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. ECC |\n",
- "| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n",
- "| | | MIG M. |\n",
- "|=========================================+========================+======================|\n",
- "| 0 NVIDIA GeForce RTX 4090 On | 00000000:01:00.0 On | Off |\n",
- "| 0% 51C P8 20W / 450W | 1856MiB / 24564MiB | 3% 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",
- "Linux Gen-AI 5.15.133.1-microsoft-standard-WSL2 #1 SMP Thu Oct 5 21:02:42 UTC 2023 x86_64 x86_64 x86_64 GNU/Linux\n",
- "PRETTY_NAME=\"Ubuntu 22.04.2 LTS\"\n",
- "NAME=\"Ubuntu\"\n",
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- "VERSION=\"22.04.2 LTS (Jammy Jellyfish)\"\n",
- "VERSION_CODENAME=jammy\n",
- "ID=ubuntu\n",
- "ID_LIKE=debian\n",
- "HOME_URL=\"https://www.ubuntu.com/\"\n",
- "SUPPORT_URL=\"https://help.ubuntu.com/\"\n",
- "BUG_REPORT_URL=\"https://bugs.launchpad.net/ubuntu/\"\n",
- "PRIVACY_POLICY_URL=\"https://www.ubuntu.com/legal/terms-and-policies/privacy-policy\"\n",
- "UBUNTU_CODENAME=jammy\n",
- "Architecture: x86_64\n",
- " CPU op-mode(s): 32-bit, 64-bit\n",
- " Address sizes: 39 bits physical, 48 bits virtual\n",
- " Byte Order: Little Endian\n",
- "CPU(s): 32\n",
- " On-line CPU(s) list: 0-31\n",
- "Vendor ID: GenuineIntel\n",
- " Model name: 13th Gen Intel(R) Core(TM) i9-13900KF\n",
- " CPU family: 6\n",
- " Model: 183\n",
- " Thread(s) per core: 2\n",
- " Core(s) per socket: 16\n",
- " Socket(s): 1\n",
- " Stepping: 1\n",
- " BogoMIPS: 5990.39\n",
- " Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mc\n",
- " a cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscal\n",
- " l nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopo\n",
- " logy tsc_reliable nonstop_tsc cpuid pni pclmulqdq vmx s\n",
- " sse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt tsc_dea\n",
- " dline_timer aes xsave avx f16c rdrand hypervisor lahf_l\n",
- " m abm 3dnowprefetch ssbd ibrs ibpb stibp ibrs_enhanced \n",
- " tpr_shadow vnmi ept vpid ept_ad fsgsbase tsc_adjust bmi\n",
- " 1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushop\n",
- " t clwb sha_ni xsaveopt xsavec xgetbv1 xsaves avx_vnni u\n",
- " mip waitpkg gfni vaes vpclmulqdq rdpid movdiri movdir64\n",
- " b fsrm md_clear serialize flush_l1d arch_capabilities\n",
- "Virtualization features: \n",
- " Virtualization: VT-x\n",
- " Hypervisor vendor: Microsoft\n",
- " Virtualization type: full\n",
- "Caches (sum of all): \n",
- " L1d: 768 KiB (16 instances)\n",
- " L1i: 512 KiB (16 instances)\n",
- " L2: 32 MiB (16 instances)\n",
- " L3: 36 MiB (1 instance)\n",
- "Vulnerabilities: \n",
- " Gather data sampling: Not affected\n",
- " Itlb multihit: Not affected\n",
- " L1tf: Not affected\n",
- " Mds: Not affected\n",
- " Meltdown: Not affected\n",
- " Mmio stale data: Not affected\n",
- " Retbleed: Mitigation; Enhanced IBRS\n",
- " Spec rstack overflow: Not affected\n",
- " Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\n",
- " and seccomp\n",
- " Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer\n",
- " sanitization\n",
- " Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB fillin\n",
- " g, PBRSB-eIBRS SW sequence\n",
- " Srbds: Not affected\n",
- " Tsx async abort: Not affected\n",
- "MemTotal: 49330020 kB\n",
- "Current Directory:\n",
- "/home/inflaton/code/logical-reasoning\n",
