects/courses/logical-reasoning to sys.path\n", "loading /home/inflaton/code/projects/courses/logical-reasoning/llm_toolkit/logical_reasoning_utils.py\n", "internlm/internlm2_5-7b-chat-1m llama-factory/saves/internlm2_5_7b/lora/sft/checkpoint-2248 True datasets/mgtv results/mgtv-results_nv4080_r2.csv\n", "(1) GPU = NVIDIA GeForce RTX 4080 Laptop GPU. Max memory = 11.994 GB.\n", "0.0 GB of memory reserved.\n", "loading model: internlm/internlm2_5-7b-chat-1m\n", "[INFO|tokenization_utils_base.py:2161] 2024-07-11 01:53:10,463 >> 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] 2024-07-11 01:53:10,464 >> loading file added_tokens.json from cache at None\n", "[INFO|tokenization_utils_base.py:2161] 2024-07-11 01:53:10,464 >> 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] 2024-07-11 01:53:10,464 >> 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] 2024-07-11 01:53:10,464 >> loading file tokenizer.json from cache at None\n", "07/11/2024 01:53:11 - INFO - llamafactory.data.template - Replace eos token: <|im_end|>\n", "07/11/2024 01:53:11 - INFO - llamafactory.data.template - Add <|im_start|> to stop words.\n", "[INFO|configuration_utils.py:733] 2024-07-11 01:53:12,027 >> 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] 2024-07-11 01:53:13,530 >> 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] 2024-07-11 01:53:13,531 >> 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", "07/11/2024 01:53:13 - INFO - llamafactory.model.model_utils.quantization - Quantizing model to 4 bit with bitsandbytes.\n", "07/11/2024 01:53:13 - INFO - llamafactory.model.patcher - Using KV cache for faster generation.\n", "[INFO|modeling_utils.py:3556] 2024-07-11 01:53:14,104 >> 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] 2024-07-11 01:53:14,454 >> Instantiating InternLM2ForCausalLM model under default dtype torch.bfloat16.\n", "[INFO|configuration_utils.py:1000] 2024-07-11 01:53:14,455 >> 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 [06:42<00:00, 50.37s/it]\n", "[INFO|modeling_utils.py:4364] 2024-07-11 01:59:57,599 >> All model checkpoint weights were used when initializing InternLM2ForCausalLM.\n", "\n", "[INFO|modeling_utils.py:4372] 2024-07-11 01:59:57,599 >> 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] 2024-07-11 01:59:58,108 >> 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] 2024-07-11 01:59:58,108 >> 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 01:59:58 - INFO - llamafactory.model.model_utils.attention - Using vanilla attention implementation.\n", "07/11/2024 01:59:58 - INFO - llamafactory.model.adapter - Loaded adapter(s): llama-factory/saves/internlm2_5_7b/lora/sft/checkpoint-2248\n", "07/11/2024 01:59:58 - INFO - llamafactory.model.loader - all params: 7,756,582,912\n", "(2) GPU = NVIDIA GeForce RTX 4080 Laptop GPU. Max memory = 11.994 GB.\n", "4.895 GB of memory reserved.\n", "loading train/test data files\n", "DatasetDict({\n", " train: Dataset({\n", " features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n", " num_rows: 25000\n", " })\n", " test: Dataset({\n", " features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n", " num_rows: 3000\n", " })\n", "})\n", "--------------------------------------------------\n", "text: 甄加索是自杀吗\n", "--------------------------------------------------\n", "label: 不是\n", "--------------------------------------------------\n", "answer: nan\n", "--------------------------------------------------\n", "title: 海岸之谜\n", "--------------------------------------------------\n", "puzzle: 在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任何打斗的迹象。请问甄加索的死因是什么？\n", "--------------------------------------------------\n", "truth: 甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在创作一幅描绘海洋生物的画作。在画即将完成的前一天晚上，他骑着自行车外出，打算在海边观赏夜景。然而，他在沙滩上意外发现了一只搁浅的海豚，为了救助这只海豚，他耗费了极大的体力，最终成功将其送回海中。筋疲力尽的甄加索在沙滩上睡着了，由于他患有严重的心脏病，却未告知旁人，在寒冷的海风中，他的心脏停止了跳动。因此，警方在现场只发现了车轮痕迹和未完成的画作，而没有发现任何他杀的迹象。\n", "--------------------------------------------------\n", "train_text: <|im_start|>system\n", "You are an expert in logical reasoning.<|im_end|>\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: 在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任何打斗的迹象。请问甄加索的死因是什么？\n", "\n", "实际情况: 甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在创作一幅描绘海洋生物的画作。在画即将完成的前一天晚上，他骑着自行车外出，打算在海边观赏夜景。然而，他在沙滩上意外发现了一只搁浅的海豚，为了救助这只海豚，他耗费了极大的体力，最终成功将其送回海中。筋疲力尽的甄加索在沙滩上睡着了，由于他患有严重的心脏病，却未告知旁人，在寒冷的海风中，他的心脏停止了跳动。因此，警方在现场只发现了车轮痕迹和未完成的画作，而没有发现任何他杀的迹象。\n", "\n", "参与者提出的问题: 甄加索是自杀吗\n", "<|im_end|>\n", "<|im_start|>assistant\n", "不是<|im_end|>\n", "--------------------------------------------------\n", "prompt: <|im_start|>system\n", "You are an expert in logical reasoning.<|im_end|>\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: 在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任何打斗的迹象。请问甄加索的死因是什么？\n", "\n", "实际情况: 甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在创作一幅描绘海洋生物的画作。在画即将完成的前一天晚上，他骑着自行车外出，打算在海边观赏夜景。然而，他在沙滩上意外发现了一只搁浅的海豚，为了救助这只海豚，他耗费了极大的体力，最终成功将其送回海中。筋疲力尽的甄加索在沙滩上睡着了，由于他患有严重的心脏病，却未告知旁人，在寒冷的海风中，他的心脏停止了跳动。因此，警方在现场只发现了车轮痕迹和未完成的画作，而没有发现任何他杀的迹象。\n", "\n", "参与者提出的问题: 甄加索是自杀吗\n", "<|im_end|>\n", "<|im_start|>assistant\n", "\n", "--------------------------------------------------\n", "text: 死者受伤了吗\n", "--------------------------------------------------\n", "label: 不是\n", "--------------------------------------------------\n", "answer: nan\n", "--------------------------------------------------\n", "title: 甄庄哭声\n", "--------------------------------------------------\n", "puzzle: 在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着一顶破旧的帽子，但没有人知道这顶帽子是从哪里来的，哭泣声又是为何。请还原故事真相。\n", "--------------------------------------------------\n", "truth: 原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖中的海龟是他们的朋友。后来，小男孩随父母去了城市生活，但每年夏天都会回到村子探望爷爷。然而，去年夏天，爷爷因病去世，小男孩伤心欲绝。今年夏天，他回到村子，来到湖边，想起和爷爷的美好回忆，忍不住哭泣。他将爷爷的帽子放在湖边的石头上，希望能让爷爷的在天之灵得到安慰。那晚的哭泣声正是小男孩在祭莫他亲爱的爷爷。\n", "--------------------------------------------------\n", "train_text: <|im_start|>system\n", "You are an expert in logical reasoning.<|im_end|>\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: 在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着一顶破旧的帽子，但没有人知道这顶帽子是从哪里来的，哭泣声又是为何。请还原故事真相。\n", "\n", "实际情况: 原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖中的海龟是他们的朋友。后来，小男孩随父母去了城市生活，但每年夏天都会回到村子探望爷爷。然而，去年夏天，爷爷因病去世，小男孩伤心欲绝。今年夏天，他回到村子，来到湖边，想起和爷爷的美好回忆，忍不住哭泣。他将爷爷的帽子放在湖边的石头上，希望能让爷爷的在天之灵得到安慰。那晚的哭泣声正是小男孩在祭莫他亲爱的爷爷。\n", "\n", "参与者提出的问题: 死者受伤了吗\n", "<|im_end|>\n", "<|im_start|>assistant\n", "不是<|im_end|>\n", "--------------------------------------------------\n", "prompt: <|im_start|>system\n", "You are an expert in logical reasoning.<|im_end|>\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: 在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着一顶破旧的帽子，但没有人知道这顶帽子是从哪里来的，哭泣声又是为何。请还原故事真相。\n", "\n", "实际情况: 原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖中的海龟是他们的朋友。后来，小男孩随父母去了城市生活，但每年夏天都会回到村子探望爷爷。然而，去年夏天，爷爷因病去世，小男孩伤心欲绝。今年夏天，他回到村子，来到湖边，想起和爷爷的美好回忆，忍不住哭泣。他将爷爷的帽子放在湖边的石头上，希望能让爷爷的在天之灵得到安慰。那晚的哭泣声正是小男孩在祭莫他亲爱的爷爷。\n", "\n", "参与者提出的问题: 死者受伤了吗\n", "<|im_end|>\n", "<|im_start|>assistant\n", "\n", "Evaluating model: internlm/internlm2_5-7b-chat-1m\n", " 0%| | 0/3000 [00:00\n", "--------\n", "step 2: 不是\n", "--------\n", "step 3: 不是\n", "--------\n", "step 4: 不是\n", "--------\n", "step 5: 不是\n", "100%|███████████████████████████████████████| 3000/3000 [46:45<00:00, 1.07it/s]\n", "(3) GPU = NVIDIA GeForce RTX 4080 Laptop GPU. Max memory = 11.994 GB.