diff --git "a/notebooks/06_Few-shot_Prompting_Open_Source.ipynb" "b/notebooks/06_Few-shot_Prompting_Open_Source.ipynb" --- "a/notebooks/06_Few-shot_Prompting_Open_Source.ipynb" +++ "b/notebooks/06_Few-shot_Prompting_Open_Source.ipynb" @@ -1,10343 +1,3 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "application/vnd.databricks.v1+cell": { - "cellMetadata": {}, - "inputWidgets": {}, - "nuid": "0ea8b46b-839b-445b-8043-ccdf4e920ace", - "showTitle": false, - "title": "" - }, - "id": "YLH80COBzi_F" - }, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "63B5exAuzq4M" - }, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "\n", - "if \"workding_dir\" not in locals():\n", - " try:\n", - " from google.colab import drive\n", - " drive.mount('/content/drive')\n", - " workding_dir = \"/content/drive/MyDrive/logical-reasoning/\"\n", - " except ModuleNotFoundError:\n", - " workding_dir = str(Path.cwd().parent)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "executionInfo": { - "elapsed": 368, - "status": "ok", - "timestamp": 1719461634865, - "user": { - "displayName": "Donghao Huang", - "userId": "00463591218503521679" - }, - "user_tz": -480 - }, - "id": "zFulf0bg0H-9", - "outputId": "debdd535-c828-40b9-efc0-8a180e5830dd" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "workding dir: /home/inflaton/code/logical-reasoning\n" - ] - } - ], - "source": [ - "import os\n", - "import sys\n", - "\n", - "os.chdir(workding_dir)\n", - "sys.path.append(workding_dir)\n", - "print(\"workding dir:\", workding_dir)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "application/vnd.databricks.v1+cell": { - "cellMetadata": {}, - "inputWidgets": {}, - "nuid": "9f67ec60-2f24-411c-84eb-0dd664b44775", - "showTitle": false, - "title": "" - }, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 589, - "status": "ok", - "timestamp": 1719462011879, - "user": { - "displayName": "Donghao Huang", - "userId": "00463591218503521679" - }, - "user_tz": -480 - }, - "id": "DIUiweYYzi_I", - "outputId": "e16e9247-9077-4b0c-f8ea-17059f05a1c4" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current Directory:\n", - "/home/inflaton/code/logical-reasoning\n", - "Sat Sep 14 23:45:32 2024 \n", - "+-----------------------------------------------------------------------------------------+\n", - "| NVIDIA-SMI 560.35.02 Driver Version: 560.94 CUDA Version: 12.6 |\n", - "|-----------------------------------------+------------------------+----------------------+\n", - "| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. 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Max memory = 23.988 GB.\n", - "0.0 GB of memory reserved.\n", - "loading model: shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat with adapter: None\n", - "[INFO|tokenization_utils_base.py:2289] 2024-09-14 23:46:28,628 >> loading file tokenizer.model from cache at /home/inflaton/.cache/huggingface/hub/models--shenzhi-wang--Mistral-7B-v0.3-Chinese-Chat/snapshots/7288d48cae9f942b4986132eb3450cf45da3dd56/tokenizer.model\n", - "[INFO|tokenization_utils_base.py:2289] 2024-09-14 23:46:28,628 >> loading file tokenizer.json from cache at /home/inflaton/.cache/huggingface/hub/models--shenzhi-wang--Mistral-7B-v0.3-Chinese-Chat/snapshots/7288d48cae9f942b4986132eb3450cf45da3dd56/tokenizer.json\n", - "[INFO|tokenization_utils_base.py:2289] 2024-09-14 23:46:28,628 >> loading file added_tokens.json from cache at None\n", - "[INFO|tokenization_utils_base.py:2289] 2024-09-14 23:46:28,628 >> loading file special_tokens_map.json from cache at /home/inflaton/.cache/huggingface/hub/models--shenzhi-wang--Mistral-7B-v0.3-Chinese-Chat/snapshots/7288d48cae9f942b4986132eb3450cf45da3dd56/special_tokens_map.json\n", - "[INFO|tokenization_utils_base.py:2289] 2024-09-14 23:46:28,629 >> loading file tokenizer_config.json from cache at /home/inflaton/.cache/huggingface/hub/models--shenzhi-wang--Mistral-7B-v0.3-Chinese-Chat/snapshots/7288d48cae9f942b4986132eb3450cf45da3dd56/tokenizer_config.json\n", - "[INFO|configuration_utils.py:733] 2024-09-14 23:46:29,610 >> loading configuration file config.json from cache at /home/inflaton/.cache/huggingface/hub/models--shenzhi-wang--Mistral-7B-v0.3-Chinese-Chat/snapshots/7288d48cae9f942b4986132eb3450cf45da3dd56/config.json\n", - "[INFO|configuration_utils.py:800] 2024-09-14 23:46:29,618 >> Model config MistralConfig {\n", - " \"_name_or_path\": \"shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat\",\n", - " \"architectures\": [\n", - " \"MistralForCausalLM\"\n", - " ],\n", - " \"attention_dropout\": 0.0,\n", - " \"bos_token_id\": 1,\n", - " \"eos_token_id\": 2,\n", - " \"head_dim\": 128,\n", - " \"hidden_act\": \"silu\",\n", - " \"hidden_size\": 4096,\n", - " \"initializer_range\": 0.02,\n", - " \"intermediate_size\": 14336,\n", - " \"max_position_embeddings\": 32768,\n", - " \"model_type\": \"mistral\",\n", - " \"num_attention_heads\": 32,\n", - " \"num_hidden_layers\": 32,\n", - " \"num_key_value_heads\": 8,\n", - " \"rms_norm_eps\": 1e-05,\n", - " \"rope_theta\": 1000000.0,\n", - " \"sliding_window\": null,\n", - " \"tie_word_embeddings\": false,\n", - " \"torch_dtype\": \"bfloat16\",\n", - " \"transformers_version\": \"4.43.3\",\n", - " \"use_cache\": false,\n", - " \"vocab_size\": 32768\n", - "}\n", - "\n", - "09/14/2024 23:46:29 - INFO - llamafactory.model.patcher - Using KV cache for faster generation.\n", - "[INFO|modeling_utils.py:3634] 2024-09-14 23:46:29,877 >> loading weights file model.safetensors from cache at /home/inflaton/.cache/huggingface/hub/models--shenzhi-wang--Mistral-7B-v0.3-Chinese-Chat/snapshots/7288d48cae9f942b4986132eb3450cf45da3dd56/model.safetensors.index.json\n", - "[INFO|modeling_utils.py:1572] 2024-09-14 23:46:29,897 >> Instantiating MistralForCausalLM model under default dtype torch.bfloat16.\n", - "[INFO|configuration_utils.py:1038] 2024-09-14 23:46:29,898 >> Generate config GenerationConfig {\n", - " \"bos_token_id\": 1,\n", - " \"eos_token_id\": 2\n", - "}\n", - "\n", - "Loading checkpoint shards: 100%|██████████████████| 3/3 [01:47<00:00, 35.90s/it]\n", - "[INFO|modeling_utils.py:4463] 2024-09-14 23:48:18,205 >> All model checkpoint weights were used when initializing MistralForCausalLM.\n", - "\n", - "[INFO|modeling_utils.py:4471] 2024-09-14 23:48:18,205 >> All the weights of MistralForCausalLM were initialized from the model checkpoint at shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat.\n", - "If your task is similar to the task the model of the checkpoint was trained on, you can already use MistralForCausalLM for predictions without further training.\n", - "[INFO|configuration_utils.py:993] 2024-09-14 23:48:18,497 >> loading configuration file generation_config.json from cache at /home/inflaton/.cache/huggingface/hub/models--shenzhi-wang--Mistral-7B-v0.3-Chinese-Chat/snapshots/7288d48cae9f942b4986132eb3450cf45da3dd56/generation_config.json\n", - "[INFO|configuration_utils.py:1038] 2024-09-14 23:48:18,497 >> Generate config GenerationConfig {\n", - " \"bos_token_id\": 1,\n", - " \"eos_token_id\": 2\n", - "}\n", - "\n", - "09/14/2024 23:48:18 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.