- "Evaluating shenzhi-wang/Mistral-7B-v0.3-Chinese-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",
- "shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat None False datasets/mgtv data/Mistral-7B-v0.3-Chinese-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: 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",
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- "Batch output: ['不是。']\n",
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- " 65%|█████████████████████████▎ | 1948/3000 [16:53<08:58, 1.96it/s][WARNING|utils.py:1542] 2024-09-15 00:05:14,720 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 65%|█████████████████████████▎ | 1949/3000 [16:54<09:05, 1.93it/s][WARNING|utils.py:1542] 2024-09-15 00:05:15,256 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 65%|█████████████████████████▎ | 1950/3000 [16:55<09:10, 1.91it/s][WARNING|utils.py:1542] 2024-09-15 00:05:15,792 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 65%|█████████████████████████▎ | 1951/3000 [16:55<09:13, 1.90it/s][WARNING|utils.py:1542] 2024-09-15 00:05:16,327 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 65%|█████████████████████████▍ | 1952/3000 [16:56<08:42, 2.00it/s][WARNING|utils.py:1542] 2024-09-15 00:05:16,760 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 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",
- " 67%|██████████████████████████ | 2001/3000 [17:25<09:35, 1.74it/s][WARNING|utils.py:1542] 2024-09-15 00:05:46,639 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 67%|██████████████████████████ | 2002/3000 [17:26<09:21, 1.78it/s][WARNING|utils.py:1542] 2024-09-15 00:05:47,171 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 67%|██████████████████████████ | 2003/3000 [17:26<09:13, 1.80it/s][WARNING|utils.py:1542] 2024-09-15 00:05:47,709 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 67%|██████████████████████████ | 2004/3000 [17:27<08:35, 1.93it/s][WARNING|utils.py:1542] 2024-09-15 00:05:48,139 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 67%|██████████████████████████ | 2005/3000 [17:27<08:40, 1.91it/s][WARNING|utils.py:1542] 2024-09-15 00:05:48,675 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 67%|██████████████████████████ | 2006/3000 [17:28<08:42, 1.90it/s][WARNING|utils.py:1542] 2024-09-15 00:05:49,207 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 67%|██████████████████████████ | 2007/3000 [17:29<08:45, 1.89it/s][WARNING|utils.py:1542] 2024-09-15 00:05:49,744 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 67%|██████████████████████████ | 2008/3000 [17:29<08:46, 1.89it/s][WARNING|utils.py:1542] 2024-09-15 00:05:50,277 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 67%|██████████████████████████ | 2009/3000 [17:30<08:47, 1.88it/s][WARNING|utils.py:1542] 2024-09-15 00:05:50,813 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 67%|██████████████████████████▏ | 2010/3000 [17:30<08:16, 1.99it/s][WARNING|utils.py:1542] 2024-09-15 00:05:51,242 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 67%|██████████████████████████▏ | 2011/3000 [17:31<08:26, 1.95it/s][WARNING|utils.py:1542] 2024-09-15 00:05:51,780 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 67%|██████████████████████████▏ | 2012/3000 [17:31<08:01, 2.05it/s][WARNING|utils.py:1542] 2024-09-15 