{"cells":[{"cell_type":"code","execution_count":3,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"0ea8b46b-839b-445b-8043-ccdf4e920ace","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":351,"status":"ok","timestamp":1721821301310,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"KSWhkNUGHk6J","outputId":"4f28bc46-4892-4fde-a7df-5fb0d08368f6"},"outputs":[{"name":"stdout","output_type":"stream","text":["The autoreload extension is already loaded. To reload it, use:\n"," %reload_ext autoreload\n"]}],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21827,"status":"ok","timestamp":1721821323679,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"O1N25gePH3nR","outputId":"e9b41419-6be0-4782-a598-e14ac7883756"},"outputs":[{"name":"stdout","output_type":"stream","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":4,"metadata":{"executionInfo":{"elapsed":4,"status":"ok","timestamp":1721821323679,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"ExKl7VacH6Va"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":5,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"6d394937-6c99-4a7c-9d32-7600a280032f","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1721821323679,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"aVAhR0LPHk6J","outputId":"2656c072-f119-4ba1-9414-59231b4e25d1"},"outputs":[{"name":"stdout","output_type":"stream","text":["workding dir: /content/drive/MyDrive/logical-reasoning/\n"]}],"source":["import os\n","import sys\n","from pathlib import Path\n","\n","workding_dir = \"/content/drive/MyDrive/logical-reasoning/\"\n","os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"workding dir:\", workding_dir)"]},{"cell_type":"code","execution_count":6,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"ac667aba-076e-4de6-9984-8f6a67cb09cd","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1721821323679,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"pQYZ1k-4Hk6J","outputId":"b07c1ecf-5669-49f3-a151-da4f55e92007"},"outputs":[{"data":{"text/plain":["True"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["need_to_setup_env = True\n","need_to_setup_env"]},{"cell_type":"code","execution_count":7,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"72f9cf79-7b0d-4d9e-90a0-1fa5251b947f","showTitle":false,"title":""},"executionInfo":{"elapsed":134242,"status":"ok","timestamp":1721821457919,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"Ant0nzR8Hk6J"},"outputs":[],"source":["%%capture\n","if need_to_setup_env:\n"," !pip install -r requirements.txt\n","\n","# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n","!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n","!pip install --no-deps \"xformers<0.0.27\" \"trl<0.9.0\" peft accelerate bitsandbytes\n"]},{"cell_type":"code","execution_count":8,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"9f67ec60-2f24-411c-84eb-0dd664b44775","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":583,"status":"ok","timestamp":1721821458498,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"p2eN5kdRHk6J","outputId":"a6411072-4da9-4421-c270-f70308c9b22f"},"outputs":[{"name":"stdout","output_type":"stream","text":["loading env vars from: /content/drive/MyDrive/logical-reasoning/.env\n"]},{"data":{"text/plain":["True"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["from dotenv import find_dotenv, load_dotenv\n","\n","found_dotenv = find_dotenv(\".env\")\n","\n","if len(found_dotenv) == 0:\n"," found_dotenv = find_dotenv(\".env.example\")\n","print(f\"loading env vars from: {found_dotenv}\")\n","load_dotenv(found_dotenv, override=True)"]},{"cell_type":"code","execution_count":9,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"f1597656-8042-4878-9d3b-9ebfb8dd86dc","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1721821458498,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"i_5wPar_Hk6J","outputId":"24b6ed31-a13f-4d6e-bdbf-180c8cf36224"},"outputs":[{"data":{"text/plain":["('unsloth/gemma-2-9b',\n"," False,\n"," None,\n"," None,\n"," 2048,\n"," 1,\n"," None,\n"," 'datasets/mgtv',\n"," 'results/mgtv-results_colab_p2_gemma2.csv',\n"," None,\n"," None)"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["import os\n","\n","model_name = os.getenv(\"MODEL_NAME\")\n","token = os.getenv(\"HF_TOKEN\") or None\n","load_in_4bit = os.getenv(\"LOAD_IN_4BIT\") == \"true\"\n","local_model = os.getenv(\"LOCAL_MODEL\")\n","hub_model = os.getenv(\"HUB_MODEL\")\n","num_train_epochs = int(os.getenv(\"NUM_TRAIN_EPOCHS\") or 0)\n","data_path = os.getenv(\"LOGICAL_REASONING_DATA_PATH\")\n","results_path = os.getenv(\"LOGICAL_REASONING_RESULTS_PATH\")\n","\n","max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n","dtype = (\n"," None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n",")\n","\n","model_name, load_in_4bit, local_model, hub_model, max_seq_length, num_train_epochs, dtype, data_path, results_path, os.getenv(\"DISABLE_MLFLOW_INTEGRATION\"), os.getenv(\"WANDB_DISABLED\")"]},{"cell_type":"code","execution_count":10,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"e3ab54ba-7b6d-4817-bf2e-c5d711508b58","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1721821458498,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"O9IDI1OsHk6J","outputId":"36604d4d-4878-4742-9dd2-f7a7ddd61d40"},"outputs":[{"name":"stdout","output_type":"stream","text":["Wed Jul 24 11:44:18 2024 \n","+---------------------------------------------------------------------------------------+\n","| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 |\n","|-----------------------------------------+----------------------+----------------------+\n","| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n","| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n","| | | MIG M. |\n","|=========================================+======================+======================|\n","| 0 NVIDIA L4 Off | 00000000:00:03.0 Off | 0 |\n","| N/A 42C P8 12W / 72W | 1MiB / 23034MiB | 0% Default |\n","| | | N/A |\n","+-----------------------------------------+----------------------+----------------------+\n"," \n","+---------------------------------------------------------------------------------------+\n","| Processes: |\n","| GPU GI CI PID Type Process name GPU Memory |\n","| ID ID Usage |\n","|=======================================================================================|\n","| No running processes found |\n","+---------------------------------------------------------------------------------------+\n"]}],"source":["!nvidia-smi"]},{"cell_type":"code","execution_count":11,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"b2a43943-9324-4839-9a47-cfa72de2244b","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":728,"status":"ok","timestamp":1721821459223,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"hSMhuoKFHk6J","outputId":"08051054-86f7-4079-f1da-2bb945f55158"},"outputs":[{"name":"stdout","output_type":"stream","text":["Python 3.10.12\n","\u001b[33mWARNING: Package(s) not found: flash-attn\u001b[0m\u001b[33m\n","\u001b[0mCPU times: user 10.7 ms, sys: 1.15 ms, total: 11.9 ms\n","Wall time: 608 ms\n"]}],"source":["%%time\n","!python --version\n","!pip show