\n", - "09/14/2024 23:48:18 - INFO - llamafactory.model.loader - all params: 7,248,023,552\n", - "(2) GPU = NVIDIA GeForce RTX 4090. Max memory = 23.988 GB.\n", - "13.627 GB of memory reserved.\n", - "Evaluating model: shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat on cuda\n", - "*** Evaluating with num_shots: 0\n", - "loading train/test data files\n", - "Map: 100%|██████████████████████| 25000/25000 [00:00<00:00, 59746.80 examples/s]\n", - "Map: 100%|████████████████████████| 3000/3000 [00:00<00:00, 63912.23 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: You are an expert in logical reasoning.[INST] 你是一个情景猜谜游戏的主持人。游戏规则如下:\n", - "\n", - "1. 参与者会得到一个谜面,谜面会描述一个简单又难以理解的事件。\n", - "2. 主持人知道谜底,谜底是谜面的答案。\n", - "3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n", - "4. 对于每个问题,主持人将根据实际情况回答以下五个选项之一:是、不是、不重要、回答正确、问法错误。各回答的判断标准如下:\n", - " - 若谜面和谜底能找到问题的答案,回答:是或者不是\n", - " - 若谜面和谜底不能直接或者间接推断出问题的答案,回答:不重要\n", - " - 若参与者提问不是一个封闭式问题或者问题难以理解,回答:问法错误\n", - " - 若参与者提问基本还原了谜底真相,回答:回答正确\n", - "5. 回答中不能添加任何其它信息,也不能省略选项中的任何一个字。例如,不可以把“不是”省略成“不”。\n", - "\n", - "请严格按照这些规则回答参与者提出的问题。\n", - "\n", - "谜面: 在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", - "谜底: 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", - "参与者提出的问题: 甄加索是自杀吗\n", - "回答: \n", - " [/INST]不是\n", - "--------------------------------------------------\n", - "prompt: You are an expert in logical reasoning.[INST] 你是一个情景猜谜游戏的主持人。游戏规则如下:\n", - "\n", - "1. 参与者会得到一个谜面,谜面会描述一个简单又难以理解的事件。\n", - "2. 主持人知道谜底,谜底是谜面的答案。\n", - "3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n", - "4. 对于每个问题,主持人将根据实际情况回答以下五个选项之一:是、不是、不重要、回答正确、问法错误。各回答的判断标准如下:\n", - " - 若谜面和谜底能找到问题的答案,回答:是或者不是\n", - " - 若谜面和谜底不能直接或者间接推断出问题的答案,回答:不重要\n", - " - 若参与者提问不是一个封闭式问题或者问题难以理解,回答:问法错误\n", - " - 若参与者提问基本还原了谜底真相,回答:回答正确\n", - "5. 回答中不能添加任何其它信息,也不能省略选项中的任何一个字。例如,不可以把“不是”省略成“不”。\n", - "\n", - "请严格按照这些规则回答参与者提出的问题。\n", - "\n", - "谜面: 在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", - "谜底: 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", - "参与者提出的问题: 甄加索是自杀吗\n", - "回答: \n", - " [/INST]\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: You are an expert in logical reasoning.[INST] 你是一个情景猜谜游戏的主持人。游戏规则如下:\n", - "\n", - "1. 参与者会得到一个谜面,谜面会描述一个简单又难以理解的事件。\n", - "2. 主持人知道谜底,谜底是谜面的答案。\n", - "3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n", - "4. 对于每个问题,主持人将根据实际情况回答以下五个选项之一:是、不是、不重要、回答正确、问法错误。各回答的判断标准如下:\n", - " - 若谜面和谜底能找到问题的答案,回答:是或者不是\n", - " - 若谜面和谜底不能直接或者间接推断出问题的答案,回答:不重要\n", - " - 若参与者提问不是一个封闭式问题或者问题难以理解,回答:问法错误\n", - " - 若参与者提问基本还原了谜底真相,回答:回答正确\n", - "5. 回答中不能添加任何其它信息,也不能省略选项中的任何一个字。例如,不可以把“不是”省略成“不”。\n", - "\n", - "请严格按照这些规则回答参与者提出的问题。\n", - "\n", - "谜面: 在一个安静的夜晚,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", - "谜底: 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���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", - "参与者提出的问题: 死者受伤了吗\n", - "回答: \n", - " [/INST]不是\n", - "--------------------------------------------------\n", - "prompt: You are an expert in logical reasoning.[INST] 你是一个情景猜谜游戏的主持人。游戏规则如下:\n", - "\n", - "1. 参与者会得到一个谜面,谜面会描述一个简单又难以理解的事件。\n", - "2. 主持人知道谜底,谜底是谜面的答案。\n", - "3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n", - "4. 对于每个问题,主持人将根据实际情况回答以下五个选项之一:是、不是、不重要、回答正确、问法错误。各回答的判断标准如下:\n", - " - 若谜面和谜底能找到问题的答案,回答:是或者不是\n", - " - 若谜面和谜底不能直接或者间接推断出问题的答案,回答:不重要\n", - " - 若参与者提问不是一个封闭式问题或者问题难以理解,回答:问法错误\n", - " - 若参与者提问基本还原了谜底真相,回答:回答正确\n", - "5. 回答中不能添加任何其它信息,也不能省略选项中的任何一个字。例如,不可以把“不是”省略成“不”。\n", - "\n", - "请严格按照这些规则回答参与者提出的问题。\n", - "\n", - "谜面: 在一个安静的夜晚,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", - "谜底: 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", - "参与者提出的问题: 死者受伤了吗\n", - "回答: \n", - " [/INST]\n", - " 0%| | 0/3000 [00:00> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - "Batch output: ['不是。']\n", - 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" 65%|█████████████████████████▍ | 1953/3000 [16:56<08:53, 1.96it/s][WARNING|utils.py:1542] 2024-09-15 00:05:17,295 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 65%|█████████████████████████▍ | 1954/3000 [16:57<09:02, 1.93it/s][WARNING|utils.py:1542] 2024-09-15 00:05:17,833 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 65%|█████████████████████████▍ | 1955/3000 [16:57<09:06, 1.91it/s][WARNING|utils.py:1542] 2024-09-15 00:05:18,368 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 65%|█████████████████████████▍ | 1956/3000 [16:58<09:10, 1.90it/s][WARNING|utils.py:1542] 2024-09-15 00:05:18,905 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 65%|█████████████████████████▍ | 1957/3000 [16:58<08:39, 2.01it/s][WARNING|utils.py:1542] 2024-09-15 00:05:19,334 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 65%|█████████████████████████▍ | 1958/3000 [16:59<08:18, 2.09it/s][WARNING|utils.py:1542] 2024-09-15 00:05:19,767 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 65%|█████████████████████████▍ | 1959/3000 [16:59<08:36, 2.01it/s][WARNING|utils.py:1542] 2024-09-15 00:05:20,305 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 65%|█████████████████████████▍ | 1960/3000 [16:59<08:16, 2.10it/s][WARNING|utils.py:1542] 2024-09-15 00:05:20,738 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 65%|█████████████████████████▍ | 1961/3000 [17:00<08:33, 2.02it/s][WARNING|utils.py:1542] 2024-09-15 00:05:21,273 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 65%|█████████████████████████▌ | 1962/3000 [17:00<08:13, 2.10it/s][WARNING|utils.py:1542] 2024-09-15 00:05:21,703 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 65%|█████████████████████████▌ | 1963/3000 [17:01<08:31, 2.03it/s][WARNING|utils.py:1542] 2024-09-15 00:05:22,238 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 65%|█████████████████████████▌ | 1964/3000 [17:02<08:45, 1.97it/s][WARNING|utils.py:1542] 2024-09-15 00:05:22,777 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▌ | 1965/3000 [17:02<08:54, 1.94it/s][WARNING|utils.py:1542] 2024-09-15 00:05:23,317 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▌ | 1966/3000 [17:03<08:28, 2.03it/s][WARNING|utils.py:1542] 2024-09-15 00:05:23,749 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▌ | 1967/3000 [17:03<08:41, 1.98it/s][WARNING|utils.py:1542] 2024-09-15 00:05:24,286 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▌ | 1968/3000 [17:04<08:51, 1.94it/s][WARNING|utils.py:1542] 2024-09-15 00:05:24,823 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▌ | 1969/3000 [17:04<08:57, 1.92it/s][WARNING|utils.py:1542] 2024-09-15 00:05:25,361 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▌ | 1970/3000 [17:05<09:02, 1.90it/s][WARNING|utils.py:1542] 2024-09-15 00:05:25,899 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▌ | 1971/3000 [17:05<09:04, 1.89it/s][WARNING|utils.py:1542] 2024-09-15 00:05:26,434 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▋ | 1972/3000 [17:06<08:33, 2.00it/s][WARNING|utils.py:1542] 2024-09-15 00:05:26,866 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▋ | 1973/3000 [17:06<08:11, 2.09it/s][WARNING|utils.py:1542] 2024-09-15 00:05:27,295 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▋ | 1974/3000 [17:07<08:28, 2.02it/s][WARNING|utils.py:1542] 2024-09-15 