00:05:52,208 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 67%|██████████████████████████▏ | 2013/3000 [17:31<07:43, 2.13it/s][WARNING|utils.py:1542] 2024-09-15 00:05:52,638 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 67%|██████████████████████████▏ | 2014/3000 [17:32<07:32, 2.18it/s][WARNING|utils.py:1542] 2024-09-15 00:05:53,071 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 67%|██████████████████████████▏ | 2015/3000 [17:32<07:55, 2.07it/s][WARNING|utils.py:1542] 2024-09-15 00:05:53,609 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 67%|██████████████████████████▏ | 2016/3000 [17:33<08:10, 2.01it/s][WARNING|utils.py:1542] 2024-09-15 00:05:54,145 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 67%|██████████████████████████▏ | 2017/3000 [17:33<07:49, 2.09it/s][WARNING|utils.py:1542] 2024-09-15 00:05:54,575 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 67%|██████████████████████████▏ | 2018/3000 [17:34<08:05, 2.02it/s][WARNING|utils.py:1542] 2024-09-15 00:05:55,109 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 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",
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- " 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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- " 68%|██████���███████████████████▎ | 2026/3000 [17:38<07:59, 2.03it/s][WARNING|utils.py:1542] 2024-09-15 00:05:58,976 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 68%|██████████████████████████▎ | 2027/3000 [17:38<07:40, 2.11it/s][WARNING|utils.py:1542] 2024-09-15 00:05:59,405 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 68%|██████████████████████████▎ | 2028/3000 [17:39<07:58, 2.03it/s][WARNING|utils.py:1542] 2024-09-15 00:05:59,941 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
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- " 68%|██████████████████████████▍ | 2030/3000 [17:40<08:17, 1.95it/s][WARNING|utils.py:1542] 2024-09-15 00:06:01,008 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
- " 68%|██████████████████████████▍ | 2031/3000 [17:40<08:23, 1.92it/s][WARNING|utils.py:1542] 2024-09-15 00:06:01,544 >> Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
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- "/home/inflaton/miniconda3/envs/llm-finetuning/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. 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, 384, 386, 387, 389, 394, 396, 397, 406, 411, 414, 421, 425, 428, 430, 431, 436, 438, 439, 445, 447, 450, 451, 452, 453, 454, 457, 459, 460, 464, 465, 467, 469, 470, 471, 472, 477, 481, 482, 486, 487, 490, 497, 500, 501, 502, 503, 504, 506, 507, 512, 515, 516, 517, 518, 519, 520, 531, 533, 536, 538, 543, 552, 553, 555, 560, 569, 570, 571, 576, 579, 581, 589, 593, 596, 597, 599, 605, 614, 621, 622, 632, 633, 636, 646, 654, 674, 689, 691, 692, 693, 694, 695, 700, 706, 707, 717, 720, 721, 727, 730, 734, 738, 740, 742, 748, 752, 754, 755, 758, 760, 764, 766, 770, 773, 774, 778, 782, 788, 791, 795, 798, 799, 801, 813, 816, 817, 819, 820, 823, 824, 826, 827, 828, 832, 834, 841, 843, 845, 847, 848, 851, 852, 854, 856, 861, 864, 866, 869, 870, 871, 873, 875, 886, 893, 894, 899, 901, 904, 909, 914, 915, 918, 919, 921, 923, 927, 932, 933, 936, 939, 940, 949, 953, 959, 960, 961, 965, 966, 969, 980, 982, 984, 991, 994, 