\n", "5.887 GB of memory reserved.\n", " text ... internlm/internlm2_5-7b-chat-1m_checkpoint-2248\n", "0 甄加索是自杀吗 ... 不是\n", "\n", "[1 rows x 10 columns]\n", "{'accuracy': 0.653, 'incorrect_ids': [6, 11, 12, 16, 17, 24, 25, 28, 29, 31, 34, 35, 36, 55, 58, 61, 63, 65, 66, 67, 77, 78, 79, 81, 82, 83, 88, 93, 94, 96, 102, 103, 104, 108, 109, 112, 113, 115, 117, 121, 129, 131, 135, 136, 139, 141, 143, 149, 150, 153, 155, 159, 160, 161, 163, 164, 169, 172, 173, 179, 180, 190, 193, 199, 200, 202, 206, 207, 216, 218, 224, 225, 226, 228, 230, 234, 236, 240, 243, 245, 248, 250, 251, 252, 253, 259, 260, 262, 263, 265, 266, 269, 271, 275, 276, 283, 286, 287, 288, 292, 295, 296, 299, 300, 301, 303, 304, 308, 314, 317, 318, 320, 321, 323, 325, 330, 332, 333, 334, 335, 337, 339, 342, 350, 351, 352, 353, 354, 355, 356, 357, 360, 361, 368, 370, 371, 373, 376, 377, 386, 389, 393, 395, 396, 397, 403, 404, 409, 411, 414, 416, 423, 428, 429, 430, 438, 445, 447, 450, 452, 453, 454, 456, 457, 461, 462, 465, 467, 471, 472, 473, 474, 476, 477, 480, 482, 486, 488, 490, 492, 493, 494, 497, 501, 502, 504, 506, 507, 508, 510, 511, 512, 514, 515, 516, 517, 518, 519, 520, 526, 530, 531, 536, 538, 555, 556, 560, 561, 566, 568, 569, 571, 579, 581, 585, 589, 591, 593, 596, 597, 599, 601, 603, 609, 612, 613, 614, 621, 625, 626, 628, 632, 636, 642, 643, 644, 647, 649, 656, 663, 665, 666, 671, 674, 678, 682, 683, 686, 690, 695, 701, 702, 703, 708, 709, 716, 718, 720, 721, 726, 729, 730, 734, 735, 739, 740, 741, 745, 747, 754, 758, 760, 763, 766, 770, 771, 772, 774, 778, 785, 788, 791, 794, 798, 799, 800, 801, 805, 809, 810, 812, 813, 816, 817, 819, 820, 821, 822, 823, 824, 828, 833, 834, 837, 840, 841, 843, 844, 847, 848, 849, 851, 856, 857, 861, 862, 866, 869, 870, 875, 876, 877, 878, 884, 889, 890, 891, 894, 899, 901, 904, 906, 910, 917, 920, 924, 927, 929, 930, 932, 935, 937, 940, 943, 945, 946, 953, 954, 956, 962, 964, 966, 969, 970, 972, 980, 982, 986, 991, 993, 994, 997, 998, 1000, 1001, 1003, 1004, 1005, 1006, 1007, 1011, 1012, 1014, 1021, 1022, 1025, 1028, 1031, 1033, 1036, 1038, 1040, 1041, 1046, 1051, 1054, 1061, 1066, 1073, 1075, 1080, 1083, 1084, 1086, 1087, 1089, 1096, 1108, 1111, 1114, 1116, 1118, 1120, 1121, 1124, 1125, 1126, 1129, 1135, 1137, 1140, 1141, 1143, 1153, 1158, 1160, 1161, 1163, 1164, 1166, 1167, 1168, 1172, 1173, 1174, 1177, 1178, 1180, 1181, 1184, 1185, 1196, 1203, 1209, 1212, 1216, 1220, 1232, 1234, 1237, 1238, 1240, 1241, 1242, 1243, 1251, 1252, 1256, 1258, 1259, 1262, 1264, 1266, 1267, 1274, 1279, 1282, 1289, 1292, 1299, 1305, 1308, 1313, 1315, 1317, 1319, 1322, 1323, 1331, 1332, 1336, 1340, 1342, 1346, 1349, 1353, 1357, 1358, 1362, 1363, 1364, 1367, 1370, 1372, 1373, 1385, 1387, 1388, 1389, 1391, 1392, 1393, 1395, 1399, 1400, 1404, 1406, 1407, 1409, 1420, 1422, 1425, 1426, 1427, 1428, 1430, 1432, 1433, 1440, 1441, 1443, 1444, 1446, 1451, 1452, 1453, 1454, 1455, 1459, 1460, 1462, 1469, 1473, 1475, 1476, 1477, 1478, 1487, 1490, 1493, 1494, 1495, 1496, 1499, 1500, 1504, 1510, 1512, 1515, 1516, 1517, 1518, 1525, 1526, 1537, 1538, 1542, 1545, 1546, 1547, 1558, 1559, 1560, 1561, 1562, 1563, 1567, 1568, 1573, 1576, 1581, 1584, 1585, 1586, 1588, 1590, 1591, 1592, 1593, 1594, 1601, 1604, 1605, 1612, 1613, 1622, 1624, 1627, 1628, 1629, 1631, 1632, 1633, 1635, 1637, 1638, 1641, 1643, 1645, 1647, 1648, 1650, 1651, 1653, 1654, 1655, 1656, 1659, 1660, 1662, 1667, 1672, 1673, 1674, 1675, 1676, 1678, 1679, 1685, 1686, 1691, 1695, 1698, 1713, 1717, 1718, 1721, 1726, 1727, 1728, 1731, 1734, 1735, 1736, 1738, 1739, 1743, 1745, 1751, 1752, 1753, 1754, 1755, 1756, 1757, 1762, 1766, 1769, 1770, 1772, 1774, 1778, 1780, 1786, 1791, 1796, 1799, 1800, 1804, 1806, 1809, 1810, 1811, 1812, 1814, 1816, 1818, 1820, 1822, 1824, 1827, 1828, 1830, 1833, 1834, 1835, 1840, 1849, 1851, 1852, 1858, 1860, 1869, 1870, 1872, 1877, 1879, 1880, 1881, 1882, 1888, 1890, 1912, 1915, 1934, 1935, 1945, 1946, 1950, 1953, 1958, 1961, 1962, 1963, 1964, 1971, 1973, 1978, 1979, 1981, 1984, 1986, 1988, 1989, 1990, 1991, 1994, 1995, 1996, 1998, 2001, 2002, 2005, 2007, 2008, 2009, 2014, 2015, 2021, 2025, 2028, 2035, 2037, 2038, 2042, 2046, 2054, 2059, 2062, 2063, 2064, 2077, 2081, 2082, 2088, 2092, 2100, 2106, 2109, 2110, 2112, 2114, 2119, 2121, 2126, 2130, 2133, 2135, 2139, 2143, 2145, 2147, 2160, 2161, 2162, 2164, 2169, 2173, 2175, 2177, 2179, 2184, 2185, 2186, 2192, 2193, 2194, 2196, 2197, 2198, 2199, 2208, 2212, 2215, 2219, 2223, 2226, 2230, 2231, 2235, 2238, 2240, 2243, 2245, 2248, 2249, 2250, 2253, 2254, 2256, 2257, 2260, 2261, 2262, 2265, 2267, 2272, 2274, 2276, 2285, 2286, 2287, 2290, 2293, 2297, 2311, 2312, 2313, 2314, 2319, 2322, 2324, 2330, 2333, 2339, 2344, 2348, 2349, 2351, 2354, 2357, 2359, 2360, 2361, 2364, 2365, 2366, 2367, 2370, 2371, 2372, 2378, 2379, 2385, 2388, 2389, 2390, 2395, 2400, 2401, 2404, 2406, 2408, 2409, 2410, 2412, 2414, 2423, 2424, 2429, 2433, 2440, 2441, 2442, 2444, 2446, 2454, 2461, 2463, 2464, 2465, 2469, 2472, 2475, 2477, 2488, 2493, 2496, 2498, 2499, 2501, 2503, 2512, 2515, 2517, 2520, 2522, 2526, 2529, 2530, 2532, 2535, 2538, 2539, 2540, 2546, 2547, 2548, 2549, 2553, 2554, 2555, 2556, 2557, 2559, 2560, 2562, 2563, 2564, 2565, 2566, 2569, 2570, 2574, 2575, 2581, 2589, 2590, 2592, 2601, 2605, 2606, 2607, 2610, 2617, 2622, 2623, 2624, 2626, 2628, 2629, 2635, 2636, 2640, 2644, 2645, 2649, 2652, 2656, 2660, 2663, 2664, 2667, 2670, 2672, 2676, 2681, 2682, 2691, 2694, 2701, 2709, 2711, 2714, 2716, 2719, 2721, 2730, 2731, 2736, 2742, 2744, 2749, 2751, 2754, 2756, 2757, 2760, 2761, 2762, 2764, 2766, 2767, 2769, 2770, 2777, 2781, 2782, 2788, 2795, 2798, 2801, 2803, 2805, 2806, 2807, 2810, 2811, 2814, 2815, 2816, 2820, 2821, 2822, 2823, 2824, 2833, 2837, 2839, 2843, 2846, 2850, 2851, 2854, 2857, 2858, 2860, 2864, 2867, 2871, 2877, 2878, 2880, 2881, 2884, 2888, 2896, 2899, 2902, 2903, 2905, 2912, 2913, 2915, 2916, 2921, 2927, 2933, 2937, 2942, 2947, 2949, 2952, 2953, 2955, 2957, 2960, 2963, 2964, 2968, 2975, 2976, 2977, 2978, 2979, 2980, 2985, 2991, 2995, 2998, 2999]}\n", "Epoch 5\n", "loading env vars from: /home/inflaton/code/projects/courses/logical-reasoning/.env\n", "Adding /home/inflaton/code/projects/courses/logical-reasoning to sys.path\n", "loading /home/inflaton/code/projects/courses/logical-reasoning/llm_toolkit/logical_reasoning_utils.py\n", "internlm/internlm2_5-7b-chat-1m llama-factory/saves/internlm2_5_7b/lora/sft/checkpoint-2810 True datasets/mgtv results/mgtv-results_nv4080_r2.csv\n", "(1) GPU = NVIDIA GeForce RTX 4080 Laptop GPU. Max memory = 11.994 GB.\n", "0.0 GB of memory reserved.\n", "loading model: internlm/internlm2_5-7b-chat-1m\n", "[INFO|tokenization_utils_base.py:2161] 2024-07-11 02:46:54,113 >> 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] 2024-07-11 02:46:54,113 >> loading file added_tokens.json from cache at None\n", "[INFO|tokenization_utils_base.py:2161] 2024-07-11 02:46:54,114 >> 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] 2024-07-11 02:46:54,114 >> 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] 2024-07-11 02:46:54,114 >> loading file tokenizer.json from cache at None\n", "07/11/2024 02:46:55 - INFO - llamafactory.data.template - Replace eos token: <|im_end|>\n", "07/11/2024 02:46:55 - INFO - llamafactory.data.template - Add <|im_start|> to stop words.