00:05:27,831 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▋ | 1975/3000 [17:07<08:07, 2.10it/s][WARNING|utils.py:1542] 2024-09-15 00:05:28,259 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▋ | 1976/3000 [17:08<08:26, 2.02it/s][WARNING|utils.py:1542] 2024-09-15 00:05:28,799 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▋ | 1977/3000 [17:08<08:38, 1.97it/s][WARNING|utils.py:1542] 2024-09-15 00:05:29,335 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▋ | 1978/3000 [17:09<08:47, 1.94it/s][WARNING|utils.py:1542] 2024-09-15 00:05:29,874 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▋ | 1979/3000 [17:09<09:26, 1.80it/s][WARNING|utils.py:1542] 2024-09-15 00:05:30,518 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▋ | 1980/3000 [17:10<09:19, 1.82it/s][WARNING|utils.py:1542] 2024-09-15 00:05:31,053 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▊ | 1981/3000 [17:10<08:43, 1.95it/s][WARNING|utils.py:1542] 2024-09-15 00:05:31,485 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▊ | 1982/3000 [17:11<08:17, 2.05it/s][WARNING|utils.py:1542] 2024-09-15 00:05:31,916 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▊ | 1983/3000 [17:11<08:31, 1.99it/s][WARNING|utils.py:1542] 2024-09-15 00:05:32,452 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▊ | 1984/3000 [17:12<08:41, 1.95it/s][WARNING|utils.py:1542] 2024-09-15 00:05:32,988 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▊ | 1985/3000 [17:12<08:15, 2.05it/s][WARNING|utils.py:1542] 2024-09-15 00:05:33,418 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▊ | 1986/3000 [17:13<07:58, 2.12it/s][WARNING|utils.py:1542] 2024-09-15 00:05:33,854 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▊ | 1987/3000 [17:13<08:17, 2.04it/s][WARNING|utils.py:1542] 2024-09-15 00:05:34,387 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▊ | 1988/3000 [17:14<08:30, 1.98it/s][WARNING|utils.py:1542] 2024-09-15 00:05:34,924 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▊ | 1989/3000 [17:14<08:07, 2.07it/s][WARNING|utils.py:1542] 2024-09-15 00:05:35,354 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▊ | 1990/3000 [17:15<08:23, 2.01it/s][WARNING|utils.py:1542] 2024-09-15 00:05:35,890 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▉ | 1991/3000 [17:20<33:05, 1.97s/it][WARNING|utils.py:1542] 2024-09-15 00:05:41,285 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▉ | 1992/3000 [17:21<25:50, 1.54s/it][WARNING|utils.py:1542] 2024-09-15 00:05:41,822 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▉ | 1993/3000 [17:21<20:45, 1.24s/it][WARNING|utils.py:1542] 2024-09-15 00:05:42,356 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▉ | 1994/3000 [17:22<17:12, 1.03s/it][WARNING|utils.py:1542] 2024-09-15 00:05:42,892 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 66%|█████████████████████████▉ | 1995/3000 [17:22<14:42, 1.14it/s][WARNING|utils.py:1542] 2024-09-15 00:05:43,425 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 67%|█████████████████████████▉ | 1996/3000 [17:23<12:59, 1.29it/s][WARNING|utils.py:1542] 2024-09-15 00:05:43,962 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 67%|█████████████████████████▉ | 1997/3000 [17:23<11:46, 1.42it/s][WARNING|utils.py:1542] 2024-09-15 00:05:44,498 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 67%|█████████████████████████▉ | 1998/3000 [17:24<10:54, 1.53it/s][WARNING|utils.py:1542] 2024-09-15 00:05:45,033 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 67%|█████████████████████████▉ | 1999/3000 [17:24<10:18, 1.62it/s][WARNING|utils.py:1542] 2024-09-15 00:05:45,570 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 67%|██████████████████████████ | 2000/3000 [17:25<09:52, 1.69it/s][WARNING|utils.py:1542] 2024-09-15 00:05:46,103 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - 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" 67%|██████████████████████████▏ | 2019/3000 [17:34<08:17, 1.97it/s][WARNING|utils.py:1542] 2024-09-15 00:05:55,645 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 67%|██████████████████████████▎ | 2020/3000 [17:35<07:53, 2.07it/s][WARNING|utils.py:1542] 2024-09-15 00:05:56,073 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 67%|██████████████████████████▎ | 2021/3000 [17:35<07:37, 2.14it/s][WARNING|utils.py:1542] 2024-09-15 00:05:56,503 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 67%|██████████████████████████▎ | 2022/3000 [17:36<07:56, 2.05it/s][WARNING|utils.py:1542] 2024-09-15 00:05:57,037 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 67%|██████████████████████████▎ | 2023/3000 [17:36<07:39, 2.13it/s][WARNING|utils.py:1542] 2024-09-15 00:05:57,468 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - " 67%|██████████████████████████▎ | 2024/3000 [17:37<07:59, 2.04it/s][WARNING|utils.py:1542] 2024-09-15 00:05:58,007 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n", - 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Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", - "shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/shots-10 metrics: {'accuracy': 0.6946666666666667, 'incorrect_ids': [2, 5, 12, 14, 15, 16, 18, 21, 27, 28, 31, 34, 36, 42, 43, 48, 53, 55, 58, 59, 62, 63, 64, 65, 66, 67, 71, 72, 75, 76, 77, 78, 79, 81, 82, 83, 84, 87, 88, 91, 93, 96, 99, 101, 102, 104, 105, 113, 115, 117, 118, 121, 124, 126, 127, 131, 134, 136, 140, 141, 143, 144, 149, 150, 153, 154, 155, 159, 160, 161, 164, 169, 170, 174, 175, 178, 188, 189, 190, 191, 192, 193, 199, 200, 201, 202, 205, 206, 207, 210, 214, 220, 221, 222, 223, 224, 233, 234, 236, 237, 238, 245, 248, 250, 251, 252, 255, 257, 258, 260, 263, 265, 266, 268, 269, 272, 275, 276, 283, 284, 286, 288, 292, 293, 295, 296, 300, 301, 302, 303, 304, 305, 308, 314, 317, 318, 319, 320, 325, 328, 331, 332, 334, 335, 338, 339, 343, 348, 349, 350, 356, 363, 368, 370, 373, 374, 375, 378, 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2789, 2791, 2793, 2794, 2798, 2801, 2806, 2810, 2811, 2814, 2816, 2819, 2824, 2829, 2833, 2837, 2843, 2851, 2852, 2854, 2860, 2863, 2865, 2866, 2877, 2879, 2880, 2881, 2882, 2884, 2892, 2897, 2902, 2904, 2905, 2906, 2912, 2916, 2921, 2926, 2929, 2931, 2935, 2937, 2939, 2942, 2944, 2946, 2948, 2953, 2954, 2960, 2963, 2966, 2967, 2968, 2971, 2977, 2979, 2982, 2985, 2994, 2995], 'precision': 0.701136267898111, 'recall': 0.6946666666666667, 'f1': 0.6634078645357937, 'ratio_valid_classifications': 0.011666666666666667}\n", - "(3) GPU = NVIDIA GeForce RTX 4090. Max memory = 23.988 GB.\n", - "14.771 GB of memory reserved.\n", - "Current Directory:\n", - "/home/inflaton/code/logical-reasoning\n", - "Evaluating internlm/internlm2_5-7b-chat with few-shot learning\n", - "loading env vars from: /home/inflaton/code/logical-reasoning/.env\n", - "Adding /home/inflaton/code/logical-reasoning to sys.path\n", - "loading /home/inflaton/code/logical-reasoning/llm_toolkit/logical_reasoning_utils.py\n", - "CUDA is available, we have found 1 GPU(s)\n", - "NVIDIA GeForce RTX 4090\n", - "CUDA version: 12.1\n", - "internlm/internlm2_5-7b-chat None False datasets/mgtv data/internlm2_5-7b-chat_results.csv 2048 1\n", - "(0) GPU = NVIDIA GeForce RTX 4090. Max memory = 23.988 GB.