998, 1003, 1011, 1012, 1014, 1015, 1016, 1018, 1019, 1020, 1022, 1024, 1026, 1028, 1029, 1030, 1032, 1033, 1036, 1038, 1043, 1044, 1048, 1049, 1051, 1057, 1058, 1062, 1063, 1069, 1073, 1074, 1077, 1081, 1084, 1087, 1089, 1093, 1102, 1108, 1113, 1117, 1120, 1124, 1125, 1128, 1130, 1133, 1134, 1141, 1143, 1147, 1152, 1158, 1161, 1166, 1171, 1172, 1174, 1177, 1180, 1181, 1183, 1185, 1188, 1192, 1196, 1198, 1203, 1206, 1212, 1220, 1221, 1222, 1227, 1228, 1236, 1239, 1241, 1245, 1251, 1252, 1259, 1267, 1271, 1272, 1274, 1276, 1278, 1279, 1282, 1286, 1289, 1290, 1292, 1293, 1296, 1298, 1299, 1305, 1308, 1313, 1316, 1317, 1318, 1322, 1324, 1329, 1330, 1331, 1339, 1342, 1348, 1349, 1358, 1359, 1364, 1367, 1371, 1373, 1374, 1377, 1379, 1385, 1387, 1388, 1399, 1402, 1405, 1406, 1416, 1417, 1419, 1422, 1423, 1426, 1427, 1432, 1440, 1446, 1451, 1452, 1453, 1454, 1459, 1469, 1474, 1476, 1478, 1484, 1490, 1491, 1495, 1499, 1501, 1502, 1504, 1506, 1507, 1510, 1515, 1516, 1517, 1518, 1522, 1524, 1525, 1535, 1536, 1540, 1543, 1545, 1547, 1548, 1549, 1551, 1554, 1558, 1561, 1562, 1568, 1575, 1578, 1580, 1581, 1588, 1594, 1600, 1601, 1604, 1605, 1607, 1611, 1613, 1614, 1619, 1623, 1624, 1627, 1631, 1633, 1637, 1644, 1648, 1650, 1652, 1653, 1655, 1657, 1662, 1665, 1672, 1678, 1679, 1680, 1686, 1691, 1695, 1697, 1698, 1710, 1716, 1718, 1722, 1729, 1734, 1735, 1737, 1738, 1739, 1741, 1743, 1751, 1753, 1755, 1759, 1762, 1766, 1769, 1771, 1779, 1780, 1798, 1800, 1810, 1812, 1818, 1824, 1826, 1831, 1835, 1848, 1857, 1858, 1859, 1863, 1867, 1875, 1879, 1881, 1884, 1887, 1890, 1892, 1894, 1897, 1899, 1903, 1906, 1907, 1915, 1916, 1919, 1923, 1928, 1930, 1940, 1941, 1948, 1953, 1957, 1962, 1963, 1964, 1970, 1973, 1977, 1978, 1979, 1981, 1984, 1985, 1986, 1989, 1990, 1995, 1998, 2000, 2004, 2007, 2011, 2017, 2022, 2024, 2028, 2029, 2031, 2035, 2036, 2038, 2041, 2043, 2044, 2048, 2049, 2054, 2064, 2067, 2068, 2069, 2070, 2076, 2077, 2078, 2079, 2081, 2087, 2088, 2093, 2099, 2100, 2105, 2106, 2109, 2110, 2114, 2119, 2120, 2124, 2126, 2132, 2133, 2141, 2142, 2146, 2147, 2159, 2160, 2161, 2162, 2164, 2167, 2173, 2174, 2177, 2185, 2186, 2187, 2190, 2194, 2196, 2199, 2210, 2212, 2214, 2222, 2223, 2226, 2229, 2233, 2237, 2238, 2240, 2241, 2243, 2245, 2260, 2262, 2263, 2265, 2274, 2278, 2279, 2280, 2281, 2287, 2289, 2297, 2298, 2302, 2306, 2311, 2317, 2318, 2324, 2330, 2331, 2333, 2339, 2342, 2344, 2348, 2351, 2352, 2354, 2355, 2358, 2360, 2366, 2367, 2370, 2372, 2373, 2379, 2381, 2384, 2395, 2396, 2400, 2404, 2406, 2407, 2408, 2409, 2412, 2416, 2421, 2422, 2423, 2429, 2442, 2453, 2465, 2470, 2472, 2473, 2475, 2480, 2486, 2500, 2502, 2503, 2506, 2511, 2513, 2515, 2517, 2522, 2524, 2529, 2530, 2535, 2540, 2544, 2545, 2559, 2570, 2573, 2575, 2576, 2581, 2589, 2590, 2592, 2597, 2602, 2604, 2605, 2616, 2631, 2634, 2640, 2644, 2658, 2661, 2663, 2664, 2667, 2676, 2677, 2688, 2692, 2701, 2711, 2712, 2713, 2717, 2722, 2726, 2730, 2731, 2732, 2736, 2738, 2745, 2746, 2753, 2754, 2757, 2758, 2766, 2768, 2769, 2770, 2771, 2774, 2775, 2776, 2786, 2787, 2788, 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, ?it/s]Batch output: ['不是']\n",
- "100%|███████████████████████████████████████| 3000/3000 [07:45<00:00, 6.45it/s]\n",