flash-attn"]},{"cell_type":"code","execution_count":12,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"4f51fafe-5527-49a2-8ccf-a0192e69493a","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/","height":427,"referenced_widgets":["b8a21f91962a4fce9cf6e61462a9c472","28ad855e464f41929364429acc4f4aa3","8b77f23d015245be8c6c309f3b5150e4","93c12be1cdc34600a0b0fa15a42a91d5","12440f7226824f10bdec45b9e3bfc500","5da671479a3a4281882bd78478a338dc","9340540d068147a88ec459edbff94157","4a516c2eb7cc4ec2a0079473c597f0c6","03813a0c022c4e4fb3ea41a2c29f1e27","2861fb9cd8814cdbafdb91eedc044c0c","c1fbc7a38a474497908daf746a67826d","91c2b1cbbaca451e9ac5d0ed9d790f81","238ef274e71a421e8b982433e6ca5aa0","9ebd426e9ab5495087e05daf841f3f34","298c4c82818a4a3aba24d0aef88f8422","6a9d60f0768b4279b59dbb37f065c16c","8552394ec1ad493ea5969c06143556d3","4c88dcf0f9ab423e98af16ba4cbb6c14","88992b914d3443f58e33e0a54be85131","ebd8515b9aaf4d6f8b5a7f382e2d3614","09fd44611f264ad28113656be0139024","fb7a9432696b4dae869e550ba9e14799","ac533cf75f994214bc100d467ba6f009","da3614cf059c4db2982a9a2fb3188163","009428919b5d49fbb3bc38d03f06c8df","3f9467eeb4454dc599136dcf67dc2db4","b7c643c9d0814450878178372e5c319c","1ba271db437d4cb0b1848d1f35445ca6","77bf6a9eb5e849f4be3d204c7d264f09","60af7c02dec14938b2867c13c71e9eca","68325227aee74d8d9ac488e931c93832","a7ee5f3abb8d4a498dfae7330f97c763","0ec399f062d647f7a156a2ad4d636e56","3d2178c37d2b4287a637d03e2ce2ef11","eff60739f3bd41ce992305aefe52d004","1539232b47c44fcf9f4567c008445c3b","98dfc78164394073b94fbe1ba7ff0727","640dc88337d147cc914ebc7af9c2e8d6","77a57ee352b9471891ab252bbe97a50e","eb5a62d798a2453bad15340690546560","40fff6a0c6f44a2e85480125b47c78ad","5b68984d42e742d59e913fa10e492f77","f97571c604004925aeb7c2c8a80c213b","2a5ef63f89ac4aec995a6017fc4eb533","4485bb791ce246ed9fc85dfaff48d3aa","978736e336d34085b0440beed577d4c9","429ba5b9b3d94e6d8c00c894d1238856","1f750b3760f64152b8e100d1f30f7317","2e03bb8e96004abf98af8b28461ccdd7","bd567c41a32d48e6b7b29429e0c97b4d","94cbfc143c374345bbaa462779bd0a92","7a04b69e28474647a12953dedab01952","d05f949fb3114494874e44519d174c80","eddf3130901f4c04a12cb6b9fe9f9d82","2a274e60da014608863b84d0e7b5d506","1e4262402bf64c5689a5c846565536cc","a4abb0d556e24a919a66aab015311268","c03e4a350d1d45d9a59e48a2faa6d859","5f3a8144e623461c8e638a79a362674b","9cc9bb81455e47d8a2743770198a7064","1b271a6e2d1741feaa97ff73859a52ec","ecab98befb1948f783762536c058354a","e8cf61fed2714d22ba5680616314a2e5","7002beb29d9047a2b568735507ebff09","ce380afb636b4e429a96761958dde045","5d55a92e70a44303aa5f2051d4df50f0","36fbcb5e62c7444e8f778b192fd7a15d","383527933d5142d48c2ec8c52806af89","05703f06302c4d5ca40547d00ac61641","268e15e5318d4ea09224390b3c5dfc1a","03242a3358934a16a0f5a11b0dfe6bc9","66c4e1341e774f5e96aa58c0c985c734","6493f45fc0484d2da2620c718d88c8a8","0d95a96618894ea194293a645397340c","a35cd2fddb1c4013b522866aacabf68e","8ea6662253ef41ea891918e4a37a3515","86630f1a9bce4fd7afbd1c6258345049","5577aed3e8f944c08efa0c63e25acc4e","ea9e29bc508b4c1f8e18fda0839592b8","da1ff9f5b9ea4660b63d7bdee8f81b54","62f580b06b5648d4ab734bf9361baeb5","3c453a0307934affbcbb6b286a0b6ada","551860cb4661452386a43b253aa3ebed","ef4cffb71ba04bc5a91bef575ce87672","fc78a43416354e218d4d836fc76bdcf2","e9bf0128f9ec432e9c0ef2306285fd8c","f2b1779efd6b4fd0a83e3fa194280f09","fe963fad395242488d401de4e18e52cc","0bbc5f8edbbf43cda88751bc1b12e521","9166c9bce10e421cbd1879795035b781","1e7dda7304af4e29a97dc8a199a53a36","1ed20d98f0bd4c40b4b1df701155d30c","c2bbb4cd65334cfe8067700f3f43054b","095c47aa298641f3aa4f4e3f5904508c","2303ed0dc12645d6be862cb545d85d12","7ae02a538ba742cea91adc60f2e1410b","5d543cd96df745319da8d60657f0b4a3","c2950165413841d7b8136b4aebae483e","23cba9fff47e41cf9f56054bcd006588"]},"executionInfo":{"elapsed":101585,"status":"ok","timestamp":1721821560806,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"xXCH_B2jHk6K","outputId":"39922295-db0c-4eb6-a1ab-ea3e7192a714"},"outputs":[{"name":"stdout","output_type":"stream","text":["🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"b8a21f91962a4fce9cf6e61462a9c472","version_major":2,"version_minor":0},"text/plain":["config.json: 