\n", - "0.0 GB of memory reserved.\n", - "loading model: internlm/internlm2_5-7b-chat with adapter: None\n", - "[INFO|tokenization_utils_base.py:2289] 2024-09-15 00:14:27,537 >> loading file ./tokenizer.model from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat/snapshots/9b8d9553846ecf6393f3408fa9d3ec9928fdab4d/./tokenizer.model\n", - "[INFO|tokenization_utils_base.py:2289] 2024-09-15 00:14:27,537 >> loading file added_tokens.json from cache at None\n", - "[INFO|tokenization_utils_base.py:2289] 2024-09-15 00:14:27,537 >> loading file special_tokens_map.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat/snapshots/9b8d9553846ecf6393f3408fa9d3ec9928fdab4d/special_tokens_map.json\n", - "[INFO|tokenization_utils_base.py:2289] 2024-09-15 00:14:27,537 >> loading file tokenizer_config.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat/snapshots/9b8d9553846ecf6393f3408fa9d3ec9928fdab4d/tokenizer_config.json\n", - "[INFO|tokenization_utils_base.py:2289] 2024-09-15 00:14:27,537 >> loading file tokenizer.json from cache at None\n", - "09/15/2024 00:14:28 - INFO - llamafactory.data.template - Add <|im_end|> to stop words.\n", - "[INFO|configuration_utils.py:733] 2024-09-15 00:14:28,592 >> loading configuration file config.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat/snapshots/9b8d9553846ecf6393f3408fa9d3ec9928fdab4d/config.json\n", - "configuration_internlm2.py: 100%|██████████| 8.84k/8.84k [00:00<00:00, 25.6MB/s]\n", - "[INFO|configuration_utils.py:733] 2024-09-15 00:14:29,444 >> loading configuration file config.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat/snapshots/9b8d9553846ecf6393f3408fa9d3ec9928fdab4d/config.json\n", - "[INFO|configuration_utils.py:800] 2024-09-15 00:14:29,446 >> Model config InternLM2Config {\n", - " \"_name_or_path\": \"internlm/internlm2_5-7b-chat\",\n", - " \"architectures\": [\n", - " \"InternLM2ForCausalLM\"\n", - " ],\n", - " \"attn_implementation\": \"eager\",\n", - " \"auto_map\": {\n", - " \"AutoConfig\": \"internlm/internlm2_5-7b-chat--configuration_internlm2.InternLM2Config\",\n", - " \"AutoModel\": \"internlm/internlm2_5-7b-chat--modeling_internlm2.InternLM2ForCausalLM\",\n", - " \"AutoModelForCausalLM\": \"internlm/internlm2_5-7b-chat--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\": 32768,\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\": 2.0,\n", - " \"type\": \"dynamic\"\n", - " },\n", - " \"rope_theta\": 1000000,\n", - " \"tie_word_embeddings\": false,\n", - " \"torch_dtype\": \"bfloat16\",\n", - " \"transformers_version\": \"4.43.3\",\n", - " \"use_cache\": true,\n", - " \"vocab_size\": 92544\n", - "}\n", - "\n", - "09/15/2024 00:14:29 - INFO - llamafactory.model.patcher - Using KV cache for faster generation.\n", - "modeling_internlm2.py: 100%|████████████████| 81.0k/81.0k [00:00<00:00, 284kB/s]\n", - "[INFO|modeling_utils.py:3634] 2024-09-15 00:14:31,135 >> loading weights file model.safetensors from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat/snapshots/9b8d9553846ecf6393f3408fa9d3ec9928fdab4d/model.safetensors.index.json\n", - "Downloading shards: 100%|█████████████████████████| 8/8 [00:02<00:00, 3.81it/s]\n", - "[INFO|modeling_utils.py:1572] 2024-09-15 00:14:33,240 >> Instantiating InternLM2ForCausalLM model under default dtype torch.bfloat16.\n", - "[INFO|configuration_utils.py:1038] 2024-09-15 00:14:33,241 >> 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 [01:58<00:00, 14.81s/it]\n", - "[INFO|modeling_utils.py:4463] 2024-09-15 00:16:31,887 >> All model checkpoint weights were used when initializing InternLM2ForCausalLM.\n", - "\n", - "[INFO|modeling_utils.py:4471] 2024-09-15 00:16:31,887 >> All the weights of InternLM2ForCausalLM were initialized from the model checkpoint at internlm/internlm2_5-7b-chat.\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:993] 2024-09-15 00:16:32,175 >> loading configuration file generation_config.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat/snapshots/9b8d9553846ecf6393f3408fa9d3ec9928fdab4d/generation_config.json\n", - "[INFO|configuration_utils.py:1038] 2024-09-15 00:16:32,175 >> 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", - "09/15/2024 00:16:32 - INFO - llamafactory.model.model_utils.attention - Using vanilla attention implementation.\n", - "09/15/2024 00:16:32 - INFO - llamafactory.model.loader - all params: 7,737,708,544\n", - "(2) GPU = NVIDIA GeForce RTX 4090. Max memory = 23.988 GB.\n", - "15.123 GB of memory reserved.\n", - "Evaluating model: internlm/internlm2_5-7b-chat on cuda\n", - "*** Evaluating with num_shots: 0\n", - "loading train/test data files\n", - "Map: 100%|██████████████████████| 25000/25000 [00:00<00:00, 61177.09 examples/s]\n", - "Map: 100%|████████████████████████| 3000/3000 [00:00<00:00, 53856.91 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", - "你是一个情景猜谜游戏的主持人。游戏规则如下:\n", - "\n", - "1. 参与者会得到一个谜面,谜面会描述一个简单又难以理解的事件。\n", - "2. 主持人知道谜底,谜底是谜面的答案。\n", - "3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n", - "4. 对于每个问题,主持人将根据实际情况回答以下五个选项之一:是、不是、不重要、回答正确、问法错误。各回答的判断标准如下:\n", - " - 若谜面和谜底能找到问题的答案,回答:是或者不是\n", - " - 若谜面和谜底不能直接或者间接推断出问题的答案,回答:不重要\n", - " - 若参与者提问不是一个封闭式问题或者问题难以理解,回答:问法错误\n", - " - 若参与者提问基本还原了谜底真相,回答:回答正确\n", - "5. 回答中不能添加任何其它信息,也不能省略选项中的任何一个字。例如,不可以把“不是”省略成“不”。\n", - "\n", - "请严格按照这些规则回答参与者提出的问题。\n", - "\n", - "谜面: 在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", - "谜底: 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��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", - "参与者提出的问题: 甄加索是自杀吗\n", - "回答: \n", - "<|im_end|>\n", - "<|im_start|>assistant\n", - "不是\n", - "--------------------------------------------------\n", - "prompt: <|im_start|>system\n", - "You are an expert in logical reasoning.<|im_end|>\n", - "<|im_start|>user\n", - "你是一个情景猜谜游戏的主持人。游戏规则如下:\n", - "\n", - "1. 参与者会得到一个谜面,谜面会描述一个简单又难以理解的事件。\n", - "2. 主持人知道谜底,谜底是谜面的答案。\n", - "3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n", - "4. 对于每个问题,主持人将根据实际情况回答以下五个选项之一:是、不是、不重要、回答正确、问法错误。各回答的判断标准如下:\n", - " - 若谜面和谜底能找到问题的答案,回答:是或者不是\n", - " - 若谜面和谜底不能直接或者间接推断出问题的答案,回答:不重要\n", - " - 若参与者提问不是一个封闭式问题或者问题难以理解,回答:问法错误\n", - " - 若参与者提问基本还原了谜底真相,回答:回答正确\n", - "5. 回答中不能添加任何其它信息,也不能省略选项中的任何一个字。例如,不可以把“不是”省略成“不”。\n", - "\n", - "请严格按照这些规则回答参与者提出的问题。\n", - "\n", - "谜面: 在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", - "谜底: 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", - "参与者提出的问题: 甄加索是自杀吗\n", - "回答: \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", - "你是一个情景猜谜游戏的主持人。游戏规则如下:\n", - "\n", - "1. 参与者会得到一个谜面,谜面会描述一个简单又难以理解的事件。\n", - "2. 主持人知道谜底,谜底是谜面的答案。\n", - "3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n", - "4. 对于每个问题,主持人将根据实际情况回答以下五个选项之一:是、不是、不重要、回答正确、问法错误。各回答的判断标准如下:\n", - " - 若谜面和谜底能找到问题的答案,回答:是或者不是\n", - " - 若谜面和谜底不能直接或者间接推断出问题的答案,回答:不重要\n", - " - 若参与者提问不是一个封闭式问题或者问题难以理解,回答:问法错误\n", - " - 若参与者提问基本还原了谜底真相,回答:回答正确\n", - "5. 回答中不能添加任何其它信息,也不能省略选项中的任何一个字。例如,不可以把“不是”省略成“不”。\n", - "\n", - "请严格按照这些规则回答参与者提出的问题。