- "/home/inflaton/miniconda3/envs/llm-finetuning/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "internlm/internlm2_5-7b-chat/shots-10 metrics: {'accuracy': 0.705, 'incorrect_ids': [6, 15, 16, 18, 19, 27, 28, 31, 34, 36, 43, 48, 51, 52, 56, 57, 59, 60, 61, 66, 67, 78, 80, 81, 91, 94, 97, 106, 108, 110, 112, 119, 120, 121, 123, 126, 128, 129, 135, 137, 138, 139, 141, 143, 149, 150, 153, 155, 161, 163, 170, 173, 176, 179, 181, 186, 199, 200, 201, 208, 218, 227, 229, 238, 241, 243, 248, 250, 256, 259, 260, 261, 262, 264, 275, 276, 278, 287, 289, 299, 304, 311, 314, 319, 321, 323, 324, 325, 327, 328, 329, 332, 333, 335, 338, 341, 342, 346, 347, 350, 354, 355, 356, 360, 361, 362, 368, 371, 373, 374, 377, 383, 384, 389, 391, 393, 395, 397, 402, 410, 419, 429, 432, 438, 440, 445, 453, 455, 456, 457, 458, 465, 471, 473, 474, 476, 478, 479, 481, 483, 484, 485, 486, 488, 492, 493, 495, 496, 499, 501, 502, 503, 506, 507, 508, 510, 511, 513, 514, 517, 519, 526, 528, 530, 533, 534, 538, 540, 545, 552, 560, 561, 564, 568, 570, 571, 572, 573, 579, 580, 581, 584, 589, 591, 592, 594, 596, 598, 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2975, 2976, 2977, 2979, 2980, 2983, 2988, 2990, 2991, 2992, 2998, 2999], 'precision': 0.7398041613378253, 'recall': 0.705, 'f1': 0.6906357423169466, 'ratio_valid_classifications': 1.0}\n",
- "(3) GPU = NVIDIA GeForce RTX 4090. Max memory = 23.988 GB.\n",
- "15.16 GB of memory reserved.\n",
- "Current Directory:\n",
- "/home/inflaton/code/logical-reasoning\n",
- "Evaluating internlm/internlm2_5-7b-chat-1m 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-1m None False datasets/mgtv data/internlm2_5-7b-chat-1m_results.csv 2048 1\n",
- "(0) GPU = NVIDIA GeForce RTX 4090. Max memory = 23.988 GB.\n",
- "0.0 GB of memory reserved.\n",
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- "[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",
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- " \"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",
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- " \"pad_token_id\": 2,\n",
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- " \"use_cache\": true,\n",
- " \"vocab_size\": 92544\n",
- "}\n",
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- " \"bos_token_id\": 1,\n",
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- "\n",
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- "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",
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- " \"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, ?it/s]Batch output: ['不是']\n",
- "100%|███████████████████████████████████████| 3000/3000 [07:38<00:00, 6.55it/s]\n",
- "/home/inflaton/miniconda3/envs/llm-finetuning/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
- " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "internlm/internlm2_5-7b-chat-1m/shots-10 metrics: {'accuracy': 0.48133333333333334, 'incorrect_ids': [2, 8, 9, 15, 16, 17, 19, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 43, 44, 45, 48, 51, 56, 57, 61, 65, 66, 67, 70, 74, 78, 81, 82, 89, 90, 92, 93, 94, 96, 97, 98, 102, 103, 104, 106, 107, 108, 110, 112, 113, 115, 117, 120, 121, 122, 123, 125, 126, 128, 129, 130, 133, 135, 137, 138, 139, 141, 143, 149, 150, 152, 153, 155, 158, 161, 164, 167, 170, 172, 173, 176, 179, 180, 181, 184, 185, 187, 190, 192, 195, 199, 200, 202, 203, 208, 209, 211, 212, 213, 218, 