0%| | 0.00/897 [00:00 0 ! Suggested 8, 16, 32, 64, 128\n"," target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n"," \"gate_proj\", \"up_proj\", \"down_proj\",],\n"," lora_alpha = 16,\n"," lora_dropout = 0, # Supports any, but = 0 is optimized\n"," bias = \"none\", # Supports any, but = \"none\" is optimized\n"," # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n"," use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n"," random_state = 3407,\n"," use_rslora = False, # We support rank stabilized LoRA\n"," loftq_config = None, # And LoftQ\n",")"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1721821565487,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"aJefUv0yU7q8","outputId":"99a9b85e-8111-46f9-acb2-dcf341775aaf"},"outputs":[{"name":"stdout","output_type":"stream","text":["The autoreload extension is already loaded. To reload it, use:\n"," %reload_ext autoreload\n"]}],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["26a043cae5364ac787b86ba11b2338da","a541c903a20745d8ad88a88c5a8f4ad2","5a22fd453d7244c285afdc78b3bcb496","f6696d5931f943d3934dcfd4185aeacf","b0d6fc327a214358822c3618975163aa","e6106425d0ce4b378fe0d133f787c4bd","c417600898494641b790b14acd3ad371","bf06471cb91b49e0a48412d2bea8c139","9add1467f497487688dddb51ccec4a7e","f9cb429e66384d4aa54374000ac25690","5595f4a03d594c499521e15649d21705","03ae21f3138141e1b6d4cb6ba5237a08","e7c5caca98c54931b90e3ca1dbf1a3e0","3bd2850901c44d0ab190852c81534779","3e748d768703400dad682776f572f701","1d6c6769feb24c168944c6b447ad11c8","5a278fb4e29e47029c7fed42535ba278","4cd44e5996384c358ec764d03cfa6cfd","037a2c1265034782afc5ff58bd8cab8a","5cbf0ccf24d64b1e9ae7bf31da12921c","3f483146675146e48a154ac6eef6accc","2acdae3d2cbe44faab4f7d6735abe54e","62260f81a55449f4b7b23dfab81f2e24","f6146fad0fc143e7bd3b5324e22b8a7e","f91de47f1a394590b0d5e4b4c53d6f97","4b492ed57c3348f094a8770e5af779c5","c277956030a345a29058a243d4b924e3","d747059a38614803a28c29e1994beffd","aed4e88327bf42fa9441fbdb4f624fba","2b3d1bce6b62462b96097cf65d4f6b47","dc12267979dd467b9236d00af7a109ae","8e4e6cdf20b046ef879c16f064b5fce1","65a152884c754e21a65627db19faa844","561f8d9ec3c843f1863b9be1f1353a57","71b907b019854694944c5bdf4c91f494","42ef9f6a52204542b0ccea3a92d6aec7","58a61c72fa5a4c49bb7a73bd364df77d","c6cfddcf13db416fa9e3c39f3079ab70","962be6b59435443b97bfdc82a59c6638","a242d2f15cd148af9823b21b19999582","c752d979eccd4626bbcdc968bf2c9283","41fdc541c4e34bd189d23417fb2896c0","46faa500cd6d43fd94bff6155f4b18c6","6e922b985fa746348eefe0e015da0f31"]},"executionInfo":{"elapsed":7731,"status":"ok","timestamp":1721821573215,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"6h7-RrIHTeNK","outputId":"75e46d73-044b-4713-9a65-06dccaf1e99e"},"outputs":[{"name":"stdout","output_type":"stream","text":["loading /content/drive/MyDrive/logical-reasoning/llm_toolkit/logical_reasoning_utils.py\n","loading train/test data files\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"26a043cae5364ac787b86ba11b2338da","version_major":2,"version_minor":0},"text/plain":["Generating train split: 0 examples [00:00, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"03ae21f3138141e1b6d4cb6ba5237a08","version_major":2,"version_minor":0},"text/plain":["Generating test split: 0 examples [00:00, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"62260f81a55449f4b7b23dfab81f2e24","version_major":2,"version_minor":0},"text/plain":["Map: 0%| | 0/25000 [00:00system\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:** 在甄家村里，有一个古老的传说：每年南瓜丰收的季节，南瓜田里总有一个最大的南瓜会不翼而飞，村民们对此现象困惑不解。