\n", - "\n", - "谜面: 在一个安静的夜晚,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", - "谜底: 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", - "参与者提出的问题: 死者受伤了吗\n", - "回答: \n", - "<|im_end|>\n", - "<|im_start|>assistant\n", - "不是\n", - "--------------------------------------------------\n", - "prompt: <|im_start|>system\n", - "You are an expert in logical reasoning.<|im_end|>\n", - "<|im_start|>user\n", - "你是一个情景猜谜游戏的主持人。游戏规则如下:\n", - "\n", - "1. 参与者会得到一个谜面,谜面会描述一个简单又难以理解的事件。\n", - "2. 主持人知道谜底,谜底是谜面的答案。\n", - "3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n", - "4. 对于每个问题,主持人将根据实际情况回答以下五个选项之一:是、不是、不重要、回答正确、问法错误。各回答的判断标准如下:\n", - " - 若谜面和谜底能找到问题的答案,回答:是或者不是\n", - " - 若谜面和谜底不能直接或者间接推断出问题的答案,回答:不重要\n", - " - 若参与者提问不是一个封闭式问题或者问题难以理解,回答:问法错误\n", - " - 若参与者提问基本还原了谜底真相,回答:回答正确\n", - "5. 回答中不能添加任何其它信息,也不能省略选项中的任何一个字。例如,不可以把“不是”省略成“不”。\n", - "\n", - "请严格按照这些规则回答参与者提出的问题。\n", - "\n", - "谜面: 在一个安静的夜晚,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", - "谜底: 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��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", - "参与者提出的问题: 死者受伤了吗\n", - "回答: \n", - "<|im_end|>\n", - "<|im_start|>assistant\n", - "\n", - " 0%| | 0/3000 [00:00> loading file ./tokenizer.model from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/846dee6fb6f5a96fb9ab7d3f1f9c383ac9e73bc1/./tokenizer.model\n", - "[INFO|tokenization_utils_base.py:2289] 2024-09-15 00:24:30,339 >> loading file added_tokens.json from cache at None\n", - "[INFO|tokenization_utils_base.py:2289] 2024-09-15 00:24:30,339 >> loading file special_tokens_map.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/846dee6fb6f5a96fb9ab7d3f1f9c383ac9e73bc1/special_tokens_map.json\n", - "[INFO|tokenization_utils_base.py:2289] 2024-09-15 00:24:30,339 >> loading file tokenizer_config.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/846dee6fb6f5a96fb9ab7d3f1f9c383ac9e73bc1/tokenizer_config.json\n", - "[INFO|tokenization_utils_base.py:2289] 2024-09-15 00:24:30,339 >> loading file tokenizer.json from cache at None\n", - "09/15/2024 00:24:31 - INFO - llamafactory.data.template - Add <|im_end|> to stop words.\n", - "config.json: 100%|█████████████████████████████| 895/895 [00:00<00:00, 4.18MB/s]\n", - "[INFO|configuration_utils.py:733] 2024-09-15 00:24:31,678 >> loading configuration file config.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/846dee6fb6f5a96fb9ab7d3f1f9c383ac9e73bc1/config.json\n", - "configuration_internlm2.py: 100%|██████████| 8.84k/8.84k [00:00<00:00, 34.3MB/s]\n", - "[INFO|configuration_utils.py:733] 2024-09-15 00:24:32,487 >> loading configuration file config.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/846dee6fb6f5a96fb9ab7d3f1f9c383ac9e73bc1/config.json\n", - "[INFO|configuration_utils.py:800] 2024-09-15 00:24:32,488 >> 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\": \"4.43.3\",\n", - " \"use_cache\": true,\n", - " \"vocab_size\": 92544\n", - "}\n", - "\n", - "09/15/2024 00:24:32 - INFO - llamafactory.model.patcher - Using KV cache for faster generation.\n", - "modeling_internlm2.py: 100%|████████████████| 81.0k/81.0k [00:00<00:00, 297kB/s]\n", - "model.safetensors.index.json: 100%|████████| 18.2k/18.2k [00:00<00:00, 5.04MB/s]\n", - "[INFO|modeling_utils.py:3634] 2024-09-15 00:24:34,371 >> loading weights file model.safetensors from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/846dee6fb6f5a96fb9ab7d3f1f9c383ac9e73bc1/model.safetensors.index.json\n", - "Downloading shards: 0%| | 0/8 [00:00> Instantiating InternLM2ForCausalLM model under default dtype torch.bfloat16.\n", - "[INFO|configuration_utils.py:1038] 2024-09-15 00:45:08,756 >> Generate config GenerationConfig {\n", - " \"bos_token_id\": 1,\n", - " \"eos_token_id\": 2,\n", - " \"pad_token_id\": 2\n", - "}\n", - "\n", - "Loading checkpoint shards: 100%|██████████████████| 8/8 [00:30<00:00, 3.81s/it]\n", - "[INFO|modeling_utils.py:4463] 2024-09-15 00:45:39,407 >> All model checkpoint weights were used when initializing InternLM2ForCausalLM.\n", - "\n", - "[INFO|modeling_utils.py:4471] 2024-09-15 00:45:39,407 >> All the weights of InternLM2ForCausalLM were initialized from the model checkpoint at internlm/internlm2_5-7b-chat-1m.\n", - "If your task is similar to the task the model of the checkpoint was trained on, you can already use InternLM2ForCausalLM for predictions without further training.\n", - "generation_config.json: 100%|███████████████████| 123/123 [00:00<00:00, 863kB/s]\n", - "[INFO|configuration_utils.py:993] 2024-09-15 00:45:39,943 >> loading configuration file generation_config.json from cache at /home/inflaton/.cache/huggingface/hub/models--internlm--internlm2_5-7b-chat-1m/snapshots/846dee6fb6f5a96fb9ab7d3f1f9c383ac9e73bc1/generation_config.json\n", - "[INFO|configuration_utils.py:1038] 2024-09-15 00:45:39,943 >> 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", - "09/15/2024 00:45:39 - INFO - llamafactory.model.model_utils.attention - Using vanilla attention implementation.\n", - "09/15/2024 00:45:39 - INFO - llamafactory.model.loader - all params: 7,737,708,544\n", - "(2) GPU = NVIDIA GeForce RTX 4090. Max memory = 23.988 GB.\n", - "15.123 GB of memory reserved.\n", - "Evaluating model: internlm/internlm2_5-7b-chat-1m on cuda\n", - "*** Evaluating with num_shots: 0\n", - "loading train/test data files\n", - "Map: 100%|██████████████████████| 25000/25000 [00:00<00:00, 63044.48 examples/s]\n", - "Map: 100%|████████████████████████| 3000/3000 [00:00<00:00, 56745.73 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", - "你是一个情景猜谜游戏的主持人。游戏规则如下:\n", - "\n", - "1. 参与者会得到一个谜面,谜面会描述一个简单又难以理解的事件。\n", - "2. 主持人知道谜底,谜底是谜面的答案。\n", - "3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n", - "4. 对于每个问题,主持人将根据实际情况回答以下五个选项之一:是、不是、不重要、回答正确、问法错误。各回答的判断标准如下:\n", - " - 若谜面和谜底能找到问题的答案,回答:是或者不是\n", - " - 若谜面和谜底不能直接或者间接推断出问题的答案,回答:不重要\n", - " - 若参与者提问不是一个封闭式问题或者问题难以理解,回答:问法错误\n", - " - 若参与者提问基本还原了谜底真相,回答:回答正确\n", - "5. 回答中不能添加任何其它信息,也不能省略选项中的任何一个字。例如,不可以把“不是”省略成“不”。\n", - "\n", - "请严格按照这些规则回答参与者提出的问题。\n", - "\n", - "谜面: 在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", - "谜底: 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", - "参与者提出的问题: 甄加索是自杀吗\n", - "回答: \n", - "<|im_end|>\n", - "<|im_start|>assistant\n", - "不是\n", - "--------------------------------------------------\n", - "prompt: <|im_start|>system\n", - "You are an expert in logical reasoning.<|im_end|>\n", - "<|im_start|>user\n", - "你是一个情景猜谜游戏的主持人。游戏规则如下:\n", - "\n", - "1. 参与者会得到一个谜面,谜面会描述一个简单又难以理解的事件。\n", - "2. 主持人知道谜底,谜底是谜面的答案。\n", - "3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n", - "4. 对于每个问题,主持人将根据实际情况回答以下五个选项之一:是、不是、不重要、回答正确、问法错误。各回答的判断标准如下:\n", - " - 若谜面和谜底能找到问题的答案,回答:是或者不是\n", - " - 若谜面和谜底不能直接或者间接推断出问题的答案,回答:不重要\n", - " - 若参与者提问不是一个封闭式问题或者问题难以理解,回答:问法错误\n", - " - 若参与者提问基本还原了谜底真相,回答:回答正确\n", - "5. 回答中不能添加任何其它信息,也不能省略选项中的任何一个字。例如,不可以把“不是”省略成“不”。\n", - "\n", - "请严格按照这些规则回答参与者提出的问题。\n", - "\n", - "谜面: 在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", - "谜底: 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", - "参与者提出的问题: 甄加索是自杀吗\n", - "回答: \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", - "你是一个情景猜谜游戏的主持人。游戏规则如下:\n", - "\n", - "1. 参与者会得到一个谜面,谜面会描述一个简单又难以理解的事件。\n", - "2. 主持人知道谜底,谜底是谜面的答案。\n", - "3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n", - "4. 