\n", "[INFO|configuration_utils.py:733] 2024-07-11 02:46:55,747 >> 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] 2024-07-11 02:46:57,051 >> 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] 2024-07-11 02:46:57,051 >> 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", "07/11/2024 02:46:57 - INFO - llamafactory.model.model_utils.quantization - Quantizing model to 4 bit with bitsandbytes.\n", "07/11/2024 02:46:57 - INFO - llamafactory.model.patcher - Using KV cache for faster generation.\n", "[INFO|modeling_utils.py:3556] 2024-07-11 02:46:57,631 >> 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] 2024-07-11 02:46:57,968 >> Instantiating InternLM2ForCausalLM model under default dtype torch.bfloat16.\n", "[INFO|configuration_utils.py:1000] 2024-07-11 02:46:57,969 >> 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 [07:22<00:00, 55.26s/it]\n", "[INFO|modeling_utils.py:4364] 2024-07-11 02:54:20,231 >> All model checkpoint weights were used when initializing InternLM2ForCausalLM.\n", "\n", "[INFO|modeling_utils.py:4372] 2024-07-11 02:54:20,231 >> 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] 2024-07-11 02:54:21,239 >> 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] 2024-07-11 02:54:21,239 >> 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 02:54:21 - INFO - llamafactory.model.model_utils.attention - Using vanilla attention implementation.\n", "07/11/2024 02:54:21 - INFO - llamafactory.model.adapter - Loaded adapter(s): llama-factory/saves/internlm2_5_7b/lora/sft/checkpoint-2810\n", "07/11/2024 02:54:21 - INFO - llamafactory.model.loader - all params: 7,756,582,912\n", "(2) GPU = NVIDIA GeForce RTX 4080 Laptop GPU. Max memory = 11.994 GB.\n", "4.895 GB of memory reserved.\n", "loading train/test data files\n", "DatasetDict({\n", " train: Dataset({\n", " features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n", " num_rows: 25000\n", " })\n", " test: Dataset({\n", " features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n", " num_rows: 3000\n", " })\n", "})\n", "--------------------------------------------------\n", "text: 甄加索是自杀吗\n", "--------------------------------------------------\n", "label: 不是\n", "--------------------------------------------------\n", "answer: nan\n", "--------------------------------------------------\n", "title: 海岸之谜\n", "--------------------------------------------------\n", "puzzle: 在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任何打斗的迹象。请问甄加索的死因是什么？\n", "--------------------------------------------------\n", "truth: 甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在创作一幅描绘海洋生物的画作。在画即将完成的前一天晚上，他骑着自行车外出，打算在海边观赏夜景。然而，他在沙滩上意外发现了一只搁浅的海豚，为了救助这只海豚，他耗费了极大的体力，最终成功将其送回海中。筋疲力尽的甄加索在沙滩上睡着了，由于他患有严重的心脏病，却未告知旁人，在寒冷的海风中，他的心脏停止了跳动。因此，警方在现场只发现了车轮痕迹和未完成的画作，而没有发现任何他杀的迹象。\n", "--------------------------------------------------\n", "train_text: <|im_start|>system\n", "You are an expert in logical reasoning.<|im_end|>\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: 在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任何打斗的迹象。请问甄加索的死因是什么？\n", "\n", "实际情况: 甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在创作一幅描绘海洋生物的画作。在画即将完成的前一天晚上，他骑着自行车外出，打算在海边观赏夜景。然而，他在沙滩上意外发现了一只搁浅的海豚，为了救助这只海豚，他耗费了极大的体力，最终成功将其送回海中。筋疲力尽的甄加索在沙滩上睡着了，由于他患有严重的心脏病，却未告知旁人，在寒冷的海风中，他的心脏停止了跳动。因此，警方在现场只发现了车轮痕迹和未完成的画作，而没有发现任何他杀的迹象。\n", "\n", "参与者提出的问题: 甄加索是自杀吗\n", "<|im_end|>\n", "<|im_start|>assistant\n", "不是<|im_end|>\n", "--------------------------------------------------\n", "prompt: <|im_start|>system\n", "You are an expert in logical reasoning.<|im_end|>\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: 在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任何打斗的迹象。请问甄加索的死因是什么？\n", "\n", "实际情况: 甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在创作一幅描绘海洋生物的画作。在画即将完成的前一天晚上，他骑着自行车外出，打算在海边观赏夜景。然而，他在沙滩上意外发现了一只搁浅的海豚，为了救助这只海豚，他耗费了极大的体力，最终成功将其送回海中。筋疲力尽的甄加索在沙滩上睡着了，由于他患有严重的心脏病，却未告知旁人，在寒冷的海风中，他的心脏停止了跳动。因此，警方在现场只发现了车轮痕迹和未完成的画作，而没有发现任何他杀的迹象。\n", "\n", "参与者提出的问题: 甄加索是自杀吗\n", "<|im_end|>\n", "<|im_start|>assistant\n", "\n", "--------------------------------------------------\n", "text: 死者受伤了吗\n", "--------------------------------------------------\n", "label: 不是\n", "--------------------------------------------------\n", "answer: nan\n", "--------------------------------------------------\n", "title: 甄庄哭声\n", "--------------------------------------------------\n", "puzzle: 在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着一顶破旧的帽子，但没有人知道这顶帽子是从哪里来的，哭泣声又是为何。请还原故事真相。\n", "--------------------------------------------------\n", "truth: 原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖中的海龟是他们的朋友。后来，小男孩随父母去了城市生活，但每年夏天都会回到村子探望爷爷。然而，去年夏天，爷爷因病去世，小男孩伤心欲绝。今年夏天，他回到村子，来到湖边，想起和爷爷的美好回忆，忍不住哭泣。他将爷爷的帽子放在湖边的石头上，希望能让爷爷的在天之灵得到安慰。那晚的哭泣声正是小男孩在祭莫他亲爱的爷爷。\n", "--------------------------------------------------\n", "train_text: <|im_start|>system\n", "You are an expert in logical reasoning.<|im_end|>\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: 在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着一顶破旧的帽子，但没有人知道这顶帽子是从哪里来的，哭泣声又是为何。请还原故事真相。\n", "\n", "实际情况: 原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖中的海龟是他们的朋友。后来，小男孩随父母去了城市生活，但每年夏天都会回到村子探望爷爷。然而，去年夏天，爷爷因病去世，小男孩伤心欲绝。今年夏天，他回到村子，来到湖边，想起和爷爷的美好回忆，忍不住哭泣。他将爷爷的帽子放在湖边的石头上，希望能让爷爷的在天之灵得到安慰。那晚的哭泣声正是小男孩在祭莫他亲爱的爷爷。\n", "\n", "参与者提出的问题: 死者受伤了吗\n", "<|im_end|>\n", "<|im_start|>assistant\n", "不是<|im_end|>\n", "--------------------------------------------------\n", "prompt: <|im_start|>system\n", "You are an expert in logical reasoning.<|im_end|>\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: 在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着一顶破旧的帽子，但没有人知道这顶帽子是从哪里来的，哭泣声又是为何。请还原故事真相。\n", "\n", "实际情况: 原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖中的海龟是他们的朋友。后来，小男孩随父母去了城市生活，但每年夏天都会回到村子探望爷爷。然而，去年夏天，爷爷因病去世，小男孩伤心欲绝。今年夏天，他回到村子，来到湖边，想起和爷爷的美好回忆，忍不住哭泣。他将爷爷的帽子放在湖边的石头上，希望能让爷爷的在天之灵得到安慰。那晚的哭泣声正是小男孩在祭莫他亲爱的爷爷。\n", "\n", "参与者提出的问题: 死者受伤了吗\n", "<|im_end|>\n", "<|im_start|>assistant\n", "\n", "Evaluating model: internlm/internlm2_5-7b-chat-1m\n", " 0%| | 0/3000 [00:00\n", "--------\n", "step 2: 不是\n", "--------\n", "step 3: 不是\n", "--------\n", "step 4: 不是\n", "--------\n", "step 5: 不是\n", "100%|███████████████████████████████████████| 3000/3000 [46:22<00:00, 1.08it/s]\n", "(3) GPU = NVIDIA GeForce RTX 4080 Laptop GPU. Max memory = 11.994 GB.\n", "5.887 GB of memory reserved.\n", " text ... internlm/internlm2_5-7b-chat-1m_checkpoint-2810\n", "0 甄加索是自杀吗 ... 