4bit RPP results

competition/12rp1.15_InternLM_Lora_NV4080_4bit.ipynb

@@ -1 +1 @@
-{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":476,"status":"ok","timestamp":1720679526275,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"uWKRSV6eZsCn"},"outputs":[],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":2,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"eb33b19f-1206-41ee-84e2-e6258a12eef7","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2534,"status":"ok","timestamp":1720679529344,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"xwFh14uiZBrI","outputId":"d767799c-34c2-46a5-f052-378146a55321"},"outputs":[],"source":["from pathlib import Path\n","\n","try:\n","    from google.colab import drive\n","\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":3,"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":5,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"G5pNu3zgZBrL","outputId":"160a554f-fb08-4aa0-bc00-0422fb7c1fac"},"outputs":[{"name":"stdout","output_type":"stream","text":["workding dir: /home/inflaton/code/projects/courses/logical-reasoning\n"]}],"source":["import os\n","import sys\n","from pathlib import Path\n","\n","os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"workding dir:\", workding_dir)"]},{"cell_type":"code","execution_count":4,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"9f67ec60-2f24-411c-84eb-0dd664b44775","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"hPCC-6m7ZBrM","outputId":"c7aa2c96-5e99-440a-c148-201d79465ff9"},"outputs":[{"name":"stdout","output_type":"stream","text":["loading env vars from: /home/inflaton/code/projects/courses/logical-reasoning/.env\n"]},{"data":{"text/plain":["True"]},"execution_count":4,"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":5,"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":3,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"1M3IraVtZBrM","outputId":"29ab35f6-2970-4ade-d85d-3174acf8cda0"},"outputs":[{"name":"stdout","output_type":"stream","text":["internlm/internlm2_5-7b-chat-1m llama-factory/saves/internlm2_5_7b/lora/sft_bf16_p2_full/checkpoint-88 True datasets/mgtv results/mgtv-results_internlm_best.csv\n"]}],"source":["import os\n","\n","model_name = os.getenv(\"MODEL_NAME\")\n","adapter_name_or_path = os.getenv(\"ADAPTER_NAME_OR_PATH\")\n","load_in_4bit = os.getenv(\"LOAD_IN_4BIT\") == \"true\"\n","data_path = os.getenv(\"LOGICAL_REASONING_DATA_PATH\")\n","results_path = os.getenv(\"LOGICAL_REASONING_RESULTS_PATH\")\n","use_english_datasets = os.getenv(\"USE_ENGLISH_DATASETS\") == \"true\"\n","\n","print(model_name, adapter_name_or_path, load_in_4bit, data_path, results_path)"]},{"cell_type":"code","execution_count":6,"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":564,"status":"ok","timestamp":1720679529907,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"UgMvt6dIZBrM","outputId":"ce37581c-fd26-46c2-ad87-d933d99f68f7"},"outputs":[{"name":"stdout","output_type":"stream","text":["Python 3.11.9\n","\u001b[33mWARNING: Package(s) not found: flash-attn\u001b[0m\u001b[33m\n","\u001b[0mCPU times: user 284 ms, sys: 88 ms, total: 372 ms\n","Wall time: 4.21 s\n"]}],"source":["%%time\n","!python --version\n","!pip show flash-attn"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1685,"status":"ok","timestamp":1720679531591,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"ZuS_FsLyZBrN","outputId":"2cba0105-c505-4395-afbd-2f2fee6581d0"},"outputs":[{"name":"stdout","output_type":"stream","text":["loading /home/inflaton/code/projects/courses/logical-reasoning/llm_toolkit/logical_reasoning_utils.py\n","GPU is available\n"]}],"source":["from llm_toolkit.llm_utils import *\n","from llm_toolkit.logical_reasoning_utils import *\n","\n","device = check_gpu()"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["loading model: llama-factory/saves/internlm2_5_7b/lora/sft_bf16_p2_full/checkpoint-88 with adapter: None\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"f59b2bb4da404f3f8aba87b5a732222b","version_major":2,"version_minor":0},"text/plain":["Loading checkpoint shards:   0%|          | 0/8 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"}],"source":["%%time\n","\n","# model, tokenizer = load_model(model_name, adapter_name_or_path=adapter_name_or_path, using_llama_factory=False)\n","model, tokenizer = load_model(adapter_name_or_path, using_llama_factory=False)"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["loading train/test data files\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"fc9a5f2556b24b42b3fb425da63fb8fc","version_major":2,"version_minor":0},"text/plain":["Map:   0%|          | 0/25000 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"268ca08da54c4f30be9e04477a42e40d","version_major":2,"version_minor":0},"text/plain":["Map:   0%|          | 0/3000 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["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"]}],"source":["datasets = load_logical_reasoning_dataset(\n","    data_path,\n","    tokenizer=tokenizer,\n","    chinese_prompt=not use_english_datasets,\n","    using_p1=False,\n",")"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[],"source":["def evaluate_model(model, tokenizer, model_name, dataset):\n","    print(f\"Evaluating model: {model_name} on {device}\")\n","    predictions = eval_model(model, tokenizer, dataset, device=device)\n","\n","    save_results(\n","        model_name,\n","        results_path,\n","        dataset,\n","        predictions,\n","        debug=False,\n","    )\n","\n","    metrics = calc_metrics(dataset[\"label\"], predictions, debug=False)\n","    print(metrics)"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Evaluating model: THUDM/glm-4-9b-chat-1m_NV4080 on cuda\n"]},{"name":"stderr","output_type":"stream","text":["  0%|          | 1/3000 [1:24:27<4221:21:42, 5067.32s/it]"]},{"name":"stdout","output_type":"stream","text":["--------\n","step 1: \n","不是\n","--------\n","step 2: \n","不是\n","--------\n","step 3: \n","不是\n","--------\n","step 4: 不是\n","--------\n","step 5: 不是\n"]},{"name":"stderr","output_type":"stream","text":[" 13%|█▎        | 392/3000 [5:21:45<35:40:38, 49.25s/it]  \n"]},{"ename":"KeyboardInterrupt","evalue":"","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","File \u001b[0;32m<timed eval>:1\u001b[0m\n","Cell \u001b[0;32mIn[12], line 3\u001b[0m, in \u001b[0;36mevaluate_model\u001b[0;34m(model, tokenizer, model_name, dataset)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mevaluate_model\u001b[39m(model, tokenizer, model_name, dataset):\n\u001b[1;32m      2\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEvaluating model: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m on \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdevice\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m     predictions \u001b[38;5;241m=\u001b[39m \u001b[43meval_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtokenizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      5\u001b[0m     save_results(\n\u001b[1;32m      6\u001b[0m         model_name,\n\u001b[1;32m      7\u001b[0m         results_path,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     10\u001b[0m         debug\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m     11\u001b[0m     )\n\u001b[1;32m     13\u001b[0m     metrics \u001b[38;5;241m=\u001b[39m calc_metrics(dataset[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m\"\u001b[39m], predictions, debug\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n","File \u001b[0;32m~/code/projects/courses/logical-reasoning/llm_toolkit/llm_utils.py:164\u001b[0m, in \u001b[0;36meval_model\u001b[0;34m(model, tokenizer, eval_dataset, device, max_new_tokens, repetition_penalty, batch_size)\u001b[0m\n\u001b[1;32m    157\u001b[0m batch_prompts \u001b[38;5;241m=\u001b[39m eval_dataset[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprompt\u001b[39m\u001b[38;5;124m\"\u001b[39m][i:batch_end]\n\u001b[1;32m    158\u001b[0m inputs \u001b[38;5;241m=\u001b[39m tokenizer(\n\u001b[1;32m    159\u001b[0m     batch_prompts,\n\u001b[1;32m    160\u001b[0m     return_tensors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    161\u001b[0m     padding\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,  \u001b[38;5;66;03m# Ensure all inputs in the batch have the same length\u001b[39;00m\n\u001b[1;32m    162\u001b[0m )\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[0;32m--> 164\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    165\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    166\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_new_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_new_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    167\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrepetition_penalty\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepetition_penalty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    168\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    169\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    170\u001b[0m outputs \u001b[38;5;241m=\u001b[39m outputs[:, inputs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minput_ids\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m] :]\n\u001b[1;32m    171\u001b[0m decoded_output \u001b[38;5;241m=\u001b[39m tokenizer\u001b[38;5;241m.\u001b[39mbatch_decode(\n\u001b[1;32m    172\u001b[0m     outputs, skip_special_tokens\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    173\u001b[0m )  \u001b[38;5;66;03m# Skip special tokens for clean output\u001b[39;00m\n","File \u001b[0;32m~/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/utils/_contextlib.py:115\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m    113\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    114\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 115\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m~/miniconda3/envs/llama-factory/lib/python3.11/site-packages/transformers/generation/utils.py:1969\u001b[0m, in \u001b[0;36mGenerationMixin.generate\u001b[0;34m(self, inputs, generation_config, logits_processor, stopping_criteria, prefix_allowed_tokens_fn, synced_gpus, assistant_model, streamer, negative_prompt_ids, negative_prompt_attention_mask, **kwargs)\u001b[0m\n\u001b[1;32m   1961\u001b[0m     input_ids, model_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_expand_inputs_for_generation(\n\u001b[1;32m   1962\u001b[0m         input_ids\u001b[38;5;241m=\u001b[39minput_ids,\n\u001b[1;32m   1963\u001b[0m         expand_size\u001b[38;5;241m=\u001b[39mgeneration_config\u001b[38;5;241m.\u001b[39mnum_return_sequences,\n\u001b[1;32m   1964\u001b[0m         is_encoder_decoder\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mis_encoder_decoder,\n\u001b[1;32m   1965\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmodel_kwargs,\n\u001b[1;32m   1966\u001b[0m     )\n\u001b[1;32m   1968\u001b[0m     \u001b[38;5;66;03m# 13. run sample (it degenerates to greedy search when `generation_config.do_sample=False`)\u001b[39;00m\n\u001b[0;32m-> 1969\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sample\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1970\u001b[0m \u001b[43m        \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1971\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlogits_processor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprepared_logits_processor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1972\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlogits_warper\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprepared_logits_warper\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1973\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstopping_criteria\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprepared_stopping_criteria\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1974\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgeneration_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgeneration_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1975\u001b[0m \u001b[43m        \u001b[49m\u001b[43msynced_gpus\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msynced_gpus\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1976\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstreamer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstreamer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1977\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1978\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1980\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m generation_mode \u001b[38;5;129;01min\u001b[39;00m (GenerationMode\u001b[38;5;241m.\u001b[39mBEAM_SAMPLE, GenerationMode\u001b[38;5;241m.\u001b[39mBEAM_SEARCH):\n\u001b[1;32m   1981\u001b[0m     \u001b[38;5;66;03m# 11. prepare logits warper\u001b[39;00m\n\u001b[1;32m   1982\u001b[0m     prepared_logits_warper \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m   1983\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_logits_warper(generation_config, device\u001b[38;5;241m=\u001b[39minput_ids\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[1;32m   1984\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m generation_config\u001b[38;5;241m.\u001b[39mdo_sample\n\u001b[1;32m   1985\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1986\u001b[0m     )\n","File \u001b[0;32m~/miniconda3/envs/llama-factory/lib/python3.11/site-packages/transformers/generation/utils.py:2912\u001b[0m, in \u001b[0;36mGenerationMixin._sample\u001b[0;34m(self, input_ids, logits_processor, stopping_criteria, generation_config, synced_gpus, streamer, logits_warper, **model_kwargs)\u001b[0m\n\u001b[1;32m   2909\u001b[0m model_inputs\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutput_hidden_states\u001b[39m\u001b[38;5;124m\"\u001b[39m: output_hidden_states} \u001b[38;5;28;01mif\u001b[39;00m output_hidden_states \u001b[38;5;28;01melse\u001b[39;00m {})\n\u001b[1;32m   2911\u001b[0m \u001b[38;5;66;03m# forward pass to get next token\u001b[39;00m\n\u001b[0;32m-> 2912\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m   2914\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m synced_gpus \u001b[38;5;129;01mand\u001b[39;00m this_peer_finished:\n\u001b[1;32m   2915\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m  \u001b[38;5;66;03m# don't waste resources running the code we don't need\u001b[39;00m\n","File \u001b[0;32m~/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m~/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n","File \u001b[0;32m~/miniconda3/envs/llama-factory/lib/python3.11/site-packages/accelerate/hooks.py:166\u001b[0m, in \u001b[0;36madd_hook_to_module.<locals>.new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    164\u001b[0m         output \u001b[38;5;241m=\u001b[39m module\u001b[38;5;241m.\u001b[39m_old_forward(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    165\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 166\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_old_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    167\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\u001b[38;5;241m.