inference-cabra-kaggle.ipynb

{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2023-07-19T19:55:26.942747Z","iopub.status.busy":"2023-07-19T19:55:26.942121Z","iopub.status.idle":"2023-07-19T19:55:28.408503Z","shell.execute_reply":"2023-07-19T19:55:28.406999Z","shell.execute_reply.started":"2023-07-19T19:55:26.942705Z"},"id":"F1eNRfEVix2D","outputId":"e5256bfb-3220-47b3-b55d-399651f8ec84","trusted":true},"outputs":[],"source":["# !nvidia-smi"]},{"cell_type":"code","execution_count":2,"metadata":{"execution":{"iopub.execute_input":"2023-07-19T19:55:28.412354Z","iopub.status.busy":"2023-07-19T19:55:28.411637Z","iopub.status.idle":"2023-07-19T19:57:19.628813Z","shell.execute_reply":"2023-07-19T19:57:19.627468Z","shell.execute_reply.started":"2023-07-19T19:55:28.412312Z"},"trusted":true},"outputs":[],"source":["# !pip install torch\n","# !pip install bitsandbytes\n","# !pip install accelerate@git+https://github.com/huggingface/accelerate.git\n","# !pip install transformers@git+https://github.com/huggingface/transformers.git\n","# !pip install peft@git+https://github.com/huggingface/peft.git"]},{"cell_type":"code","execution_count":3,"metadata":{"execution":{"iopub.execute_input":"2023-07-19T19:57:19.631841Z","iopub.status.busy":"2023-07-19T19:57:19.631380Z","iopub.status.idle":"2023-07-19T19:57:19.639233Z","shell.execute_reply":"2023-07-19T19:57:19.636856Z","shell.execute_reply.started":"2023-07-19T19:57:19.631795Z"},"id":"Fv-BYdUbkGN_","trusted":true},"outputs":[],"source":["# Uncomment if necessary\n","# import os\n","# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\""]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2023-07-19T19:57:19.644123Z","iopub.status.busy":"2023-07-19T19:57:19.643165Z","iopub.status.idle":"2023-07-19T19:57:19.655142Z","shell.execute_reply":"2023-07-19T19:57:19.653951Z","shell.execute_reply.started":"2023-07-19T19:57:19.644089Z"},"id":"S9w8N6whkMkN","trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["/home/r/anaconda3/envs/llama/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n","  from .autonotebook import tqdm as notebook_tqdm\n"]}],"source":["import torch\n","from peft import PeftModel\n","from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":374,"referenced_widgets":["1fe3fa9e863a42eb9757f8a10f75872a","e08dfa2655fa449c8276f27298e886c3","7ff807b669a342cca49f28a4537ef3e5","52de7533762540a68744ef037db2979f","3364e9c9b3a84eea9e27318b91acc3d3","c3836ca7d81e47f4a5559e9a1613c152","e94be44a09d940139f6d3ad055574ef7","49e23dd98a5a4e2aa8e88c63425677a8","452c08414c444c81b548a932d8a3ebb3","a24a3df96d6f4d308cc37909de8a5034","9b749013b93942e48c2ab488f2dca6ae","0e804e7b582b4bd9b2211c53b6330e0c","533db68fdc2946868bc1601ab4546a58","e7b9fe9fb4bc48b187937814eec2533d","f04623254c7942aeac192df8be7e36b3","c043d94fc3d64cd7a1d972a55b371454","80ac78cbb27d4e608b01e23edde18479","b510c21e2c214b00aa90d28f9013bebc","46f55c99f4a745d4948a8aa92badb500","45a8c9fd178248f8a6fb4aa7751ed6fe","c445af2f18e14ce8bbb1ffe0bb305d9c","48342d708eb4476aa1378d61e8ab7740","be18bb17615c42dcac2da02f3adb9873","3e8700b08a0e4f61b06cb4c526155f8b","2ec802aa19784c53b8544c9d6dfea15a","4d3852167a444097998f1a12550a01f0","79c87a0549b643c8ba3d42094b331e42","23bc3f1d9c464beb83cc7aae3626f82a","46e54271029e4eb6a80a3bbd1d9856de","b2dad94d66174099845ab4efaca27ad6","8bc5899e089a4fc5a197369e9dd07cb3","d6db5dbd2e0e42c199eb0957124de7dd","e9be38fb432e48f6b395dca5112a9f4b","3ddb85b749264851bdf8a2ca12fab0bf","50f839d255b24244aa25c5885c11b9ec","1a2e50e7ead544329abaa3d9d82c8eef","b8f8ec84a0c34f70a9873e0d37e248eb","dee880fc5d764