不是\n", "\n", "[1 rows x 11 columns]\n", "{'accuracy': 0.6846666666666666, 'incorrect_ids': [6, 10, 12, 16, 17, 24, 25, 27, 28, 31, 34, 35, 36, 37, 38, 55, 57, 58, 61, 63, 65, 66, 67, 70, 77, 78, 79, 81, 82, 83, 84, 88, 93, 94, 96, 103, 104, 106, 108, 109, 112, 115, 117, 120, 121, 123, 128, 135, 137, 138, 139, 143, 149, 150, 153, 155, 159, 161, 163, 164, 172, 173, 179, 180, 190, 193, 199, 200, 202, 206, 207, 216, 218, 224, 225, 227, 228, 234, 236, 240, 243, 245, 248, 250, 251, 252, 253, 259, 260, 261, 262, 263, 265, 266, 271, 275, 276, 286, 288, 292, 295, 296, 299, 303, 304, 308, 314, 315, 317, 318, 320, 321, 323, 328, 332, 334, 335, 337, 338, 342, 350, 352, 353, 354, 355, 356, 357, 358, 360, 361, 362, 364, 368, 370, 371, 372, 373, 374, 376, 377, 383, 386, 389, 395, 396, 397, 398, 403, 404, 411, 414, 416, 423, 428, 429, 430, 438, 442, 445, 447, 450, 452, 454, 456, 457, 458, 465, 467, 468, 471, 472, 473, 476, 478, 482, 486, 488, 490, 492, 493, 494, 495, 497, 501, 502, 504, 506, 507, 508, 510, 511, 512, 513, 514, 515, 517, 519, 520, 526, 530, 531, 536, 555, 556, 560, 561, 566, 568, 569, 570, 571, 579, 581, 585, 589, 591, 593, 596, 597, 601, 612, 613, 614, 621, 625, 626, 628, 632, 636, 643, 644, 647, 649, 651, 656, 657, 663, 665, 666, 671, 678, 682, 683, 686, 690, 695, 701, 702, 708, 709, 716, 720, 721, 727, 729, 730, 734, 735, 739, 740, 741, 747, 748, 754, 758, 760, 763, 766, 769, 771, 772, 774, 778, 785, 790, 798, 799, 800, 801, 805, 809, 810, 812, 813, 818, 819, 820, 821, 822, 823, 824, 828, 833, 834, 837, 840, 843, 847, 851, 856, 857, 861, 866, 869, 870, 875, 876, 878, 884, 886, 889, 890, 891, 894, 897, 899, 901, 904, 906, 910, 913, 917, 920, 924, 927, 930, 932, 935, 937, 940, 942, 943, 945, 952, 954, 956, 962, 964, 966, 968, 969, 970, 972, 980, 981, 982, 986, 991, 993, 994, 998, 1001, 1003, 1004, 1006, 1011, 1012, 1014, 1015, 1021, 1022, 1025, 1028, 1031, 1040, 1043, 1046, 1051, 1053, 1054, 1066, 1073, 1075, 1076, 1078, 1080, 1084, 1087, 1089, 1096, 1108, 1111, 1114, 1116, 1118, 1120, 1121, 1124, 1125, 1126, 1129, 1153, 1158, 1161, 1163, 1166, 1170, 1172, 1173, 1174, 1177, 1178, 1180, 1181, 1184, 1185, 1196, 1203, 1212, 1232, 1234, 1237, 1239, 1240, 1241, 1242, 1251, 1252, 1254, 1256, 1258, 1259, 1262, 1266, 1267, 1274, 1282, 1289, 1305, 1306, 1308, 1313, 1315, 1317, 1323, 1331, 1342, 1349, 1353, 1357, 1362, 1363, 1364, 1367, 1370, 1373, 1382, 1385, 1386, 1387, 1388, 1389, 1391, 1392, 1393, 1395, 1399, 1404, 1406, 1407, 1409, 1420, 1424, 1425, 1426, 1427, 1430, 1433, 1440, 1441, 1443, 1444, 1445, 1446, 1448, 1451, 1452, 1453, 1455, 1459, 1462, 1467, 1469, 1473, 1476, 1477, 1478, 1487, 1490, 1493, 1494, 1495, 1496, 1499, 1504, 1510, 1512, 1517, 1518, 1519, 1525, 1526, 1537, 1542, 1545, 1546, 1547, 1558, 1562, 1563, 1576, 1581, 1585, 1586, 1588, 1590, 1592, 1593, 1603, 1604, 1605, 1606, 1612, 1613, 1622, 1627, 1628, 1629, 1631, 1632, 1633, 1635, 1637, 1641, 1645, 1647, 1648, 1650, 1651, 1653, 1654, 1655, 1656, 1658, 1659, 1665, 1668, 1672, 1674, 1675, 1676, 1679, 1685, 1686, 1690, 1691, 1694, 1695, 1717, 1718, 1726, 1727, 1728, 1731, 1734, 1735, 1736, 1737, 1738, 1751, 1753, 1754, 1755, 1756, 1757, 1758, 1768, 1769, 1770, 1774, 1778, 1780, 1784, 1786, 1791, 1792, 1796, 1799, 1804, 1806, 1809, 1812, 1814, 1816, 1818, 1820, 1822, 1827, 1834, 1835, 1839, 1840, 1846, 1848, 1849, 1852, 1858, 1860, 1869, 1870, 1872, 1879, 1882, 1888, 1890, 1912, 1914, 1931, 1932, 1934, 1935, 1953, 1955, 1958, 1961, 1962, 1963, 1964, 1973, 1978, 1979, 1981, 1983, 1984, 1986, 1988, 1989, 1990, 1994, 1995, 1996, 1998, 2001, 2002, 2005, 2008, 2009, 2014, 2015, 2021, 2025, 2028, 2035, 2038, 2042, 2044, 2046, 2048, 2052, 2054, 2059, 2063, 2064, 2072, 2077, 2082, 2092, 2097, 2109, 2110, 2112, 2114, 2119, 2121, 2124, 2126, 2130, 2133, 2135, 2139, 2143, 2145, 2147, 2156, 2160, 2161, 2162, 2164, 2169, 2173, 2175, 2177, 2184, 2185, 2186, 2188, 2193, 2194, 2195, 2196, 2197, 2198, 2199, 2212, 2215, 2219, 2223, 2226, 2230, 2231, 2235, 2238, 2240, 2245, 2247, 2250, 2253, 2254, 2256, 2261, 2262, 2265, 2267, 2272, 2274, 2276, 2285, 2286, 2290, 2293, 2297, 2304, 2311, 2312, 2313, 2314, 2319, 2322, 2324, 2330, 2333, 2339, 2340, 2344, 2348, 2349, 2351, 2355, 2357, 2359, 2360, 2361, 2364, 2366, 2372, 2378, 2379, 2385, 2388, 2395, 2400, 2404, 2406, 2409, 2410, 2412, 2414, 2423, 2424, 2429, 2433, 2437, 2440, 2441, 2442, 2444, 2445, 2446, 2448, 2461, 2463, 2464, 2465, 2469, 2475, 2476, 2488, 2493, 2495, 2496, 2498, 2499, 2501, 2503, 2512, 2517, 2520, 2522, 2525, 2526, 2529, 2530, 2532, 2535, 2538, 2540, 2546, 2547, 2548, 2549, 2554, 2555, 2556, 2557, 2559, 2560, 2562, 2563, 2564, 2565, 2566, 2567, 2574, 2575, 2577, 2589, 2590, 2601, 2606, 2607, 2610, 2616, 2617, 2620, 2622, 2623, 2624, 2626, 2628, 2629, 2635, 2636, 2640, 2644, 2645, 2649, 2652, 2660, 2663, 2664, 2667, 2670, 2672, 2676, 2678, 2682, 2691, 2694, 2709, 2714, 2716, 2719, 2721, 2730, 2731, 2735, 2736, 2742, 2744, 2749, 2756, 2757, 2760, 2761, 2762, 2764, 2766, 2767, 2770, 2776, 2777, 2781, 2788, 2795, 2798, 2803, 2806, 2807, 2811, 2814, 2815, 2816, 2820, 2821, 2822, 2831, 2833, 2834, 2837, 2842, 2843, 2846, 2857, 2858, 2861, 2864, 2867, 2871, 2877, 2882, 2884, 2888, 2899, 2902, 2903, 2905, 2912, 2913, 2915, 2916, 2921, 2926, 2933, 2937, 2938, 2942, 2949, 2952, 2953, 2955, 2957, 2960, 2963, 2964, 2968, 2975, 2976, 2977, 2978, 2979, 2980, 2985, 2990, 2991, 2995, 2998]}\n", "Epoch 6\n", "loading env vars from: /home/inflaton/code/projects/courses/logical-reasoning/.env\n", "Adding /home/inflaton/code/projects/courses/logical-reasoning to sys.path\n", "loading /home/inflaton/code/projects/courses/logical-reasoning/llm_toolkit/logical_reasoning_utils.py\n", "internlm/internlm2_5-7b-chat-1m llama-factory/saves/internlm2_5_7b/lora/sft/checkpoint-3372 True datasets/mgtv results/mgtv-results_nv4080_r2.csv\n", "(1) GPU = NVIDIA GeForce RTX 4080 Laptop GPU. Max memory = 11.994 GB.\n", "0.0 GB of memory reserved.\n", "loading model: internlm/internlm2_5-7b-chat-1m\n", "[INFO|tokenization_utils_base.py:2161] 2024-07-11 03:40:56,840 >> 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] 2024-07-11 03:40:56,840 >> loading file added_tokens.json from cache at None\n", "[INFO|tokenization_utils_base.py:2161] 2024-07-11 03:40:56,841 >> 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] 2024-07-11 03:40:56,841 >> 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] 2024-07-11 03:40:56,841 >> loading file tokenizer.json from cache at None\n", "07/11/2024 03:40:58 - INFO - llamafactory.data.template - Replace eos token: <|im_end|>\n", "07/11/2024 03:40:58 - INFO - llamafactory.data.template - Add <|im_start|> to stop words.\n", "[INFO|configuration_utils.py:733] 2024-07-11 03:40:58,927 >> 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] 2024-07-11 03:41:00,357 >> 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] 2024-07-11 03:41:00,357 >> 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", "07/11/2024 03:41:00 - INFO - llamafactory.model.model_utils.quantization - Quantizing model to 4 bit with bitsandbytes.\n", "07/11/2024 03:41:00 - INFO - llamafactory.model.patcher - Using KV cache for faster generation.\n", "[INFO|modeling_utils.py:3556] 2024-07-11 03:41:01,051 >> 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] 2024-07-11 03:41:01,208 >> Instantiating InternLM2ForCausalLM model under default dtype torch.bfloat16.\n", "[INFO|configuration_utils.py:1000] 2024-07-11 03:41:01,209 >> 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 [07:04<00:00, 53.00s/it]\n", "[INFO|modeling_utils.py:4364] 2024-07-11 03:48:05,380 >> All model checkpoint weights were used when initializing InternLM2ForCausalLM.\n", "\n", "[INFO|modeling_utils.py:4372] 2024-07-11 03:48:05,380 >> 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] 2024-07-11 03:48:05,983 >> 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] 2024-07-11 03:48:05,983 >> 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 03:48:06 - INFO - llamafactory.model.model_utils.attention - Using vanilla attention implementation.