对于每个问题,主持人将根据实际情况回答以下五个选项之一:是、不是、不重要、回答正确、问法错误。各回答的判断标准如下:\n", - " - 若谜面和谜底能找到问题的答案,回答:是或者不是\n", - " - 若谜面和谜底不能直接或者间接推断出问题的答案,回答:不重要\n", - " - 若参与者提问不是一个封闭式问题或者问题难以理解,回答:问法错误\n", - " - 若参与者提问基本还原了谜底真相,回答:回答正确\n", - "5. 回答中不能添加任何其它信息,也不能省略选项中的任何一个字。例如,不可以把“不是”省略成“不”。\n", - "\n", - "请严格按照这些规则回答参与者提出的问题。\n", - "\n", - "谜面: 在一个安静的夜晚,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", - "谜底: 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", - "参与者提出的问题: 死者受伤了吗\n", - "回答: \n", - "<|im_end|>\n", - "<|im_start|>assistant\n", - "不是\n", - "--------------------------------------------------\n", - "prompt: <|im_start|>system\n", - "You are an expert in logical reasoning.<|im_end|>\n", - "<|im_start|>user\n", - "你是一个情景猜谜游戏的主持人。游戏规则如下:\n", - "\n", - "1. 参与者会得到一个谜面,谜面会描述一个简单又难以理解的事件。\n", - "2. 主持人知道谜底,谜底是谜面的答案。\n", - "3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n", - "4. 对于每个问题,主持人将根据实际情况回答以下五个选项之一:是、不是、不重要、回答正确、问法错误。各回答的判断标准如下:\n", - " - 若谜面和谜底能找到问题的答案,回答:是或者不是\n", - " - 若谜面和谜底不能直接或者间接推断出问题的答案,回答:不重要\n", - " - 若参与者提问不是一个封闭式问题或者问题难以理解,回答:问法错误\n", - " - 若参与者提问基本还原了谜底真相,回答:回答正确\n", - "5. 回答中不能添加任何其它信息,也不能省略选项中的任何一个字。例如,不可以把“不是”省略成“不”。\n", - "\n", - "请严格按照这些规则回答参与者提出的问题。\n", - "\n", - "谜面: 在一个安静的夜晚,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", - "谜底: 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", - "参与者提出的问题: 死者受伤了吗\n", - "回答: \n", - "<|im_end|>\n", - "<|im_start|>assistant\n", - "\n", - " 0%| | 0/3000 [00:00> loading file vocab.json from cache at /home/inflaton/.cache/huggingface/hub/models--Qwen--Qwen2-7B-Instruct/snapshots/f2826a00ceef68f0f2b946d945ecc0477ce4450c/vocab.json\n", - "[INFO|tokenization_utils_base.py:2289] 2024-09-15 00:53:34,709 >> loading file merges.txt from cache at /home/inflaton/.cache/huggingface/hub/models--Qwen--Qwen2-7B-Instruct/snapshots/f2826a00ceef68f0f2b946d945ecc0477ce4450c/merges.txt\n", - "[INFO|tokenization_utils_base.py:2289] 2024-09-15 00:53:34,709 >> loading file tokenizer.json from cache at /home/inflaton/.cache/huggingface/hub/models--Qwen--Qwen2-7B-Instruct/snapshots/f2826a00ceef68f0f2b946d945ecc0477ce4450c/tokenizer.json\n", - "[INFO|tokenization_utils_base.py:2289] 2024-09-15 00:53:34,709 >> loading file added_tokens.json from cache at None\n", - "[INFO|tokenization_utils_base.py:2289] 2024-09-15 00:53:34,709 >> loading file special_tokens_map.json from cache at None\n", - "[INFO|tokenization_utils_base.py:2289] 2024-09-15 00:53:34,709 >> loading file tokenizer_config.json from cache at /home/inflaton/.cache/huggingface/hub/models--Qwen--Qwen2-7B-Instruct/snapshots/f2826a00ceef68f0f2b946d945ecc0477ce4450c/tokenizer_config.json\n", - "[INFO|tokenization_utils_base.py:2533] 2024-09-15 00:53:34,845 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n", - "09/15/2024 00:53:34 - INFO - llamafactory.data.template - Replace eos token: <|im_end|>\n", - "config.json: 100%|█████████████████████████████| 663/663 [00:00<00:00, 9.06MB/s]\n", - "[INFO|configuration_utils.py:733] 2024-09-15 00:53:35,407 >> loading configuration file config.json from cache at /home/inflaton/.cache/huggingface/hub/models--Qwen--Qwen2-7B-Instruct/snapshots/f2826a00ceef68f0f2b946d945ecc0477ce4450c/config.json\n", - "[INFO|configuration_utils.py:800] 2024-09-15 00:53:35,407 >> Model config Qwen2Config {\n", - " \"_name_or_path\": \"Qwen/Qwen2-7B-Instruct\",\n", - " \"architectures\": [\n", - " \"Qwen2ForCausalLM\"\n", - " ],\n", - " \"attention_dropout\": 0.0,\n", - " \"bos_token_id\": 151643,\n", - " \"eos_token_id\": 151645,\n", - " \"hidden_act\": \"silu\",\n", - " \"hidden_size\": 3584,\n", - " \"initializer_range\": 0.02,\n", - " \"intermediate_size\": 18944,\n", - " \"max_position_embeddings\": 32768,\n", - " \"max_window_layers\": 28,\n", - " \"model_type\": \"qwen2\",\n", - " \"num_attention_heads\": 28,\n", - " \"num_hidden_layers\": 28,\n", - " \"num_key_value_heads\": 4,\n", - " \"rms_norm_eps\": 1e-06,\n", - " \"rope_theta\": 1000000.0,\n", - " \"sliding_window\": null,\n", - " \"tie_word_embeddings\": false,\n", - " \"torch_dtype\": \"bfloat16\",\n", - " \"transformers_version\": \"4.43.3\",\n", - " \"use_cache\": true,\n", - " \"use_sliding_window\": false,\n", - " \"vocab_size\": 152064\n", - "}\n", - "\n", - "09/15/2024 00:53:35 - INFO - llamafactory.model.patcher - Using KV cache for faster generation.\n", - "model.safetensors.index.json: 100%|█████████| 27.8k/27.8k [00:00<00:00, 184MB/s]\n", - "[INFO|modeling_utils.py:3634] 2024-09-15 00:53:36,518 >> loading weights file model.safetensors from cache at /home/inflaton/.cache/huggingface/hub/models--Qwen--Qwen2-7B-Instruct/snapshots/f2826a00ceef68f0f2b946d945ecc0477ce4450c/model.safetensors.index.json\n", - "Downloading shards: 0%| | 0/4 [00:00> Instantiating Qwen2ForCausalLM model under default dtype torch.bfloat16.\n", - "[INFO|configuration_utils.py:1038] 2024-09-15 01:15:03,865 >> Generate config GenerationConfig {\n", - " \"bos_token_id\": 151643,\n", - " \"eos_token_id\": 151645\n", - "}\n", - "\n", - "Loading checkpoint shards: 100%|██████████████████| 4/4 [00:41<00:00, 10.40s/it]\n", - "[INFO|modeling_utils.py:4463] 2024-09-15 01:15:45,989 >> All model checkpoint weights were used when initializing Qwen2ForCausalLM.\n", - "\n", - "[INFO|modeling_utils.py:4471] 2024-09-15 01:15:45,989 >> All the weights of Qwen2ForCausalLM were initialized from the model checkpoint at Qwen/Qwen2-7B-Instruct.\n", - "If your task is similar to the task the model of the checkpoint was trained on, you can already use Qwen2ForCausalLM for predictions without further training.\n", - "generation_config.json: 100%|███████████████████| 243/243 [00:00<00:00, 943kB/s]\n", - "[INFO|configuration_utils.py:993] 2024-09-15 01:15:47,301 >> loading configuration file generation_config.json from cache at /home/inflaton/.cache/huggingface/hub/models--Qwen--Qwen2-7B-Instruct/snapshots/f2826a00ceef68f0f2b946d945ecc0477ce4450c/generation_config.json\n", - "[INFO|configuration_utils.py:1038] 2024-09-15 01:15:47,301 >> Generate config GenerationConfig {\n", - " \"bos_token_id\": 151643,\n", - " \"do_sample\": true,\n", - " \"eos_token_id\": [\n", - " 151645,\n", - " 151643\n", - " ],\n", - " \"pad_token_id\": 151643,\n", - " \"repetition_penalty\": 1.05,\n", - " \"temperature\": 0.7,\n", - " \"top_k\": 20,\n", - " \"top_p\": 0.8\n", - "}\n", - "\n", - "09/15/2024 01:15:47 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.\n", - "09/15/2024 01:15:47 - INFO - llamafactory.model.loader - all params: 7,615,616,512\n", - "(2) GPU = NVIDIA GeForce RTX 4090. Max memory = 23.988 GB.\n", - "15.895 GB of memory reserved.\n", - "Evaluating model: Qwen/Qwen2-7B-Instruct on cuda\n", - "*** Evaluating with num_shots: 0\n", - "loading train/test data files\n", - "Map: 100%|██████████████████████| 25000/25000 [00:00<00:00, 61986.68 examples/s]\n", - "Map: 100%|████████████████████████| 3000/3000 [00:00<00:00, 59084.41 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", - "你是一个情景猜谜游戏的主持人。游戏规则如下:\n", - "\n", - "1. 参与者会得到一个谜面,谜面会描述一个简单又难以理解的事件。\n", - "2. 主持人知道谜底,谜底是谜面的答案。\n", - "3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n", - "4. 对于每个问题,主持人将根据实际情况回答以下五个选项之一:是、不是、不重要、回答正确、问法错误。