\u001b[39m_hf_hook\u001b[38;5;241m.\u001b[39mpost_forward(module, output)\n","File \u001b[0;32m~/.cache/huggingface/modules/transformers_modules/THUDM/glm-4-9b-chat-1m/7d23e9e0bb8e63d85c4489b37b099489c1515370/modeling_chatglm.py:1003\u001b[0m, in \u001b[0;36mChatGLMForConditionalGeneration.forward\u001b[0;34m(self, input_ids, position_ids, attention_mask, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict, return_last_logit)\u001b[0m\n\u001b[1;32m   1000\u001b[0m use_cache \u001b[38;5;241m=\u001b[39m use_cache \u001b[38;5;28;01mif\u001b[39;00m use_cache \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39muse_cache\n\u001b[1;32m   1001\u001b[0m return_dict \u001b[38;5;241m=\u001b[39m return_dict \u001b[38;5;28;01mif\u001b[39;00m return_dict \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39muse_return_dict\n\u001b[0;32m-> 1003\u001b[0m transformer_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransformer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1004\u001b[0m \u001b[43m    \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1005\u001b[0m \u001b[43m    \u001b[49m\u001b[43mposition_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposition_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1006\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1007\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpast_key_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpast_key_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1008\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1009\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1010\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1011\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1012\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1014\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m transformer_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m   1015\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_last_logit:\n","File \u001b[0;32m~/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m~/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n","File \u001b[0;32m~/.cache/huggingface/modules/transformers_modules/THUDM/glm-4-9b-chat-1m/7d23e9e0bb8e63d85c4489b37b099489c1515370/modeling_chatglm.py:898\u001b[0m, in \u001b[0;36mChatGLMModel.forward\u001b[0;34m(self, input_ids, position_ids, attention_mask, full_attention_mask, past_key_values, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    895\u001b[0m     rotary_pos_emb \u001b[38;5;241m=\u001b[39m rotary_pos_emb[\u001b[38;5;28;01mNone\u001b[39;00m, :seq_length]\n\u001b[1;32m    897\u001b[0m \u001b[38;5;66;03m# Run encoder.\u001b[39;00m\n\u001b[0;32m--> 898\u001b[0m hidden_states, presents, all_hidden_states, all_self_attentions \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    899\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfull_attention_mask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrotary_pos_emb\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrotary_pos_emb\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    900\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkv_caches\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpast_key_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cache\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\n\u001b[1;32m    901\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    902\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m presents \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(presents) \u001b[38;5;129;01mis\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mTensor:\n\u001b[1;32m    903\u001b[0m     presents \u001b[38;5;241m=\u001b[39m presents\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;241m1\u001b[39m, dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n","File \u001b[0;32m~/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m~/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n","File \u001b[0;32m~/.cache/huggingface/modules/transformers_modules/THUDM/glm-4-9b-chat-1m/7d23e9e0bb8e63d85c4489b37b099489c1515370/modeling_chatglm.py:727\u001b[0m, in \u001b[0;36mGLMTransformer.forward\u001b[0;34m(self, hidden_states, attention_mask, rotary_pos_emb, kv_caches, use_cache, output_hidden_states)\u001b[0m\n\u001b[1;32m    717\u001b[0m     layer_ret \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mutils\u001b[38;5;241m.\u001b[39mcheckpoint\u001b[38;5;241m.\u001b[39mcheckpoint(\n\u001b[1;32m    718\u001b[0m         layer,\n\u001b[1;32m    719\u001b[0m         hidden_states,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    724\u001b[0m         use_reentrant\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    725\u001b[0m     )\n\u001b[1;32m    726\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 727\u001b[0m     layer_ret \u001b[38;5;241m=\u001b[39m \u001b[43mlayer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    728\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    729\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    730\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrotary_pos_emb\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    731\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkv_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkv_caches\u001b[49m\u001b[43m[\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    732\u001b[0m \u001b[43m        \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cache\u001b[49m\n\u001b[1;32m    733\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    734\u001b[0m hidden_states, kv_cache \u001b[38;5;241m=\u001b[39m layer_ret\n\u001b[1;32m    735\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_cache:\n\u001b[1;32m    736\u001b[0m     \u001b[38;5;66;03m# token by token decoding, use tuple format\u001b[39;00m\n","File \u001b[0;32m~/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m~/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n","File \u001b[0;32m~/miniconda3/envs/llama-factory/lib/python3.11/site-packages/accelerate/hooks.py:166\u001b[0m, in \u001b[0;36madd_hook_to_module.<locals>.new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    164\u001b[0m         output \u001b[38;5;241m=\u001b[39m module\u001b[38;5;241m.\u001b[39m_old_forward(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    165\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 166\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_old_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    167\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\u001b[38;5;241m.\u001b[39m_hf_hook\u001b[38;5;241m.\u001b[39mpost_forward(module, output)\n","File \u001b[0;32m~/.cache/huggingface/modules/transformers_modules/THUDM/glm-4-9b-chat-1m/7d23e9e0bb8e63d85c4489b37b099489c1515370/modeling_chatglm.py:651\u001b[0m, in \u001b[0;36mGLMBlock.forward\u001b[0;34m(self, hidden_states, attention_mask, rotary_pos_emb, kv_cache, use_cache)\u001b[0m\n\u001b[1;32m    648\u001b[0m layernorm_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpost_attention_layernorm(layernorm_input)\n\u001b[1;32m    650\u001b[0m \u001b[38;5;66;03m# MLP.\u001b[39;00m\n\u001b[0;32m--> 651\u001b[0m mlp_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmlp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlayernorm_output\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    653\u001b[0m \u001b[38;5;66;03m# Second residual connection.