\n", "07/11/2024 03:48:06 - INFO - llamafactory.model.adapter - Loaded adapter(s): llama-factory/saves/internlm2_5_7b/lora/sft/checkpoint-3372\n", "07/11/2024 03:48:06 - INFO - llamafactory.model.loader - all params: 7,756,582,912\n", "(2) GPU = NVIDIA GeForce RTX 4080 Laptop GPU. Max memory = 11.994 GB.\n", "4.895 GB of memory reserved.\n", "loading train/test data files\n", "DatasetDict({\n", " train: Dataset({\n", " features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n", " num_rows: 25000\n", " })\n", " test: Dataset({\n", " features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth', 'train_text', 'prompt'],\n", " num_rows: 3000\n", " })\n", "})\n", "--------------------------------------------------\n", "text: 甄加索是自杀吗\n", "--------------------------------------------------\n", "label: 不是\n", "--------------------------------------------------\n", "answer: nan\n", "--------------------------------------------------\n", "title: 海岸之谜\n", "--------------------------------------------------\n", "puzzle: 在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任何打斗的迹象。请问甄加索的死因是什么？\n", "--------------------------------------------------\n", "truth: 甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在创作一幅描绘海洋生物的画作。在画即将完成的前一天晚上，他骑着自行车外出，打算在海边观赏夜景。然而，他在沙滩上意外发现了一只搁浅的海豚，为了救助这只海豚，他耗费了极大的体力，最终成功将其送回海中。筋疲力尽的甄加索在沙滩上睡着了，由于他患有严重的心脏病，却未告知旁人，在寒冷的海风中，他的心脏停止了跳动。因此，警方在现场只发现了车轮痕迹和未完成的画作，而没有发现任何他杀的迹象。\n", "--------------------------------------------------\n", "train_text: <|im_start|>system\n", "You are an expert in logical reasoning.<|im_end|>\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: 在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任何打斗的迹象。请问甄加索的死因是什么？\n", "\n", "实际情况: 甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在创作一幅描绘海洋生物的画作。在画即将完成的前一天晚上，他骑着自行车外出，打算在海边观赏夜景。然而，他在沙滩上意外发现了一只搁浅的海豚，为了救助这只海豚，他耗费了极大的体力，最终成功将其送回海中。筋疲力尽的甄加索在沙滩上睡着了，由于他患有严重的心脏病，却未告知旁人，在寒冷的海风中，他的心脏停止了跳动。因此，警方在现场只发现了车轮痕迹和未完成的画作，而没有发现任何他杀的迹象。\n", "\n", "参与者提出的问题: 甄加索是自杀吗\n", "<|im_end|>\n", "<|im_start|>assistant\n", "不是<|im_end|>\n", "--------------------------------------------------\n", "prompt: <|im_start|>system\n", "You are an expert in logical reasoning.<|im_end|>\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: 在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任何打斗的迹象。请问甄加索的死因是什么？\n", "\n", "实际情况: 甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在创作一幅描绘海洋生物的画作。在画即将完成的前一天晚上，他骑着自行车外出，打算在海边观赏夜景。然而，他在沙滩上意外发现了一只搁浅的海豚，为了救助这只海豚，他耗费了极大的体力，最终成功将其送回海中。筋疲力尽的甄加索在沙滩上睡着了，由于他患有严重的心脏病，却未告知旁人，在寒冷的海风中，他的心脏停止了跳动。因此，警方在现场只发现了车轮痕迹和未完成的画作，而没有发现任何他杀的迹象。\n", "\n", "参与者提出的问题: 甄加索是自杀吗\n", "<|im_end|>\n", "<|im_start|>assistant\n", "\n", "--------------------------------------------------\n", "text: 死者受伤了吗\n", "--------------------------------------------------\n", "label: 不是\n", "--------------------------------------------------\n", "answer: nan\n", "--------------------------------------------------\n", "title: 甄庄哭声\n", "--------------------------------------------------\n", "puzzle: 在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着一顶破旧的帽子，但没有人知道这顶帽子是从哪里来的，哭泣声又是为何。请还原故事真相。\n", "--------------------------------------------------\n", "truth: 原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖中的海龟是他们的朋友。后来，小男孩随父母去了城市生活，但每年夏天都会回到村子探望爷爷。然而，去年夏天，爷爷因病去世，小男孩伤心欲绝。今年夏天，他回到村子，来到湖边，想起和爷爷的美好回忆，忍不住哭泣。他将爷爷的帽子放在湖边的石头上，希望能让爷爷的在天之灵得到安慰。那晚的哭泣声正是小男孩在祭莫他亲爱的爷爷。\n", "--------------------------------------------------\n", "train_text: <|im_start|>system\n", "You are an expert in logical reasoning.<|im_end|>\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: 在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着一顶破旧的帽子，但没有人知道这顶帽子是从哪里来的，哭泣声又是为何。请还原故事真相。\n", "\n", "实际情况: 原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖中的海龟是他们的朋友。后来，小男孩随父母去了城市生活，但每年夏天都会回到村子探望爷爷。然而，去年夏天，爷爷因病去世，小男孩伤心欲绝。今年夏天，他回到村子，来到湖边，想起和爷爷的美好回忆，忍不住哭泣。他将爷爷的帽子放在湖边的石头上，希望能让爷爷的在天之灵得到安慰。那晚的哭泣声正是小男孩在祭莫他亲爱的爷爷。\n", "\n", "参与者提出的问题: 死者受伤了吗\n", "<|im_end|>\n", "<|im_start|>assistant\n", "不是<|im_end|>\n", "--------------------------------------------------\n", "prompt: <|im_start|>system\n", "You are an expert in logical reasoning.<|im_end|>\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: 在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着一顶破旧的帽子，但没有人知道这顶帽子是从哪里来的，哭泣声又是为何。请还原故事真相。\n", "\n", "实际情况: 原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖中的海龟是他们的朋友。后来，小男孩随父母去了城市生活，但每年夏天都会回到村子探望爷爷。然而，去年夏天，爷爷因病去世，小男孩伤心欲绝。今年夏天，他回到村子，来到湖边，想起和爷爷的美好回忆，忍不住哭泣。他将爷爷的帽子放在湖边的石头上，希望能让爷爷的在天之灵得到安慰。那晚的哭泣声正是小男孩在祭莫他亲爱的爷爷。\n", "\n", "参与者提出的问题: 死者受伤了吗\n", "<|im_end|>\n", "<|im_start|>assistant\n", "\n", "Evaluating model: internlm/internlm2_5-7b-chat-1m\n", " 0%| | 0/3000 [00:00\n", "--------\n", "step 2: 不是\n", "--------\n", "step 3: 不是\n", "--------\n", "step 4: 不是\n", "--------\n", "step 5: 不是\n", "100%|███████████████████████████████████████| 3000/3000 [46:52<00:00, 1.07it/s]\n", "(3) GPU = NVIDIA GeForce RTX 4080 Laptop GPU. Max memory = 11.994 GB.\n", "5.887 GB of memory reserved.\n", " text ... internlm/internlm2_5-7b-chat-1m_checkpoint-3372\n", "0 甄加索是自杀吗 ... 不是\n", "\n", "[1 rows x 12 columns]\n", "{'accuracy': 0.6683333333333333, 'incorrect_ids': [6, 10, 11, 12, 16, 17, 24, 25, 27, 28, 29, 31, 34, 35, 36, 37, 38, 55, 57, 58, 61, 63, 65, 66, 67, 70, 77, 78, 79, 81, 82, 83, 84, 88, 93, 94, 96, 103, 104, 106, 108, 109, 112, 113, 115, 117, 120, 121, 128, 131, 135, 137, 138, 139, 143, 149, 150, 153, 155, 159, 160, 161, 163, 164, 172, 173, 176, 179, 180, 190, 193, 199, 200, 202, 206, 207, 216, 218, 224, 225, 226, 227, 228, 230, 234, 236, 240, 241, 243, 245, 248, 250, 251, 252, 253, 259, 260, 261, 263, 265, 266, 271, 275, 276, 286, 288, 292, 295, 296, 299, 301, 303, 304, 308, 309, 311, 314, 315, 317, 318, 320, 321, 323, 328, 330, 332, 334, 335, 337, 338, 342, 350, 351, 352, 353, 354, 355, 356, 357, 358, 360, 362, 364, 368, 370, 371, 372, 373, 374, 376, 377, 383, 386, 389, 395, 396, 397, 398, 403, 404, 409, 411, 414, 416, 423, 428, 429, 430, 438, 442, 445, 447, 450, 452, 454, 456, 457, 458, 461, 465, 467, 468, 471, 472, 473, 476, 477, 478, 480, 482, 486, 488, 490, 492, 493, 494, 495, 497, 501, 502, 504, 506, 507, 508, 510, 511, 512, 513, 514, 515, 517, 519, 520, 526, 530, 531, 536, 555, 556, 560, 561, 566, 568, 569, 571, 579, 581, 585, 589, 591, 593, 596, 597, 599, 601, 603, 612, 613, 614, 621, 625, 626, 628, 632, 636, 643, 644, 647, 649, 651, 656, 657, 663, 665, 666, 671, 678, 682, 683, 686, 690, 695, 701, 702, 708, 709, 716, 718, 720, 721, 727, 729, 730, 734, 735, 739, 740, 741, 745, 747, 754, 758, 760, 763, 766, 769, 771, 772, 774, 778, 785, 790, 797, 798, 799, 800, 801, 805, 809, 810, 812, 813, 818, 819, 820, 821, 822, 823, 824, 828, 833, 834, 837, 840, 841, 843, 847, 851, 856, 857, 861, 866, 869, 870, 875, 876, 877, 878, 884, 886, 889, 890, 891, 894, 897, 899, 901, 904, 906, 910, 913, 917, 920, 924, 927, 929, 930, 932, 935, 937, 940, 942, 943, 945, 952, 954, 956, 962, 964, 966, 968, 969, 970, 972, 980, 981, 982, 986, 991, 993, 994, 998, 1001, 1003, 1004, 1006, 1007, 1011, 1012, 1014, 1015, 1021, 1022, 1025, 1028, 1031, 1040, 1041, 1043, 1046, 1051, 1053, 1054, 1066, 1073, 1075, 1076, 1078, 1080, 1082, 1084, 1087, 1089, 1096, 1108, 1111, 1114, 