各回答的判断标准如下:\n", - " - 若谜面和谜底能找到问题的答案,回答:是或者不是\n", - " - 若谜面和谜底不能直接或者间接推断出问题的答案,回答:不重要\n", - " - 若参与者提问不是一个封闭式问题或者问题难以理解,回答:问法错误\n", - " - 若参与者提问基本还原了谜底真相,回答:回答正确\n", - "5. 回答中不能添加任何其它信息,也不能省略选项中的任何一个字。例如,不可以把“不是”省略成“不”。\n", - "\n", - "请严格按照这些规则回答参与者提出的问题。\n", - "\n", - "谜面: 在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", - "谜底: 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", - "参与者提出的问题: 甄加索是自杀吗\n", - "回答: \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", - "你是一个情景猜谜游戏的主持人。游戏规则如下:\n", - "\n", - "1. 参与者会得到一个谜面,谜面会描述一个简单又难以理解的事件。\n", - "2. 主持人知道谜底,谜底是谜面的答案。\n", - "3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n", - "4. 对于每个问题,主持人将根据实际情况回答以下五个选项之一:是、不是、不重要、回答正确、问法错误。各回答的判断标准如下:\n", - " - 若谜面和谜底能找到问题的答案,回答:是或者不是\n", - " - 若谜面和谜底不能直接或者间接推断出问题的答案,回答:不重要\n", - " - 若参与者提问不是一个封闭式问题或者问题难以理解,回答:问法错误\n", - " - 若参与者提问基本还原了谜底真相,回答:回答正确\n", - "5. 回答中不能添加任何其它信息,也不能省略选项中的任何一个字。例如,不可以把“不是”省略成“不”。\n", - "\n", - "请严格按照这些规则回答参与者提出的问题。\n", - "\n", - "谜面: 在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", - "谜底: 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", - "参与者提出的问题: 甄加索是自杀吗\n", - "回答: \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", - "你是一个情景猜谜游戏的主持人。游戏规则如下:\n", - "\n", - "1. 参与者会得到一个谜面,谜面会描述一个简单又难以理解的事件。\n", - "2. 主持人知道谜底,谜底是谜面的答案。\n", - "3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n", - "4. 对于每个问题,主持人将根据实际情况回答以下五个选项之一:是、不是、不重要、回答正确、问法错误。各回答的判断标准如下:\n", - " - 若谜面和谜底能找到问题的答案,回答:是或者不是\n", - " - 若谜面和谜底不能直接或者间接推断出问题的答案,回答:不重要\n", - " - 若参与者提问不是一个封闭式问题或者问题难以理解,回答:问法错误\n", - " - 若参与者提问基本还原了谜底真相,回答:回答正确\n", - "5. 回答中不能添加任何其它信息,也不能省略选项中的任何一个字。例如,不可以把“不是”省略成“不”。\n", - "\n", - "请严格按照这些规则回答参与者提出的问题。\n", - "\n", - "谜面: 在一个安静的夜晚,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��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", - "谜底: 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", - "参与者提出的问题: 死者受伤了吗\n", - "回答: \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", - "你是一个情景猜谜游戏的主持人。游戏规则如下:\n", - "\n", - "1. 参与者会得到一个谜面,谜面会描述一个简单又难以理解的事件。\n", - "2. 主持人知道谜底,谜底是谜面的答案。\n", - "3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n", - "4. 对于每个问题,主持人将根据实际情况回答以下五个选项之一:是、不是、不重要、回答正确、问法错误。各回答的判断标准如下:\n", - " - 若谜面和谜底能找到问题的答案,回答:是或者不是\n", - " - 若谜面和谜底不能直接或者间接推断出问题的答案,回答:不重要\n", - " - 若参与者提问不是一个封闭式问题或者问题难以理解,回答:问法错误\n", - " - 若参与者提问基本还原了谜底真相,回答:回答正确\n", - "5. 回答中不能添加任何其它信息,也不能省略选项中的任何一个字。例如,不可以把“不是”省略成“不”。\n", - "\n", - "请严格按照这些规则回答参与者提出的问题。\n", - "\n", - "谜面: 在一个安静的夜晚,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", - "谜底: 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", - "参与者提出的问题: 死者受伤了吗\n", - "回答: \n", - "<|im_end|>\n", - "<|im_start|>assistant\n", - "\n", - " 0%| | 0/3000 [00:00> loading file tokenizer.json from cache at /home/inflaton/.cache/huggingface/hub/models--shenzhi-wang--Llama3.1-8B-Chinese-Chat/snapshots/404a735a6205e5ef992f589b6d5d28922822928e/tokenizer.json\n", - "[INFO|tokenization_utils_base.py:2289] 2024-09-15 01:23:32,283 >> loading file added_tokens.json from cache at None\n", - "[INFO|tokenization_utils_base.py:2289] 2024-09-15 01:23:32,283 >> loading file special_tokens_map.json from cache at /home/inflaton/.cache/huggingface/hub/models--shenzhi-wang--Llama3.1-8B-Chinese-Chat/snapshots/404a735a6205e5ef992f589b6d5d28922822928e/special_tokens_map.json\n", - "[INFO|tokenization_utils_base.py:2289] 2024-09-15 01:23:32,283 >> loading file tokenizer_config.json from cache at /home/inflaton/.cache/huggingface/hub/models--shenzhi-wang--Llama3.1-8B-Chinese-Chat/snapshots/404a735a6205e5ef992f589b6d5d28922822928e/tokenizer_config.json\n", - "[INFO|tokenization_utils_base.py:2533] 2024-09-15 01:23:32,483 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n", - "09/15/2024 01:23:32 - INFO - llamafactory.data.template - Replace eos token: <|eot_id|>\n", - "[INFO|configuration_utils.py:733] 2024-09-15 01:23:32,745 >> loading configuration file config.json from cache at /home/inflaton/.cache/huggingface/hub/models--shenzhi-wang--Llama3.1-8B-Chinese-Chat/snapshots/404a735a6205e5ef992f589b6d5d28922822928e/config.json\n", - "[INFO|configuration_utils.py:800] 2024-09-15 01:23:32,745 >> Model config LlamaConfig {\n", - " \"_name_or_path\": \"shenzhi-wang/Llama3.1-8B-Chinese-Chat\",\n", - " \"architectures\": [\n", - " \"LlamaForCausalLM\"\n", - " ],\n", - " \"attention_bias\": false,\n", - " \"attention_dropout\": 0.0,\n", - " \"bos_token_id\": 128000,\n", - " \"eos_token_id\": [\n", - " 128001,\n", - " 128008,\n", - " 128009\n", - " ],\n", - " \"hidden_act\": \"silu\",\n", - " \"hidden_size\": 4096,\n", - " \"initializer_range\": 0.02,\n", - " \"intermediate_size\": 14336,\n", - " \"max_position_embeddings\": 131072,\n", - " \"mlp_bias\": false,\n", - " \"model_type\": \"llama\",\n", - " \"num_attention_heads\": 32,\n", - " \"num_hidden_layers\": 32,\n", - " \"num_key_value_heads\": 8,\n", - " \"pretraining_tp\": 1,\n", - " \"rms_norm_eps\": 1e-05,\n", - " \"rope_scaling\": {\n", - " \"factor\": 8.0,\n", - " \"high_freq_factor\": 4.0,\n", - " \"low_freq_factor\": 1.0,\n", - " \"original_max_position_embeddings\": 8192,\n", - " \"rope_type\": \"llama3\"\n", - " },\n", - " \"rope_theta\": 500000.0,\n", - " \"tie_word_embeddings\": false,\n", - " \"torch_dtype\": \"bfloat16\",\n", - " \"transformers_version\": \"4.43.3\",\n", - " \"use_cache\": true,\n", - " \"vocab_size\": 128256\n", - "}\n", - "\n", - "09/15/2024 01:23:32 - INFO - llamafactory.model.patcher - Using KV cache for faster generation.\n", - "[INFO|modeling_utils.py:3634] 2024-09-15 01:23:32,759 >> loading weights file model.safetensors from cache at /home/inflaton/.cache/huggingface/hub/models--shenzhi-wang--Llama3.1-8B-Chinese-Chat/snapshots/404a735a6205e5ef992f589b6d5d28922822928e/model.safetensors.index.json\n", - "[INFO|modeling_utils.py:1572] 2024-09-15 01:23:32,790 >> Instantiating LlamaForCausalLM model under default dtype torch.bfloat16.\n", - "[INFO|configuration_utils.py:1038] 2024-09-15 01:23:32,791 >> Generate config GenerationConfig {\n", - " \"bos_token_id\": 128000,\n", - " \"eos_token_id\": [\n", - " 128001,\n", - " 128008,\n", - " 128009\n", - " ]\n", - "}\n", - "\n", - "Loading checkpoint shards: 100%|██████████████████| 4/4 [02:06<00:00, 31.72s/it]\n", - "[INFO|modeling_utils.py:4463] 2024-09-15 01:25:39,853 >> All model checkpoint weights were used when initializing LlamaForCausalLM.\n", - "\n", - "[INFO|modeling_utils.py:4471] 2024-09-15 01:25:39,853 >> All the weights of LlamaForCausalLM were initialized from the model checkpoint at shenzhi-wang/Llama3.1-8B-Chinese-Chat.\n", - "If your task is similar to the task the model of the checkpoint was trained on, you can already use LlamaForCausalLM for predictions without further training.\n", - "[INFO|modeling_utils.py:3959] 2024-09-15 01:25:40,184 >> Generation config file not found, using a generation config created from the model config.\n", - "09/15/2024 01:25:40 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.\n", - "09/15/2024 01:25:40 - INFO - llamafactory.model.loader - all params: 8,030,261,248\n", - "(2) GPU = NVIDIA GeForce RTX 4090. Max memory = 23.988 GB.\n", - "15.084 GB of memory reserved.