\u001b[39;00m\n\u001b[1;32m    654\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_residual_connection_post_layernorm:\n","File \u001b[0;32m~/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m~/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n","File \u001b[0;32m~/miniconda3/envs/llama-factory/lib/python3.11/site-packages/accelerate/hooks.py:166\u001b[0m, in \u001b[0;36madd_hook_to_module.<locals>.new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    164\u001b[0m         output \u001b[38;5;241m=\u001b[39m module\u001b[38;5;241m.\u001b[39m_old_forward(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    165\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 166\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_old_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    167\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\u001b[38;5;241m.\u001b[39m_hf_hook\u001b[38;5;241m.\u001b[39mpost_forward(module, output)\n","File \u001b[0;32m~/.cache/huggingface/modules/transformers_modules/THUDM/glm-4-9b-chat-1m/7d23e9e0bb8e63d85c4489b37b099489c1515370/modeling_chatglm.py:584\u001b[0m, in \u001b[0;36mMLP.forward\u001b[0;34m(self, hidden_states)\u001b[0m\n\u001b[1;32m    582\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, hidden_states):\n\u001b[1;32m    583\u001b[0m     \u001b[38;5;66;03m# [s, b, 4hp]\u001b[39;00m\n\u001b[0;32m--> 584\u001b[0m     intermediate_parallel \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdense_h_to_4h\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    585\u001b[0m     intermediate_parallel \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mactivation_func(intermediate_parallel)\n\u001b[1;32m    586\u001b[0m     \u001b[38;5;66;03m# [s, b, h]\u001b[39;00m\n","File \u001b[0;32m~/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m~/miniconda3/envs/llama-factory/lib/python3.11/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n","File \u001b[0;32m~/miniconda3/envs/llama-factory/lib/python3.11/site-packages/accelerate/hooks.py:161\u001b[0m, in \u001b[0;36madd_hook_to_module.<locals>.new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    160\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnew_forward\u001b[39m(module, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 161\u001b[0m     args, kwargs \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_hf_hook\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpre_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    162\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m module\u001b[38;5;241m.\u001b[39m_hf_hook\u001b[38;5;241m.\u001b[39mno_grad:\n\u001b[1;32m    163\u001b[0m         \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():\n","File \u001b[0;32m~/miniconda3/envs/llama-factory/lib/python3.11/site-packages/accelerate/hooks.py:347\u001b[0m, in \u001b[0;36mAlignDevicesHook.pre_forward\u001b[0;34m(self, module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    339\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m    340\u001b[0m             value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    341\u001b[0m             \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtied_params_map \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    342\u001b[0m             \u001b[38;5;129;01mand\u001b[39;00m value\u001b[38;5;241m.\u001b[39mdata_ptr() \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtied_params_map\n\u001b[1;32m    343\u001b[0m             \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexecution_device \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtied_params_map[value\u001b[38;5;241m.\u001b[39mdata_ptr()]\n\u001b[1;32m    344\u001b[0m         ):\n\u001b[1;32m    345\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtied_pointers_to_remove\u001b[38;5;241m.\u001b[39madd((value\u001b[38;5;241m.\u001b[39mdata_ptr(), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexecution_device))\n\u001b[0;32m--> 347\u001b[0m         \u001b[43mset_module_tensor_to_device\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    348\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmodule\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    349\u001b[0m \u001b[43m            \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    350\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecution_device\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    351\u001b[0m \u001b[43m            \u001b[49m\u001b[43mvalue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    352\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfp16_statistics\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfp16_statistics\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    353\u001b[0m \u001b[43m            \u001b[49m\u001b[43mtied_params_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtied_params_map\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    354\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    356\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m send_to_device(args, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexecution_device), send_to_device(\n\u001b[1;32m    357\u001b[0m     kwargs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexecution_device, skip_keys\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mskip_keys\n\u001b[1;32m    358\u001b[0m )\n","File \u001b[0;32m~/miniconda3/envs/llama-factory/lib/python3.11/site-packages/accelerate/utils/modeling.py:400\u001b[0m, in \u001b[0;36mset_module_tensor_to_device\u001b[0;34m(module, tensor_name, device, value, dtype, fp16_statistics, tied_params_map)\u001b[0m\n\u001b[1;32m    398\u001b[0m             module\u001b[38;5;241m.\u001b[39m_parameters[tensor_name] \u001b[38;5;241m=\u001b[39m param_cls(new_value, requires_grad\u001b[38;5;241m=\u001b[39mold_value\u001b[38;5;241m.\u001b[39mrequires_grad)\n\u001b[1;32m    399\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value, torch\u001b[38;5;241m.\u001b[39mTensor):\n\u001b[0;32m--> 400\u001b[0m     new_value \u001b[38;5;241m=\u001b[39m \u001b[43mvalue\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    401\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    402\u001b[0m     new_value \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor(value, device\u001b[38;5;241m=\u001b[39mdevice)\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"source":["%%time\n","\n","evaluate_model(model, tokenizer, f\"{model_name}{'_' + adapter_name_or_path if adapter_name_or_path else ''}_NV4080\", datasets[\"test\"])"]}],"metadata":{"accelerator":"GPU","application/vnd.databricks.v1+notebook":{"dashboards":[],"environmentMetadata":null,"language":"python","notebookMetadata":{"mostRecentlyExecutedCommandWithImplicitDF":{"commandId":-1,"dataframes":["_sqldf"]},"pythonIndentUnit":4},"notebookName":"10_eval-lf-medium-py3.11","widgets":{}},"colab":{"gpuType":"L4","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.9"}},"nbformat":4,"nbformat_minor":0}