1116, 1118, 1120, 1121, 1124, 1125, 1126, 1129, 1153, 1158, 1160, 1161, 1163, 1166, 1167, 1170, 1172, 1173, 1174, 1177, 1178, 1180, 1181, 1185, 1196, 1212, 1232, 1237, 1239, 1240, 1241, 1242, 1251, 1252, 1254, 1256, 1258, 1259, 1262, 1266, 1267, 1274, 1282, 1289, 1305, 1306, 1308, 1313, 1315, 1317, 1323, 1331, 1332, 1339, 1340, 1342, 1349, 1353, 1357, 1362, 1363, 1364, 1367, 1370, 1373, 1382, 1385, 1386, 1387, 1388, 1389, 1391, 1392, 1393, 1395, 1399, 1400, 1404, 1406, 1407, 1409, 1420, 1424, 1425, 1426, 1427, 1430, 1433, 1440, 1441, 1443, 1444, 1445, 1446, 1448, 1451, 1452, 1453, 1455, 1459, 1460, 1462, 1463, 1467, 1469, 1473, 1476, 1477, 1478, 1487, 1490, 1493, 1494, 1495, 1496, 1499, 1504, 1510, 1512, 1517, 1518, 1519, 1525, 1526, 1537, 1542, 1545, 1546, 1547, 1558, 1560, 1562, 1563, 1576, 1581, 1585, 1586, 1588, 1590, 1592, 1593, 1603, 1604, 1605, 1606, 1612, 1613, 1617, 1622, 1627, 1628, 1629, 1631, 1632, 1633, 1635, 1637, 1638, 1641, 1645, 1647, 1648, 1650, 1651, 1653, 1654, 1655, 1658, 1659, 1662, 1668, 1672, 1674, 1675, 1676, 1677, 1679, 1685, 1686, 1691, 1694, 1695, 1717, 1718, 1726, 1727, 1728, 1731, 1734, 1735, 1736, 1737, 1738, 1751, 1753, 1754, 1755, 1756, 1757, 1758, 1766, 1768, 1769, 1770, 1774, 1778, 1780, 1784, 1786, 1789, 1791, 1792, 1796, 1799, 1804, 1806, 1809, 1811, 1812, 1814, 1816, 1818, 1820, 1822, 1827, 1834, 1835, 1839, 1840, 1846, 1848, 1849, 1852, 1855, 1858, 1860, 1869, 1870, 1872, 1879, 1882, 1888, 1890, 1912, 1914, 1931, 1932, 1934, 1935, 1950, 1953, 1955, 1958, 1961, 1962, 1963, 1964, 1973, 1978, 1979, 1981, 1983, 1984, 1986, 1988, 1989, 1990, 1994, 1995, 1996, 1998, 2001, 2002, 2005, 2007, 2008, 2009, 2014, 2015, 2021, 2025, 2028, 2035, 2037, 2038, 2042, 2046, 2048, 2052, 2053, 2054, 2059, 2063, 2064, 2072, 2077, 2082, 2092, 2097, 2107, 2109, 2110, 2112, 2114, 2119, 2121, 2124, 2126, 2130, 2133, 2135, 2139, 2143, 2145, 2147, 2156, 2160, 2161, 2162, 2164, 2169, 2173, 2175, 2176, 2177, 2184, 2185, 2186, 2188, 2192, 2193, 2194, 2195, 2196, 2197, 2198, 2199, 2203, 2212, 2215, 2219, 2223, 2226, 2230, 2231, 2235, 2238, 2240, 2245, 2247, 2250, 2253, 2254, 2256, 2261, 2262, 2265, 2267, 2272, 2274, 2276, 2282, 2285, 2286, 2290, 2293, 2297, 2304, 2311, 2312, 2313, 2314, 2319, 2322, 2324, 2330, 2333, 2339, 2340, 2344, 2348, 2349, 2355, 2357, 2359, 2360, 2361, 2364, 2366, 2372, 2378, 2379, 2385, 2388, 2389, 2395, 2400, 2404, 2406, 2408, 2409, 2410, 2412, 2414, 2423, 2424, 2429, 2433, 2437, 2440, 2441, 2442, 2444, 2445, 2446, 2448, 2461, 2463, 2464, 2465, 2469, 2475, 2476, 2477, 2488, 2493, 2495, 2496, 2498, 2499, 2501, 2503, 2512, 2517, 2520, 2522, 2525, 2526, 2529, 2530, 2532, 2535, 2538, 2539, 2540, 2546, 2547, 2548, 2549, 2553, 2554, 2555, 2556, 2557, 2559, 2560, 2562, 2563, 2564, 2565, 2566, 2574, 2575, 2577, 2589, 2590, 2592, 2601, 2606, 2607, 2610, 2616, 2617, 2620, 2622, 2623, 2624, 2626, 2628, 2629, 2635, 2636, 2640, 2644, 2645, 2649, 2652, 2660, 2663, 2664, 2667, 2670, 2672, 2676, 2678, 2681, 2682, 2691, 2694, 2709, 2714, 2716, 2719, 2721, 2730, 2731, 2735, 2736, 2742, 2744, 2756, 2757, 2760, 2761, 2762, 2764, 2766, 2767, 2770, 2776, 2777, 2781, 2788, 2795, 2798, 2801, 2803, 2806, 2807, 2811, 2814, 2815, 2816, 2820, 2821, 2822, 2831, 2833, 2834, 2837, 2842, 2843, 2846, 2857, 2858, 2861, 2864, 2867, 2871, 2877, 2882, 2884, 2888, 2891, 2899, 2902, 2903, 2905, 2912, 2913, 2915, 2916, 2921, 2933, 2937, 2938, 2942, 2949, 2952, 2953, 2955, 2957, 2960, 2963, 2964, 2968, 2975, 2976, 2977, 2978, 2979, 2980, 2983, 2985, 2991, 2995, 2998, 2999]}\n", "CPU times: user 23min 56s, sys: 8min 54s, total: 32min 51s\n", "Wall time: 5h 23min 19s\n" ] } ], "source": [ "%%time\n", "\n", "evaluate_model_all_epochs(\"internlm/internlm2_5-7b-chat-1m\", \"llama-factory/saves/internlm2_5_7b/lora/sft\", 6, start_epoch=1, load_in_4bit=True, 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": "T4", "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" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "036fc5746f43416db18c19ad8fd36677": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "06e806c82c7b4cbea31c5358dd9c3434": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "087b76a8b7514269b1f0ab29b062e444": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_a069d2ab23824f29aa320ac256e2cfe9", "placeholder": "​", "style": "IPY_MODEL_06e806c82c7b4cbea31c5358dd9c3434", "value": "Map (num_proc=2): 100%" } }, "09b76013aa9e45efb6deb23a7a0d0925": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_dea41c5260884aa6879b5e1d1697b14f", "placeholder": "​", "style": "IPY_MODEL_89965917796a4f81b899fdc7685f33df", "value": "config.json: 100%" } }, "0a92c56bfa134ef583220d7ef0b13e17": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "0c34be936c8145d3ab41282f30a70713": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "0f8b6bfe16894500838793f2491d403f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "177c78fce95d4b4ab33057c5a048d693": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "1f44c9ce1adf470cbb19784493ed209f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_0c34be936c8145d3ab41282f30a70713", "placeholder": "​", "style": "IPY_MODEL_0a92c56bfa134ef583220d7ef0b13e17", "value": "model.safetensors: 100%" } }, "201b59ccd9f845e197029b57e424aefc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "2157f01726d748f8a9ae4a00664430da": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "21db8a77b00d4a4e82fdfa608657531f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "26e4202cca81496a90d15a0dd4ca9cf1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_ba90fdb8822d47dab7ba203bee297f37", "IPY_MODEL_61560ff6a36b44f4a9dfdae5c52791d4", "IPY_MODEL_95fbe66647904c06a20f640630d6dc0e" ], "layout": "IPY_MODEL_57182a263d324a3dbf1471c74290a0d5" } }, "27155728b6b84cb199c91c940095d0a8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_6b91feeed5464877991ac2c207aebe7c", "IPY_MODEL_cca8113c54c0495daedce1327bf9c68b", "IPY_MODEL_2e63a29e2f7247bba5beede9a568c99f" ], "layout": "IPY_MODEL_5c9d781c28944f3eb86e2a6d44efdf18" } }, "271ddaa553a042d09b6db7b450643d8f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "2a58d04b428c46f4b3dbadd3bc6cd529": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "2d18ddf6482c4d97829ac0e5a7b9868f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_9f679ad3ec7f4fe8ad0510ffb57bc2ab", "IPY_MODEL_f2df530d22c74977b249dd9fb5f4829b", "IPY_MODEL_89b2ef0dbfea47ab8e6f8d659e3351d1" ], "layout": "IPY_MODEL_3056b148aa9f4e6e8aa3b61d26886255" } }, "2e5087c76f98437cb5dc729230358cba": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "2e63a29e2f7247bba5beede9a568c99f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_b993eaec6b224440bf80c0958c6fb536", "placeholder": "​", "style": "IPY_MODEL_de868e26e7154f62aa86223a539ad421", "value": " 464/464 [00:00<00:00, 27.1kB/s]" } }, "2f6c70dd266c4816bfad3fd3d192929a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "30307300bc4e4baf96560e30969a82b6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_e36a3f9eff0e4cf68834d66b0213ae96", "placeholder": "​", "style": "IPY_MODEL_a0037bdccf254159becde630bee3d1db", "value": "generation_config.json: 