请找出南瓜失踪背后的原因。\n","\n","**谜底:** 真相原来与一位年迈的农夫有关。这位农夫年轻时，曾与一位美丽的姑娘相恋。他们约定在南瓜丰收的季节结婚。然而，命运弄人，姑娘在婚礼前的一场意外中离世。悲伤的农夫为了纪念心爱的姑娘，每年都会将最大的南瓜偷走，放到姑娘的墓前，以此寄托自己的哀思。这一行为延续了多年，成为了乡村里一个神秘的传说。\n","\n","**参与者提出的问题:** 偷的人信神吗\n","<|im_end|>\n","<|im_start|>assistant\n","不是\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:** 在甄家村里，有一个古老的传说：每年南瓜丰收的季节，南瓜田里总有一个最大的南瓜会不翼而飞，村民们对此现象困惑不解。请找出南瓜失踪背后的原因。\n","\n","**谜底:** 真相原来与一位年迈的农夫有关。这位农夫年轻时，曾与一位美丽的姑娘相恋。他们约定在南瓜丰收的季节结婚。然而，命运弄人，姑娘在婚礼前的一场意外中离世。悲伤的农夫为了纪念心爱的姑娘，每年都会将最大的南瓜偷走，放到姑娘的墓前，以此寄托自己的哀思。这一行为延续了多年，成为了乡村里一个神秘的传说。\n","\n","**参与者提出的问题:** 偷的人信神吗\n","<|im_end|>\n","<|im_start|>assistant\n","\n"]}],"source":["from llm_toolkit.logical_reasoning_utils import *\n","from llm_toolkit.llm_utils import print_row_details\n","\n","dataset = load_logical_reasoning_dataset(data_path, tokenizer=tokenizer, using_p1=False)\n","print_row_details(dataset[\"train\"].to_pandas())"]},{"cell_type":"code","execution_count":16,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":66,"referenced_widgets":["8057d1767a9c44a3ada8ca2125bdc499","a2852c8fa854440e895529838df8f36b","05b5cb56b506453e8e08a0d14d51706f","c300776f0f6b487aaaac5cc0bfb6dcad","027bd98ae02e4bea92cefd0f76f34b6f","dfd683002a184e879310eb6ef2375736","ac2fdee18bde47cda20c90dbf6829b20","4fcc2285335849f5be81ff2cb90a26df","25ecc11d06cc4d3cb94d653d4e988921","19a060c942534d409694f7c0abbd155c","c95c628cb816423ab469cb979c9aa67b"]},"executionInfo":{"elapsed":11118,"status":"ok","timestamp":1721821584322,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"OS3wqbFNVPUd","outputId":"5b2c62ba-0990-45c2-9c05-5b2169dad824"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"8057d1767a9c44a3ada8ca2125bdc499","version_major":2,"version_minor":0},"text/plain":["Map (num_proc=2): 0%| | 0/25000 [00:00"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Tracking run with wandb version 0.17.4"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Run data is saved locally in /content/drive/MyDrive/logical-reasoning/wandb/run-20240724_114629-g7k3onq3"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Syncing run outputs to Weights & Biases (docs)
"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":[" View project at https://wandb.ai/inflaton-ai/huggingface"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":[" View run at https://wandb.ai/inflaton-ai/huggingface/runs/g7k3onq3"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["\n","
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StepTraining Loss
1000.206200
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3000.043100
4000.041000
5000.039400

"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"ename":"KeyboardInterrupt","evalue":"","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/unsloth/tokenizer_utils.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/unsloth/models/llama.py\u001b[0m in \u001b[0;36m_fast_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 3347\u001b[0m \u001b[0mscaled_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3348\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3349\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3350\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3351\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m 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\u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2153\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mset_trigger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 523\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 524\u001b[0m )\n\u001b[0;32m--> 525\u001b[0;31m torch.autograd.backward(\n\u001b[0m\u001b[1;32m 526\u001b[0m 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