+{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":476,"status":"ok","timestamp":1720679526275,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"uWKRSV6eZsCn"},"outputs":[],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":2,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"eb33b19f-1206-41ee-84e2-e6258a12eef7","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2534,"status":"ok","timestamp":1720679529344,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"xwFh14uiZBrI","outputId":"d767799c-34c2-46a5-f052-378146a55321"},"outputs":[],"source":["from pathlib import Path\n","\n","try:\n","    from google.colab import drive\n","\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":3,"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":5,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"G5pNu3zgZBrL","outputId":"160a554f-fb08-4aa0-bc00-0422fb7c1fac"},"outputs":[{"name":"stdout","output_type":"stream","text":["workding dir: /home/inflaton/code/projects/courses/logical-reasoning\n"]}],"source":["import os\n","import sys\n","from pathlib import Path\n","\n","os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"workding dir:\", workding_dir)"]},{"cell_type":"code","execution_count":4,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"9f67ec60-2f24-411c-84eb-0dd664b44775","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"hPCC-6m7ZBrM","outputId":"c7aa2c96-5e99-440a-c148-201d79465ff9"},"outputs":[{"name":"stdout","output_type":"stream","text":["loading env vars from: /home/inflaton/code/projects/courses/logical-reasoning/.env\n"]},{"data":{"text/plain":["True"]},"execution_count":4,"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":5,"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":3,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"1M3IraVtZBrM","outputId":"29ab35f6-2970-4ade-d85d-3174acf8cda0"},"outputs":[{"name":"stdout","output_type":"stream","text":["internlm/internlm2_5-7b-chat-1m inflaton-ai/InternLM_2_5-7b_LoRA-Adapter True datasets/mgtv results/mgtv-results_internlm_4bit.csv\n"]}],"source":["import os\n","\n","model_name = os.getenv(\"MODEL_NAME\")\n","adapter_name_or_path = os.getenv(\"ADAPTER_NAME_OR_PATH\")\n","load_in_4bit = os.getenv(\"LOAD_IN_4BIT\") == \"true\"\n","data_path = os.getenv(\"LOGICAL_REASONING_DATA_PATH\")\n","results_path = os.getenv(\"LOGICAL_REASONING_RESULTS_PATH\")\n","use_english_datasets = os.getenv(\"USE_ENGLISH_DATASETS\") == \"true\"\n","\n","print(model_name, adapter_name_or_path, load_in_4bit, data_path, results_path)"]},{"cell_type":"code","execution_count":16,"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":564,"status":"ok","timestamp":1720679529907,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"UgMvt6dIZBrM","outputId":"ce37581c-fd26-46c2-ad87-d933d99f68f7"},"outputs":[{"name":"stdout","output_type":"stream","text":["Python 3.11.9\n","Name: transformers\n","Version: 4.43.0.dev0\n","Summary: State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow\n","Home-page: https://github.com/huggingface/transformers\n","Author: The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/transformers/graphs/contributors)\n","Author-email: [email protected]\n","License: Apache 2.0 License\n","Location: /home/inflaton/miniconda3/envs/llama-factory/lib/python3.11/site-packages\n","Requires: filelock, huggingface-hub, numpy, packaging, pyyaml, regex, requests, safetensors, tokenizers, tqdm\n","Required-by: llamafactory, peft, trl, vllm\n","CPU times: user 121 ms, sys: 61.4 ms, total: 183 ms\n","Wall time: 4.8 s\n"]}],"source":["%%time\n","os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n","\n","!python --version\n","!pip show transformers"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1685,"status":"ok","timestamp":1720679531591,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"ZuS_FsLyZBrN","outputId":"2cba0105-c505-4395-afbd-2f2fee6581d0"},"outputs":[{"name":"stdout","output_type":"stream","text":["loading /home/inflaton/code/projects/courses/logical-reasoning/llm_toolkit/logical_reasoning_utils.py\n","GPU is available\n"]}],"source":["from llm_toolkit.llm_utils import *\n","from llm_toolkit.logical_reasoning_utils import *\n","\n","device = check_gpu()"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["loading model: internlm/internlm2_5-7b-chat-1m with adapter: inflaton-ai/InternLM_2_5-7b_LoRA-Adapter\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"87acc39044d740678c277ce98df127ae","version_major":2,"version_minor":0},"text/plain":["Loading checkpoint shards:   0%|          | 0/8 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"98f2d4b6dcd24bc899c5c68f15b8c583","version_major":2,"version_minor":0},"text/plain":["adapter_config.json:   0%|          | 0.00/679 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"8b5282f7c87c484ebd9c56fc321a4a6c","version_major":2,"version_minor":0},"text/plain":["adapter_model.safetensors:   0%|          | 0.00/37.8M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["CPU times: user 2min 18s, sys: 9min 43s, total: 12min 2s\n","Wall time: 1h 41min 28s\n"]}],"source":["%%time\n","\n","model, tokenizer = load_model(model_name, adapter_name_or_path=adapter_name_or_path, load_in_4bit=load_in_4bit, using_llama_factory=False)"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["loading train/test data files\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"f6a9ee0f310e44249f0360774a1e1585","version_major":2,"version_minor":0},"text/plain":["Map:   0%|          | 0/25000 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"b74a5771a94f4321b0d6862bdcc30207","version_major":2,"version_minor":0},"text/plain":["Map:   0%|          | 0/3000 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["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"]}],"source":["datasets = load_logical_reasoning_dataset(\n","    data_path,\n","    tokenizer=tokenizer,\n","    chinese_prompt=not use_english_datasets,\n","    using_p1=False,\n",")"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[],"source":["import numpy as np\n","\n","\n","def evaluate_model_with_repetition_penalty(\n","    model,\n","    tokenizer,\n","    model_name,\n","    dataset,\n","    start_repetition_penalty=1.0,\n","    end_repetition_penalty=1.15,\n","    step_repetition_penalty=0.01,\n","):\n","    print(f\"Evaluating model: {model_name} on {device}\")\n","\n","    for repetition_penalty in np.arange(\n","        start_repetition_penalty,\n","        end_repetition_penalty + step_repetition_penalty,\n","        step_repetition_penalty,\n","    ):\n","        print(f\"*** Evaluating with repetition_penalty: {repetition_penalty}\")\n","        predictions = eval_model(\n","            model,\n","            tokenizer,\n","            dataset,\n","            device=device,\n","            repetition_penalty=repetition_penalty,\n","            max_new_tokens=8,\n","        )\n","\n","        model_name_with_rp = f\"{model_name}_4bit_rp{repetition_penalty}\"\n","\n","        save_results(\n","            model_name_with_rp,\n","            results_path,\n","            dataset,\n","            predictions,\n","            debug=True,\n","        )\n","\n","        metrics = calc_metrics(dataset[\"label\"], predictions, debug=False)\n","        print(metrics)"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Evaluating model: internlm/internlm2_5-7b-chat-1m on cuda\n","*** Evaluating with repetition_penalty: 1.0\n"]},{"name":"stderr","output_type":"stream","text":["  0%|          | 1/3000 [00:17<14:45:44, 17.72s/it]"]},{"name":"stdout","output_type":"stream","text":["--------\n","step 1: 不是\n","--------\n","step 2: 不是\n","--------\n","step 3: 不是\n","--------\n","step 4: 不是\n","--------\n","step 5: 不是\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 3000/3000 [2:23:40<00:00,  2.87s/it]  \n"]},{"name":"stdout","output_type":"stream","text":["      text label title                                             puzzle  \\\n","0  甄加索是自杀吗    不是  海岸之谜  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...   \n","\n","                                               truth  \\\n","0  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...   