100%" } }, "3056b148aa9f4e6e8aa3b61d26886255": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "30cdc32298134cb0be4d41615b9e5774": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "3572201bd4d74a58b7a665f9bdfdcdba": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "35b0e8c26d6640e9bd0ed7b242a423d8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_2e5087c76f98437cb5dc729230358cba", "max": 51760, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_036fc5746f43416db18c19ad8fd36677", "value": 51760 } }, "36166c7bcb854b34aca1f41a5d6ea50b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "370692d819df41828b48c4ad446f977b": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "39b29a75374b45c0a22506010be2b84e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_30cdc32298134cb0be4d41615b9e5774", "max": 1179, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_47928317548c454bba6358ab132e8dee", "value": 1179 } }, "3cf2dd993b5e4d3daecf61e4bab5a404": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_087b76a8b7514269b1f0ab29b062e444", "IPY_MODEL_35b0e8c26d6640e9bd0ed7b242a423d8", "IPY_MODEL_54ad89e05fd74576b9b8b5b5a10eaf8d" ], "layout": "IPY_MODEL_a41dc44766444a998bec2d777f249d23" } }, "43dec2ede91341f5af60eb522e18e984": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "4463edd481c1467f914c7dcd6c6e6ffc": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "47928317548c454bba6358ab132e8dee": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "49277aeeac16434a865a4d12308b1abc": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "4ae7e449e4ea4c729b5f34607c18ebae": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "4b2061b8a73c43ffb0c2f83daf0d0183": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "4c4c88d4c701450692fa0f6b0c5764b0": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "4c666f4ace3943f8b80ecd20e7503236": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "4ccedf0d93094e63b57a0f8a434fba06": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_4463edd481c1467f914c7dcd6c6e6ffc", "max": 44307561, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_6d3b9a05db0b4dadb638c686faa0c40a", "value": 44307561 } }, "4dcf6ff672d24983a1877a8431709aa9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_5807d5fb827d490fb3bc698f801ffff5", "placeholder": "​", "style": "IPY_MODEL_c4f2b06a82fd4987b8b659524a7b503b", "value": "Generating train split: 100%" } }, "4ea63adfce694725bdba878aef709dd3": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "5234566b1bfc4655b8d582ea5b46ed9f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "54ad89e05fd74576b9b8b5b5a10eaf8d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_fdb1941405ed4e4aa06019933892deb3", "placeholder": "​", "style": "IPY_MODEL_668d5377ca56426a99753867e6e24862", "value": " 51760/51760 [01:02<00:00, 1131.51 examples/s]" } }, "56aee4853b7740e6a977254f5d1fa66d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "57182a263d324a3dbf1471c74290a0d5": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "5807d5fb827d490fb3bc698f801ffff5": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "5c9d781c28944f3eb86e2a6d44efdf18": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "5f40db8173dd4d76b6ef5ed6d9ec8b6e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "61560ff6a36b44f4a9dfdae5c52791d4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_db19fc8d37db4e45a5790a876836d8c4", "max": 11610, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_36166c7bcb854b34aca1f41a5d6ea50b", "value": 11610 } }, "6578fd7acdb54c4c93528ea431fd0144": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_370692d819df41828b48c4ad446f977b", "placeholder": "​", "style": "IPY_MODEL_a0bf9160eb2647409b3200270914b90f", "value": " 50.6k/50.6k [00:00<00:00, 2.71MB/s]" } }, "668d5377ca56426a99753867e6e24862": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "697f027529b54ee9956bae78a11e0611": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "69ac12aec0714318bf2c83d4f4e745f5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "6b2012c3f88547af8884a9ea90e3164b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_938f45f1b3e24118b815d96ae34ba86a", "placeholder": "​", "style": "IPY_MODEL_9367047a800747f79c6b225d92397846", "value": " 44.3M/44.3M [00:01<00:00, 31.0MB/s]" } }, "6b91feeed5464877991ac2c207aebe7c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_4b2061b8a73c43ffb0c2f83daf0d0183", "placeholder": "​", "style": "IPY_MODEL_69ac12aec0714318bf2c83d4f4e745f5", "value": "special_tokens_map.json: 100%" } }, "6d3b9a05db0b4dadb638c686faa0c40a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "6dbbedeca9314e66ae50e44ffa31a414": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "6e34619b45934040b6092e6fb01ea7fe": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "71ce208e20d6483abb9ed923510c86d7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_d69dc491b3ab44d7852b21873ed7bb7f", "placeholder": "​", "style": "IPY_MODEL_f401d53bf28e44eb906bce6c05412662", "value": " 51760/51760 [00:01<00:00, 45512.81 examples/s]" } }, "7358cdad832342c983e31efb8754ab78": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "73e352a3404f4c7dad0737f57d29e92f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_988a0e8c1f89446086858da0a891a79c", "IPY_MODEL_4ccedf0d93094e63b57a0f8a434fba06", "IPY_MODEL_6b2012c3f88547af8884a9ea90e3164b" ], "layout": "IPY_MODEL_7e29cb8dd4df4d5b94407cd8fd3f2011" } }, "74501720ac7e4dbb911a4a99b3633bc6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "78e5400bff924a92a4cc61c4ff18b182": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_b9b313fd861948f5aba25b24b1518d30", "placeholder": "​", "style": "IPY_MODEL_4c666f4ace3943f8b80ecd20e7503236", "value": " 1.18k/1.18k [00:00<00:00, 31.3kB/s]" } }, "7975adbc2ec5489ea7fa0167e620d85c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_6e34619b45934040b6092e6fb01ea7fe", "max": 51760, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_271ddaa553a042d09b6db7b450643d8f", "value": 51760 } }, "7e29cb8dd4df4d5b94407cd8fd3f2011": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "810ff6c0e17d4fa09a30fef27eacff90": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "89965917796a4f81b899fdc7685f33df": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "89b2ef0dbfea47ab8e6f8d659e3351d1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_b8908fa0df3743ecb9d12983a739104f", "placeholder": "​", "style": "IPY_MODEL_177c78fce95d4b4ab33057c5a048d693", "value": " 9.09M/9.09M [00:00<00:00, 32.6MB/s]" } }, "8b3505352a5a42bf910428c40ce40465": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_49277aeeac16434a865a4d12308b1abc", "placeholder": "​", "style": "IPY_MODEL_2157f01726d748f8a9ae4a00664430da", "value": " 5.70G/5.70G [01:02<00:00, 30.1MB/s]" } }, "8fc142b628fb40568730234de1cafde2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_4ae7e449e4ea4c729b5f34607c18ebae", "max": 172, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_3572201bd4d74a58b7a665f9bdfdcdba", "value": 172 } }, "9367047a800747f79c6b225d92397846": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "938f45f1b3e24118b815d96ae34ba86a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "95fbe66647904c06a20f640630d6dc0e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_b0a370dc20654b279b9680692e34418e", "placeholder": "​", "style": "IPY_MODEL_cfeb365ddf7548d58b2557f22737fcf5", "value": " 11.6k/11.6k [00:00<00:00, 