450a2eccfe88e62785e","bfb36134ae324b6b89b8ef05a3cc81b9","3005bbdd0eef4744b953696e0b625639","c691bc4969c04f72b060752945899490","a7c670c9f971404fb755c20a652d1a89","9b69fb6dd4fb4bcc9c2e2536d7024307","ba67e1e4c6794a809fd973fdbee704a3","0890c35ca8484cc1957332a4dff56054","b5fc9dfedb744180bae9ea7c37d8de46","8172544adaf44c3fa02879eb6c0244e3","04427a76181a41c0bbc1cbe52ea54dad","911bbd3a9a984b8cbabb866db94d5d85","7f1cce0992c344bcb0dbb72f0599b5a3","8d0bf3e406ee47c2abc4bc07c82376e5","029bc110fd564ac19c1175ef5667bc32","be452a9d43234658a9f16aa8878da863","52cab7f2bf6f4a228da8db3d592657f1","7a3fdb0585b24724841c78d3e95787c4","6b07d028df0149719e31263431c7d8d5","88c89d4697ec43dcbfe38ac1aa1cdd9b","a969a878c5c14780b5accf3dbc939bba","9ebfa21789e745df9297cbf090173cb6","540b301b5a604c5a9ce2561117e8dc83","15a19b8693bb4261b4e09867447addbb","f370be6f70564cc88d6359788fbd94ad","41edfb6dcc0c48e497c793d76937da4d","1474b973be8f4ea7a4f65217b4d0298b","3d50096c7aec4fd9a624286ac55ad530","8286e5823d7c4b398678ab937a6988d1","3d046bb7a3d94e33bfbe5f960aced875","76ff1176ea9d4665bd0a29240a5f7596","45865273b9594d00b947d4a0b0fba0cb","b1cf47cec90140689983d7591c806ce9","c79f4361b49f462182af72097ec739f9","8255b8279a094101a3aeb0be370a0e58","65eaaf0ea7e7482190d519d617bee9e4","6af3678a25424ed2961a3f16e63bd62c","b293409ca5364672b3264ac1890ed582","8b29d4d171b5434b8800263c08cb8d3a","5bbc5324b60947c4be0928c964392a48","d0c80a073ed1426d8521d2f0c9234089","9fd1e3930c41448f98d8eef2f9e30d53","1ff527d51f424853b014bfba773678ab","619d0b0ffde548f89c6ff453ff79bd96","7f1942d840f248a3a5a5f91fd797d123","d55fa94f11b548a0abd207b1dd006e10","de817732784e4ebaa8973c0002f3e4f8","565dd4962fb7470199d8f116fbc59312","e3afe28335584d9d9865a83cb55f8263","7772f5420e42492ca01d849f0b9695b1","10d4a8ed16a74ce3885554c97e90ef78","6d2fa1630e59477a8a797977793bb6a1","7dabd9cafbce449f8705b0ea4dbaec23","1972ab24b8374d51a6803e218c58cd27","0b001c210d234e26b9c75aba0e90818f","3100842ae9cd478e929b175f2a50368a","67c4325d42f04641ba2b8d12eb110c18","aa4d6f2e58cc4e59960291b01c404770","8175518eaa5145908c4b77177be4356a","0ca06674785c4c199d4535370993f905","7bf0b838fea34c368ff1e9c2aead905a","cb59e709c7ae4c3f9d2078a872005edd","c770ee957da7453795448b88ec3a1ca0","3527d18121ed47aeaa158208109891db","42c94720363342f1aa6b73bfaf3c3f1e","01cfcd6aed8e419380b976fa6f5f5a31","511de3d3aa8d4b1a83fd6e91c3badf65","6d7ea9921f5f4350b58c101edae8ffc8","0cf9ae568e5f426fbf922574de9668cf","2eae8053c9664750b4d00373fef3eef2","72a3d1ddeba9462cb25eaf749052ffd5","4c576158dbf44db9a18dcbdd3389d1d0","0dc59f2c7b1846228c5e81ed24fc931b"]},"execution":{"iopub.execute_input":"2023-07-19T19:57:19.657778Z","iopub.status.busy":"2023-07-19T19:57:19.657262Z","iopub.status.idle":"2023-07-19T19:59:44.805146Z","shell.execute_reply":"2023-07-19T19:59:44.804067Z","shell.execute_reply.started":"2023-07-19T19:57:19.657739Z"},"id":"R-3H-7V6kPNc","outputId":"090feda0-2dd4-47aa-8b9a-81fbe9456955","trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["Loading the tokenizer from the `special_tokens_map.json` and the `added_tokens.json` will be removed in `transformers 5`,  it is kept for forward compatibility, but it is recommended to update your `tokenizer_config.json` by uploading it again. You will see the new `added_tokens_decoder` attribute that will store the relevant information.\n","You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. If you see this, DO NOT PANIC! This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thouroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n","You shouldn't move a model when it is dispatched on multiple devices.