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.0  \n","0                                         不是  \n","{'accuracy': 0.783}\n","*** Evaluating with repetition_penalty: 1.01\n"]},{"name":"stderr","output_type":"stream","text":["  0%|          | 1/3000 [00:01<54:45,  1.10s/it]"]},{"name":"stdout","output_type":"stream","text":["--------\n","step 1: 不是\n","--------\n","step 2: 不是\n","--------\n","step 3: 不是\n","--------\n","step 4: 不是\n","--------\n","step 5: 不是\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 3000/3000 [53:34<00:00,  1.07s/it] \n"]},{"name":"stdout","output_type":"stream","text":["      text label title                                             puzzle  \\\n","0  甄加索是自杀吗    不是  海岸之谜  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...   \n","\n","                                               truth  \\\n","0  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...   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.0  \\\n","0                                         不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.01  \n","0                                          不是  \n","{'accuracy': 0.783}\n","*** Evaluating with repetition_penalty: 1.02\n"]},{"name":"stderr","output_type":"stream","text":["  0%|          | 1/3000 [00:01<52:52,  1.06s/it]"]},{"name":"stdout","output_type":"stream","text":["--------\n","step 1: 不是\n","--------\n","step 2: 不是\n","--------\n","step 3: 不是\n","--------\n","step 4: 不是\n","--------\n","step 5: 不是\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 3000/3000 [53:33<00:00,  1.07s/it] \n"]},{"name":"stdout","output_type":"stream","text":["      text label title                                             puzzle  \\\n","0  甄加索是自杀吗    不是  海岸之谜  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...   \n","\n","                                               truth  \\\n","0  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...   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.0  \\\n","0                                         不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.01  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.02  \n","0                                          不是  \n","{'accuracy': 0.783}\n","*** Evaluating with repetition_penalty: 1.03\n"]},{"name":"stderr","output_type":"stream","text":["  0%|          | 1/3000 [00:01<52:45,  1.06s/it]"]},{"name":"stdout","output_type":"stream","text":["--------\n","step 1: 不是\n","--------\n","step 2: 不是\n","--------\n","step 3: 不是\n","--------\n","step 4: 不是\n","--------\n","step 5: 不是\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 3000/3000 [53:32<00:00,  1.07s/it] \n"]},{"name":"stdout","output_type":"stream","text":["      text label title                                             puzzle  \\\n","0  甄加索是自杀吗    不是  海岸之谜  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...   \n","\n","                                               truth  \\\n","0  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...   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.0  \\\n","0                                         不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.01  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.02  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.03  \n","0                                          不是  \n","{'accuracy': 0.783}\n","*** Evaluating with repetition_penalty: 1.04\n"]},{"name":"stderr","output_type":"stream","text":["  0%|          | 1/3000 [00:01<52:21,  1.05s/it]"]},{"name":"stdout","output_type":"stream","text":["--------\n","step 1: 不是\n","--------\n","step 2: 不是\n","--------\n","step 3: 不是\n","--------\n","step 4: 不是\n","--------\n","step 5: 不是\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 3000/3000 [53:36<00:00,  1.07s/it] \n"]},{"name":"stdout","output_type":"stream","text":["      text label title                                             puzzle  \\\n","0  甄加索是自杀吗    不是  海岸之谜  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...   \n","\n","                                               truth  \\\n","0  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...   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.0  \\\n","0                                         不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.01  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.02  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.03  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.04  \n","0                                          不是  \n","{'accuracy': 0.783}\n","*** Evaluating with repetition_penalty: 1.05\n"]},{"name":"stderr","output_type":"stream","text":["  0%|          | 1/3000 [00:01<51:10,  1.02s/it]"]},{"name":"stdout","output_type":"stream","text":["--------\n","step 1: 不是\n","--------\n","step 2: 不是\n","--------\n","step 3: 不是\n","--------\n","step 4: 不是\n","--------\n","step 5: 不是\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 3000/3000 [51:51<00:00,  1.04s/it] \n"]},{"name":"stdout","output_type":"stream","text":["      text label title                                             puzzle  \\\n","0  甄加索是自杀吗    不是  海岸之谜  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...   \n","\n","                                               truth  \\\n","0  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...   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.0  \\\n","0                                         不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.01  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.02  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.03  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.04  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.05  \n","0                                          不是  \n","{'accuracy': 0.783}\n","*** Evaluating with repetition_penalty: 1.06\n"]},{"name":"stderr","output_type":"stream","text":["  0%|          | 1/3000 [00:01<52:24,  1.05s/it]"]},{"name":"stdout","output_type":"stream","text":["--------\n","step 1: 不是\n","--------\n","step 2: 不是\n","--------\n","step 3: 不是\n","--------\n","step 4: 不是\n","--------\n","step 5: 不是\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 3000/3000 [51:39<00:00,  1.03s/it] \n"]},{"name":"stdout","output_type":"stream","text":["      text label title                                             puzzle  \\\n","0  甄加索是自杀吗    不是  海岸之谜  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...   \n","\n","                                               truth  \\\n","0  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...   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.0  \\\n","0                                         不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.01  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.02  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.03  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.04  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.05  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.06  \n","0                                          不是  \n","{'accuracy': 0.783}\n","*** Evaluating with repetition_penalty: 1.07\n"]},{"name":"stderr","output_type":"stream","text":["  0%|          | 1/3000 [00:01<50:37,  1.01s/it]"]},{"name":"stdout","output_type":"stream","text":["--------\n","step 1: 不是\n","--------\n","step 2: 不是\n","--------\n","step 3: 不是\n","--------\n","step 4: 不是\n","--------\n","step 5: 不是\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 3000/3000 [51:43<00:00,  1.03s/it] \n"]},{"name":"stdout","output_type":"stream","text":["      text label title                                             puzzle  \\\n","0  甄加索是自杀吗    不是  海岸之谜  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...   \n","\n","                                               truth  \\\n","0  甄加索是一位热爱自然的画家，他���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...   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.0  \\\n","0                                         不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.01  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.02  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.03  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.04  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.05  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.06  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.07  \n","0                                          不是  \n","{'accuracy': 0.783}\n","*** Evaluating with repetition_penalty: 1.08\n"]},{"name":"stderr","output_type":"stream","text":["  0%|          | 1/3000 [00:01<51:32,  1.03s/it]"]},{"name":"stdout","output_type":"stream","text":["--------\n","step 1: 不是\n","--------\n","step 2: 不是\n","--------\n","step 3: 不是\n","--------\n","step 4: 不是\n","--------\n","step 5: 不是\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 3000/3000 [51:40<00:00,  1.03s/it] \n"]},{"name":"stdout","output_type":"stream","text":["      text label title                                             puzzle  \\\n","0  甄加索是自杀吗    不是  海岸之谜  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...   \n","\n","                                               truth  \\\n","0  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...   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.0  \\\n","0                                         不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.01  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.02  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.03  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.04  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.05  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.06  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.07  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.08  \n","0                                          不是  \n","{'accuracy': 0.7796666666666666}\n","*** Evaluating with repetition_penalty: 1.09\n"]},{"name":"stderr","output_type":"stream","text":["  0%|          | 1/3000 [00:01<53:33,  1.07s/it]"]},{"name":"stdout","output_type":"stream","text":["--------\n","step 1: 不是\n","--------\n","step 2: 不是\n","--------\n","step 3: 不是\n","--------\n","step 4: 不是\n","--------\n","step 5: 不是\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 3000/3000 [51:40<00:00,  1.03s/it] \n"]},{"name":"stdout","output_type":"stream","text":["      text label title                                             puzzle  \\\n","0  甄加索是自杀吗    不是  海岸之谜  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...   \n","\n","                                               truth  \\\n","0  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...   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.0  \\\n","0                                         不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.01  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.02  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.03  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.04  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.05  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.06  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.07  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.08  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.09  \n","0                                          不是  \n","{'accuracy': 0.694}\n","*** Evaluating with repetition_penalty: 1.1\n"]},{"name":"stderr","output_type":"stream","text":["  0%|          | 1/3000 [00:01<50:35,  1.01s/it]"]},{"name":"stdout","output_type":"stream","text":["--------\n","step 1: 不是\n","--------\n","step 2: 不是\n","--------\n","step 3: 不是\n","--------\n","step 4: 不是\n","--------\n","step 5: 不是\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 3000/3000 [51:39<00:00,  1.03s/it] \n"]},{"name":"stdout","output_type":"stream","text":["      text label title                                             puzzle  \\\n","0  甄加索是自杀吗    不是  海岸之谜  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...   \n","\n","                                               truth  \\\n","0  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...   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.0  \\\n","0                                         不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.01  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.02  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.03  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.04  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.05  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.06  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.07  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.08  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.09  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.1  \n","0                                         不是  \n","{'accuracy': 0.532}\n","*** Evaluating with repetition_penalty: 1.11\n"]},{"name":"stderr","output_type":"stream","text":["  0%|          | 1/3000 [00:01<51:02,  1.02s/it]"]},{"name":"stdout","output_type":"stream","text":["--------\n","step 1: 不是\n","--------\n","step 2: 不是\n","--------\n","step 3: 不是\n","--------\n","step 4: 不是\n","--------\n","step 5: 不是\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 3000/3000 [51:34<00:00,  1.03s/it] \n"]},{"name":"stdout","output_type":"stream","text":["      text label title                                             puzzle  \\\n","0  甄加索是自杀吗    不是  海岸之谜  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...   \n","\n","                                               truth  \\\n","0  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...   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.0  \\\n","0                                         不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.01  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.02  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.03  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.04  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.05  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.06  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.07  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.08  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.09  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.1  \\\n","0                                         不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.11  \n","0                                          不是  \n","{'accuracy': 0.4703333333333333}\n","*** Evaluating with repetition_penalty: 1.12\n"]},{"name":"stderr","output_type":"stream","text":["  0%|          | 1/3000 [00:01<50:52,  1.02s/it]"]},{"name":"stdout","output_type":"stream","text":["--------\n","step 1: 不是\n","--------\n","step 2: 不是\n","--------\n","step 3: 不是\n","--------\n","step 4: 不是\n","--------\n","step 5: 不是\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 3000/3000 [53:35<00:00,  1.07s/it] \n"]},{"name":"stdout","output_type":"stream","text":["      text label title                                             puzzle  \\\n","0  甄加索是自杀吗    不是  海岸之谜  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...   \n","\n","                                               truth  \\\n","0  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...   