219, 225, 227, 228, 234, 236, 237, 241, 242, 243, 244, 245, 248, 250, 253, 256, 259, 260, 261, 262, 265, 267, 269, 270, 272, 274, 275, 276, 278, 279, 282, 285, 286, 287, 289, 291, 292, 293, 294, 295, 297, 298, 299, 304, 306, 311, 314, 317, 318, 320, 321, 322, 323, 325, 328, 329, 332, 333, 334, 335, 338, 341, 342, 345, 346, 347, 351, 352, 353, 355, 356, 357, 358, 359, 360, 361, 362, 364, 368, 369, 370, 371, 372, 373, 376, 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'ratio_valid_classifications': 0.9986666666666667}\n",
- "(3) GPU = NVIDIA GeForce RTX 4090. Max memory = 23.988 GB.\n",
- "15.16 GB of memory reserved.\n",
- "Current Directory:\n",
- "/home/inflaton/code/logical-reasoning\n",
- "Evaluating Qwen/Qwen2-7B-Instruct 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",
- "Qwen/Qwen2-7B-Instruct None False datasets/mgtv data/Qwen2-7B-Instruct_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: Qwen/Qwen2-7B-Instruct with adapter: None\n",
- "tokenizer_config.json: 100%|███████████████| 1.29k/1.29k [00:00<00:00, 4.74MB/s]\n",
- "vocab.json: 100%|██████████████████████████| 2.78M/2.78M [00:01<00:00, 1.76MB/s]\n",
- "merges.txt: 100%|██████████████████████████| 1.67M/1.67M [00:00<00:00, 16.0MB/s]\n",
- "tokenizer.json: 100%|██████████████████████| 7.03M/7.03M [00:00<00:00, 12.7MB/s]\n",
- "[INFO|tokenization_utils_base.py:2289] 2024-09-15 00:53:34,709 >> 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",
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- "[INFO|modeling_utils.py:1572] 2024-09-15 01:15:03,865 >> 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",
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- "\n",
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- "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",
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- " \"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, ?it/s]Batch output: ['不是']\n",
- "100%|███████████████████████████████████████| 3000/3000 [07:06<00:00, 7.03it/s]\n",
- "Qwen/Qwen2-7B-Instruct/shots-10 metrics: {'accuracy': 0.683, 'incorrect_ids': [2, 7, 9, 11, 12, 13, 15, 16, 17, 18, 19, 23, 24, 26, 29, 31, 33, 34, 35, 36, 37, 38, 43, 51, 59, 61, 65, 66, 67, 75, 77, 78, 80, 81, 82, 88, 89, 90, 91, 92, 93, 96, 97, 99, 102, 104, 106, 107, 108, 113, 115, 118, 120, 121, 123, 124, 131, 137, 139, 141, 143, 150, 151, 156, 157, 161, 164, 170, 172, 179, 180, 181, 187, 188, 189, 190, 191, 192, 193, 196, 200, 202, 203, 211, 213, 223, 224, 225, 227, 229, 236, 237, 239, 240, 241, 242, 243, 244, 245, 246, 248, 250, 253, 255, 256, 257, 260, 261, 263, 264, 265, 267, 268, 269, 270, 272, 274, 275, 276, 277, 281, 282, 283, 285, 287, 290, 291, 292, 293, 294, 295, 297, 301, 306, 308, 309, 314, 316, 317, 320, 321, 324, 325, 328, 330, 332, 333, 334, 338, 350, 351, 354, 355, 356, 359, 360, 370, 371, 372, 373, 374, 376, 377, 380, 383, 386, 389, 391, 392, 393, 395, 396, 397, 400, 402, 403, 405, 408, 409, 410, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 428, 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- "(3) GPU = NVIDIA GeForce RTX 4090. Max memory = 23.988 GB.\n",
- "15.938 GB of memory reserved.\n",
- "Current Directory:\n",
- "/home/inflaton/code/logical-reasoning\n",
- "Evaluating shenzhi-wang/Llama3.1-8B-Chinese-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",