\n", - "Evaluating model: shenzhi-wang/Llama3.1-8B-Chinese-Chat on cuda\n", - "*** Evaluating with num_shots: 0\n", - "loading train/test data files\n", - "Map: 100%|██████████████████████| 25000/25000 [00:00<00:00, 61647.62 examples/s]\n", - "Map: 100%|████████████████████████| 3000/3000 [00:00<00:00, 54446.26 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: <|begin_of_text|><|start_header_id|>system<|end_header_id|>\n", - "\n", - "You are an expert in logical reasoning.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", - "\n", - "你是一个情景猜谜游戏的主持人。游戏规则如下:\n", - "\n", - "1. 参与者会得到一个谜面,谜面会描述一个简单又难以理解的事件。\n", - "2. 主持人知道谜底,谜底是谜面的答案。\n", - "3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n", - "4. 对于每个问题,主持人将根据实际情况回答以下五个选项之一:是、不是、不重要、回答正确、问法错误。各回答的判断标准如下:\n", - " - 若谜面和谜底能找到问题的答案,回答:是或者不是\n", - " - 若谜面和谜底不能直接或者间接推断出问题的答案,回答:不重要\n", - " - 若参与者提问不是一个封闭式问题或者问题难以理解,回答:问法错误\n", - " - 若参与者提问基本还原了谜底真相,回答:回答正确\n", - "5. 回答中不能添加任何其它信息,也不能省略选项中的任何一个字。例如,不可以把“不是”省略成“不”。\n", - "\n", - "请严格按照这些规则回答参与者提出的问题。\n", - "\n", - "谜面: 在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", - "谜底: 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", - "参与者提出的问题: 甄加索是自杀吗\n", - "回答: \n", - "<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n", - "\n", - "不是<|eot_id|>\n", - "--------------------------------------------------\n", - "prompt: <|begin_of_text|><|start_header_id|>system<|end_header_id|>\n", - "\n", - "You are an expert in logical reasoning.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", - "\n", - "你是一个情景猜谜游戏的主持人。游戏规则如下:\n", - "\n", - "1. 参与者会得到一个谜面,谜面会描述一个简单又难以理解的事件。\n", - "2. 主持人知道谜底,谜底是谜面的答案。\n", - "3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n", - "4. 对于每个问题,主持人将根据实际情况回答以下五个选项之一:是、不是、不重要、回答正确、问法错误。各回答的判断标准如下:\n", - " - 若谜面和谜底能找到问题的答案,回答:是或者不是\n", - " - 若谜面和谜底不能直接或者间接推断出问题的答案,回答:不重要\n", - " - 若参与者提问不是一个封闭式问题或者问题难以理解,回答:问法错误\n", - " - 若参与者提问基本还原了谜底真相,回答:回答正确\n", - "5. 回答中不能添加任何其它信息,也不能省略选项中的任何一个字。例如,不可以把“不是”省略成“不”。\n", - "\n", - "请严格按照这些规则回答参与者提出的问题。\n", - "\n", - "谜面: 在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", - "谜底: 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��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", - "参与者提出的问题: 甄加索是自杀吗\n", - "回答: \n", - "<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n", - "\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: <|begin_of_text|><|start_header_id|>system<|end_header_id|>\n", - "\n", - "You are an expert in logical reasoning.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", - "\n", - "你是一个情景猜谜游戏的主持人。游戏规则如下:\n", - "\n", - "1. 参与者会得到一个谜面,谜面会描述一个简单又难以理解的事件。\n", - "2. 主持人知道谜底,谜底是谜面的答案。\n", - "3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n", - "4. 对于每个问题,主持人将根据实际情况回答以下五个选项之一:是、不是、不重要、回答正确、问法错误。各回答的判断标准如下:\n", - " - 若谜面和谜底能找到问题的答案,回答:是或者不是\n", - " - 若谜面和谜底不能直接或者间接推断出问题的答案,回答:不重要\n", - " - 若参与者提问不是一个封闭式问题或者问题难以理解,回答:问法错误\n", - " - 若参与者提问基本还原了谜底真相,回答:回答正确\n", - "5. 回答中不能添加任何其它信息,也不能省略选项中的任何一个字。例如,不可以把“不是”省略成“不”。\n", - "\n", - "请严格按照这些规则回答参与者提出的问题。\n", - "\n", - "谜面: 在一个安静的夜晚,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", - "谜底: 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", - "参与者提出的问题: 死者受伤了吗\n", - "回答: \n", - "<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n", - "\n", - "不是<|eot_id|>\n", - "--------------------------------------------------\n", - "prompt: <|begin_of_text|><|start_header_id|>system<|end_header_id|>\n", - "\n", - "You are an expert in logical reasoning.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", - "\n", - "你是一个情景猜谜游戏的主持人。游戏规则如下:\n", - "\n", - "1. 参与者会得到一个谜面,谜面会描述一个简单又难以理解的事件。\n", - "2. 主持人知道谜底,谜底是谜面的答案。\n", - "3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n", - "4. 对于每个问题,主持人将根据实际情况回答以下五个选项之一:是、不是、不重要、回答正确、问法错误。各回答的判断标准如下:\n", - " - 若谜面和谜底能找到问题的答案,回答:是或者不是\n", - " - 若谜面和谜底不能直接或者间接推断出问题的答案,回答:不重要\n", - " - 若参与者提问不是一个封闭式问题或者问题难以理解,回答:问法错误\n", - " - 若参与者提问基本还原了谜底真相,回答:回答正确\n", - "5. 回答中不能添加任何其它信息,也不能省略选项中的任何一个字。例如,不可以把“不是”省略成“不”。\n", - "\n", - "请严格按照这些规则回答参与者提出的问题。\n", - "\n", - "谜面: 在一个安静的夜晚,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", - "谜底: 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", - "参与者提出的问题: 死者受伤了吗\n", - "回答: \n", - "<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n", - "\n", - "\n", - " 0%| | 0/3000 [00:00> Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n", - "Batch 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453, 456, 457, 465, 468, 469, 470, 471, 478, 481, 484, 487, 488, 490, 493, 494, 497, 500, 501, 502, 503, 504, 506, 507, 513, 516, 517, 518, 519, 530, 531, 536, 538, 544, 552, 555, 560, 569, 570, 571, 579, 581, 589, 593, 596, 597, 599, 601, 606, 614, 622, 624, 632, 635, 636, 646, 647, 651, 657, 671, 674, 680, 682, 686, 695, 700, 717, 721, 727, 729, 730, 732, 738, 739, 740, 742, 748, 754, 758, 764, 773, 774, 781, 788, 791, 794, 798, 801, 809, 813, 819, 820, 823, 824, 826, 827, 828, 840, 841, 842, 844, 847, 849, 851, 856, 861, 862, 866, 867, 869, 873, 875, 886, 890, 904, 906, 915, 918, 919, 922, 927, 929, 935, 940, 942, 943, 953, 964, 965, 966, 969, 974, 977, 980, 994, 998, 1001, 1005, 1006, 1011, 1012, 1014, 1015, 1017, 1018, 1019, 1022, 1024, 1027, 1028, 1030, 1036, 1038, 1040, 1043, 1051, 1052, 1055, 1057, 1061, 1069, 1074, 1076, 1081, 1083, 1087, 1089, 1096, 1107, 1117, 1125, 1126, 1129, 1137, 1141, 1143, 1147, 1161, 1164, 1166, 1174, 1178, 1180, 1181, 1183, 1185, 1188, 1192, 1198, 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2440, 2442, 2463, 2465, 2469, 2471, 2472, 2475, 2476, 2486, 2491, 2499, 2501, 2503, 2511, 2515, 2520, 2522, 2524, 2529, 2530, 2532, 2535, 2538, 2546, 2558, 2559, 2575, 2577, 2589, 2592, 2594, 2605, 2626, 2628, 2629, 2633, 2644, 2649, 2653, 2663, 2664, 2666, 2667, 2671, 2676, 2685, 2701, 2710, 2712, 2716, 2727, 2731, 2732, 2735, 2736, 2742, 2745, 2746, 2752, 2754, 2757, 2758, 2760, 2762, 2766, 2770, 2777, 2782, 2787, 2788, 2789, 2791, 2794, 2797, 2798, 2800, 2805, 2806, 2807, 2810, 2811, 2815, 2816, 2820, 2823, 2824, 2829, 2834, 2837, 2843, 2845, 2851, 2854, 2858, 2866, 2870, 2877, 2880, 2882, 2884, 2888, 2896, 2902, 2905, 2912, 2913, 2916, 2921, 2927, 2929, 2937, 2939, 2944, 2949, 2953, 2964, 2966, 2967, 2969, 2975, 2977, 2979, 2985, 2990, 2995, 2998], 'precision': 0.7477056799746837, 'recall': 0.742, 'f1': 0.7371050181385632, 'ratio_valid_classifications': 0.8033333333333333}\n", - "(3) GPU = NVIDIA GeForce RTX 4090. Max memory = 23.988 GB.\n", - "18.621 GB of memory reserved.\n", - "CPU times: user 49.8 s, sys: 22.7 s, total: 1min 12s\n", - "Wall time: 1h 49min 42s\n" - ] - } - ], - "source": [ - "%%time\n", - "\n", - "# zero-shot\n", - "!./scripts/eval-mgtv-shots.sh" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "application/vnd.databricks.v1+notebook": { - "dashboards": [], - "environmentMetadata": null, - "language": "python", - "notebookMetadata": { - "pythonIndentUnit": 4 - }, - "notebookName": "07_MAC_+_Qwen2-7B-Instructi_Unsloth_train", - "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" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} +version https://git-lfs.github.com/spec/v1 +oid sha256:8cb55fdcf6c1aed08f8c1276ad01080ea5089147de6ab9681da58b15cc8f6c0d +size 2151945