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.0  \\\n","0                                         不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.01  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.02  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.03  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.04  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.05  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.06  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.07  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.08  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.09  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.1  \\\n","0                                         不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.11  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.12  \n","0                                          不是  \n","{'accuracy': 0.438}\n","*** Evaluating with repetition_penalty: 1.1300000000000001\n"]},{"name":"stderr","output_type":"stream","text":["  0%|          | 1/3000 [00:01<53:19,  1.07s/it]"]},{"name":"stdout","output_type":"stream","text":["--------\n","step 1: 不是\n","--------\n","step 2: 不是\n","--------\n","step 3: 不是\n","--------\n","step 4: 不是\n","--------\n","step 5: 不是\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 3000/3000 [52:44<00:00,  1.05s/it] \n"]},{"name":"stdout","output_type":"stream","text":["      text label title                                             puzzle  \\\n","0  甄加索是自杀吗    不是  海岸之谜  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...   \n","\n","                                               truth  \\\n","0  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...   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.0  \\\n","0                                         不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.01  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.02  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.03  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.04  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.05  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.06  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.07  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.08  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.09  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.1  \\\n","0                                         不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.11  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.12  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.1300000000000001  \n","0                                                 不是         \n","{'accuracy': 0.4076666666666667}\n","*** Evaluating with repetition_penalty: 1.1400000000000001\n"]},{"name":"stderr","output_type":"stream","text":["  0%|          | 1/3000 [00:01<51:16,  1.03s/it]"]},{"name":"stdout","output_type":"stream","text":["--------\n","step 1: 不是\n","--------\n","step 2: 不是\n","--------\n","step 3: 不是\n","--------\n","step 4: 不是\n","--------\n","step 5: 不是\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 3000/3000 [52:24<00:00,  1.05s/it] \n"]},{"name":"stdout","output_type":"stream","text":["      text label title                                             puzzle  \\\n","0  甄加索是自杀吗    不是  海岸之谜  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...   \n","\n","                                               truth  \\\n","0  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...   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.0  \\\n","0                                         不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.01  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.02  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.03  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.04  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.05  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.06  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.07  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.08  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.09  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.1  \\\n","0                                         不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.11  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.12  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.1300000000000001  \\\n","0                                                 不是          \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.1400000000000001  \n","0                                                 不是         \n","{'accuracy': 0.35933333333333334}\n","*** Evaluating with repetition_penalty: 1.1500000000000001\n"]},{"name":"stderr","output_type":"stream","text":["  0%|          | 1/3000 [00:01<51:49,  1.04s/it]"]},{"name":"stdout","output_type":"stream","text":["--------\n","step 1: 不是\n","--------\n","step 2: 不是\n","--------\n","step 3: 不是\n","--------\n","step 4: 不是\n","--------\n","step 5: 不是\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 3000/3000 [53:06<00:00,  1.06s/it] \n"]},{"name":"stdout","output_type":"stream","text":["      text label title                                             puzzle  \\\n","0  甄加索是自杀吗    不是  海岸之谜  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...   \n","\n","                                               truth  \\\n","0  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...   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.0  \\\n","0                                         不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.01  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.02  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.03  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.04  ...  \\\n","0                                          不是  ...   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.06  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.07  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.08  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.09  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.1  \\\n","0                                         不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.11  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.12  \\\n","0                                          不是   \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.1300000000000001  \\\n","0                                                 不是          \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.1400000000000001  \\\n","0                                                 不是          \n","\n","  internlm/internlm2_5-7b-chat-1m_4bit_rp1.1500000000000001  \n","0                                                 不是         \n","\n","[1 rows x 21 columns]\n","{'accuracy': 0.301}\n","CPU times: user 14h 58min 15s, sys: 35min 2s, total: 15h 33min 18s\n","Wall time: 15h 31min 41s\n"]}],"source":["%%time\n","\n","evaluate_model_with_repetition_penalty(model, tokenizer, model_name, datasets[\"test\"])"]}],"metadata":{"accelerator":"GPU","application/vnd.databricks.v1+notebook":{"dashboards":[],"environmentMetadata":null,"language":"python","notebookMetadata":{"mostRecentlyExecutedCommandWithImplicitDF":{"commandId":-1,"dataframes":["_sqldf"]},"pythonIndentUnit":4},"notebookName":"10_eval-lf-medium-py3.11","widgets":{}},"colab":{"gpuType":"L4","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.9"}},"nbformat":4,"nbformat_minor":0}