- "shenzhi-wang/Llama3.1-8B-Chinese-Chat None False datasets/mgtv data/Llama3.1-8B-Chinese-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: shenzhi-wang/Llama3.1-8B-Chinese-Chat with adapter: None\n",
- "[INFO|tokenization_utils_base.py:2289] 2024-09-15 01:23:32,283 >> 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",
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- "Batch output: ['不是。']\n",
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- "shenzhi-wang/Llama3.1-8B-Chinese-Chat/shots-10 metrics: {'accuracy': 0.742, 'incorrect_ids': [10, 15, 16, 18, 43, 48, 53, 58, 59, 61, 63, 65, 66, 67, 78, 80, 81, 83, 88, 91, 93, 96, 97, 99, 102, 104, 106, 110, 112, 113, 115, 123, 126, 141, 143, 150, 153, 161, 164, 170, 171, 190, 191, 192, 193, 198, 199, 200, 202, 218, 221, 222, 224, 227, 233, 234, 236, 237, 238, 240, 241, 243, 245, 247, 248, 250, 251, 252, 255, 260, 261, 263, 264, 265, 268, 269, 271, 275, 276, 279, 290, 292, 293, 295, 301, 311, 314, 317, 318, 320, 324, 325, 328, 330, 332, 334, 335, 338, 343, 350, 356, 360, 361, 362, 368, 370, 375, 377, 383, 384, 389, 397, 398, 406, 409, 410, 414, 428, 430, 432, 438, 445, 446, 447, 450, 452, 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, 1203, 1211, 1212, 1220, 1221, 1224, 1226, 1228, 1231, 1232, 1235, 1238, 1239, 1240, 1241, 1251, 1252, 1254, 1257, 1259, 1266, 1270, 1274, 1278, 1282, 1289, 1292, 1295, 1296, 1304, 1305, 1308, 1313, 1315, 1316, 1317, 1319, 1322, 1323, 1324, 1329, 1339, 1340, 1342, 1344, 1345, 1348, 1349, 1353, 1360, 1362, 1363, 1364, 1365, 1367, 1368, 1372, 1385, 1386, 1387, 1389, 1391, 1394, 1400, 1402, 1405, 1406, 1409, 1411, 1416, 1420, 1422, 1423, 1424, 1426, 1427, 1439, 1446, 1449, 1451, 1452, 1453, 1459, 1464, 1469, 1475, 1476, 1478, 1485, 1486, 1490, 1491, 1492, 1494, 1496, 1499, 1500, 1506, 1510, 1515, 1516, 1517, 1518, 1519, 1522, 1525, 1526, 1533, 1536, 1545, 1547, 1551, 1554, 1556, 1558, 1559, 1560, 1562, 1568, 1572, 1580, 1581, 1587, 1594, 1603, 1604, 1607, 1612, 1613, 1614, 1624, 1627, 1633, 1639, 1643, 1648, 1650, 1652, 1654, 1655, 1659, 1660, 1661, 1662, 1665, 1672, 1679, 1684, 1685, 1686, 1691, 1695, 1712, 1716, 1718, 1726, 1727, 1734, 1736, 1738, 1743, 1745, 1751, 1753, 1757, 1762, 1765, 1767, 1769, 1780, 1783, 1786, 1791, 1798, 1801, 1804, 1810, 1812, 1824, 1827, 1839, 1869, 1879, 1884, 1886, 1887, 1890, 1891, 1897, 1899, 1901, 1902, 1903, 1907, 1908, 1914, 1919, 1936, 1938, 1941, 1945, 1946, 1953, 1958, 1959, 1962, 1963, 1964, 1967, 1973, 1976, 1977, 1978, 1979, 1981, 1983, 1985, 1986, 1989, 1990, 1991, 1992, 1995, 2003, 2017, 2025, 2026, 2028, 2029, 2031, 2036, 2037, 2038, 2044, 2046, 2049, 2054, 2064, 2067, 2068, 2070, 2077, 2081, 2084, 2088, 2100, 2105, 2106, 2109, 2110, 2119, 2120, 2121, 2126, 2133, 2147, 2152, 2159, 2160, 2161, 2162, 2164, 2173, 2174, 2177, 2183, 2185, 2186, 2188, 2192, 2193, 2194, 2196, 2197, 2199, 2205, 2208, 2212, 2213, 2215, 2223, 2225, 2226, 2236, 2237, 2238, 2240, 2243, 2245, 2246, 2257, 2262, 2274, 2278, 2281, 2287, 2297, 2304, 2311, 2312, 2320, 2324, 2328, 2330, 2333, 2337, 2343, 2348, 2351, 2354, 2355, 2360, 2366, 2367, 2370, 2372, 2378, 2381, 2395, 2396, 2400, 2404, 2409, 2410, 2412, 2419, 2420, 2422, 2423, 2427, 2429, 2437, 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"
- ]
- }
- ],
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