716kB/s]" } }, "988a0e8c1f89446086858da0a891a79c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_ad2be500fc164c0f86f33e914ef8e6a0", "placeholder": "​", "style": "IPY_MODEL_5234566b1bfc4655b8d582ea5b46ed9f", "value": "Downloading data: 100%" } }, "98c58f23f4d549518832cb2d18f796e8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_09b76013aa9e45efb6deb23a7a0d0925", "IPY_MODEL_39b29a75374b45c0a22506010be2b84e", "IPY_MODEL_78e5400bff924a92a4cc61c4ff18b182" ], "layout": "IPY_MODEL_2a58d04b428c46f4b3dbadd3bc6cd529" } }, "99fdbb0300c14c139d1937c646f0cfe7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_7358cdad832342c983e31efb8754ab78", "placeholder": "​", "style": "IPY_MODEL_e9adf418296e436fb48bb9f78885598b", "value": " 51760/51760 [00:01<00:00, 38665.95 examples/s]" } }, "9f679ad3ec7f4fe8ad0510ffb57bc2ab": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_4ea63adfce694725bdba878aef709dd3", "placeholder": "​", "style": "IPY_MODEL_74501720ac7e4dbb911a4a99b3633bc6", "value": "tokenizer.json: 100%" } }, "a0037bdccf254159becde630bee3d1db": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "a069d2ab23824f29aa320ac256e2cfe9": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "a0bf9160eb2647409b3200270914b90f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "a41dc44766444a998bec2d777f249d23": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "a8464a4c711e4e00aafdfc919b60d07e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_fb995c740590427b882572c81d4e848c", "placeholder": "​", "style": "IPY_MODEL_201b59ccd9f845e197029b57e424aefc", "value": " 172/172 [00:00<00:00, 12.0kB/s]" } }, "a9f0cc51fc3d4d7b874c32dcf1c5bdf2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "ad2be500fc164c0f86f33e914ef8e6a0": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "b0240cd9a4554b29ae11f8051984a1c6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_edaf890370314a218f138015faa0b05d", "placeholder": "​", "style": "IPY_MODEL_697f027529b54ee9956bae78a11e0611", "value": "Map: 100%" } }, "b0a370dc20654b279b9680692e34418e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "b518dcee69074b87be73957cd810e7ed": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_d891f8d0b1fc462f8008d02bb2a15692", "placeholder": "​", "style": "IPY_MODEL_cced8fd7e998472794f3f3e3018956a5", "value": "tokenizer_config.json: 100%" } }, "b8908fa0df3743ecb9d12983a739104f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "b993eaec6b224440bf80c0958c6fb536": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "b9b313fd861948f5aba25b24b1518d30": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "ba90fdb8822d47dab7ba203bee297f37": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_0f8b6bfe16894500838793f2491d403f", "placeholder": "​", "style": "IPY_MODEL_bb19f6c747754682a514373a3a0535ba", "value": "Downloading readme: 100%" } }, "bb19f6c747754682a514373a3a0535ba": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "bc883d4cf13e4f8b8a4fe5f410cb6efd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_e9159e03e61f4f56978ece9c3bca49b2", "max": 51760, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_810ff6c0e17d4fa09a30fef27eacff90", "value": 51760 } }, "c161d94df0f04feba9542237e0856c22": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "c22f71b1f85843209d7e5321506b9cb9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_1f44c9ce1adf470cbb19784493ed209f", "IPY_MODEL_f1addc4479d849879e743cf9089e6540", "IPY_MODEL_8b3505352a5a42bf910428c40ce40465" ], "layout": "IPY_MODEL_4c4c88d4c701450692fa0f6b0c5764b0" } }, "c4f2b06a82fd4987b8b659524a7b503b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "cca8113c54c0495daedce1327bf9c68b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_e02f9b7849c64531835eb77b860d1c93", "max": 464, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_56aee4853b7740e6a977254f5d1fa66d", "value": 464 } }, "cced8fd7e998472794f3f3e3018956a5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "cf245afeb1c04f29a24d291608c3d157": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_b518dcee69074b87be73957cd810e7ed", "IPY_MODEL_e29104486d594b2992d7285e0ef77371", "IPY_MODEL_6578fd7acdb54c4c93528ea431fd0144" ], "layout": "IPY_MODEL_d35db8148a354c56aaac56dbae22536f" } }, "cfe8cae0e22b495bafa221a63d13b283": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "cfeb365ddf7548d58b2557f22737fcf5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "d1b47d39450d4019ae85c9b2f943eeaf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_4dcf6ff672d24983a1877a8431709aa9", "IPY_MODEL_7975adbc2ec5489ea7fa0167e620d85c", "IPY_MODEL_71ce208e20d6483abb9ed923510c86d7" ], "layout": "IPY_MODEL_cfe8cae0e22b495bafa221a63d13b283" } }, "d35db8148a354c56aaac56dbae22536f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "d69dc491b3ab44d7852b21873ed7bb7f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "d891f8d0b1fc462f8008d02bb2a15692": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "d8e5318cead340c4adbeaccc05d39225": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "daf4cd890b35422683d22fd30bc71e83": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_b0240cd9a4554b29ae11f8051984a1c6", "IPY_MODEL_bc883d4cf13e4f8b8a4fe5f410cb6efd", "IPY_MODEL_99fdbb0300c14c139d1937c646f0cfe7" ], "layout": "IPY_MODEL_c161d94df0f04feba9542237e0856c22" } }, "db19fc8d37db4e45a5790a876836d8c4": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "de868e26e7154f62aa86223a539ad421": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "dea41c5260884aa6879b5e1d1697b14f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "e02f9b7849c64531835eb77b860d1c93": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "e29104486d594b2992d7285e0ef77371": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_a9f0cc51fc3d4d7b874c32dcf1c5bdf2", "max": 50641, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_2f6c70dd266c4816bfad3fd3d192929a", "value": 50641 } }, "e36a3f9eff0e4cf68834d66b0213ae96": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "e9159e03e61f4f56978ece9c3bca49b2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "e9adf418296e436fb48bb9f78885598b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "edaf890370314a218f138015faa0b05d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "f1addc4479d849879e743cf9089e6540": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_43dec2ede91341f5af60eb522e18e984", "max": 5702746405, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_d8e5318cead340c4adbeaccc05d39225", "value": 5702746405 } }, "f2df530d22c74977b249dd9fb5f4829b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_21db8a77b00d4a4e82fdfa608657531f", "max": 9085698, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_6dbbedeca9314e66ae50e44ffa31a414", "value": 9085698 } }, "f401d53bf28e44eb906bce6c05412662": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "fb995c740590427b882572c81d4e848c": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "fce7a61c25ec4390af43d92b7c473a45": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_30307300bc4e4baf96560e30969a82b6", "IPY_MODEL_8fc142b628fb40568730234de1cafde2", "IPY_MODEL_a8464a4c711e4e00aafdfc919b60d07e" ], "layout": "IPY_MODEL_5f40db8173dd4d76b6ef5ed6d9ec8b6e" } }, "fdb1941405ed4e4aa06019933892deb3": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } } } } }, "nbformat": 4, "nbformat_minor": 0 }