\n"]},{"ename":"ValueError","evalue":"`.to` is not supported for `4-bit` or `8-bit` bitsandbytes models. Please use the model as it is, since the model has already been set to the correct devices and casted to the correct `dtype`.","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[1;32m/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb Cell 5\u001b[0m line \u001b[0;36m1\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#W4sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m tokenizer \u001b[39m=\u001b[39m AutoTokenizer\u001b[39m.\u001b[39mfrom_pretrained(base_model_name_or_path, padding_side\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mright\u001b[39m\u001b[39m\"\u001b[39m, use_fast\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#W4sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m \u001b[39m# tokenizer.pad_token = tokenizer.eos_token\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#W4sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m base_model \u001b[39m=\u001b[39m AutoModelForCausalLM\u001b[39m.\u001b[39;49mfrom_pretrained(\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#W4sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m     base_model_name_or_path,\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#W4sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m     \u001b[39m# load_in_4bit=True,\u001b[39;49;00m\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#W4sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m     torch_dtype\u001b[39m=\u001b[39;49mtorch\u001b[39m.\u001b[39;49mfloat16,\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#W4sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m     device_map\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mauto\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#W4sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m     load_in_8bit\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#W4sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m     \u001b[39m# trust_remote_code=True,\u001b[39;49;00m\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#W4sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m     \u001b[39m# low_cpu_mem_usage=True,\u001b[39;49;00m\n\u001b[0;32m---> <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#W4sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m )\u001b[39m.\u001b[39;49mto(\u001b[39m\"\u001b[39;49m\u001b[39mcuda:0\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n","File \u001b[0;32m~/anaconda3/envs/llama/lib/python3.10/site-packages/accelerate/big_modeling.py:416\u001b[0m, in \u001b[0;36mdispatch_model.<locals>.add_warning.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    414\u001b[0m     \u001b[39mif\u001b[39;00m param\u001b[39m.\u001b[39mdevice \u001b[39m==\u001b[39m torch\u001b[39m.\u001b[39mdevice(\u001b[39m\"\u001b[39m\u001b[39mmeta\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m    415\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mYou can\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt move a model that has some modules offloaded to cpu or disk.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 416\u001b[0m \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n","File \u001b[0;32m~/anaconda3/envs/llama/lib/python3.10/site-packages/transformers/modeling_utils.py:2170\u001b[0m, in \u001b[0;36mPreTrainedModel.to\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2166\u001b[0m \u001b[39m@wraps\u001b[39m(torch\u001b[39m.\u001b[39mnn\u001b[39m.\u001b[39mModule\u001b[39m.\u001b[39mto)\n\u001b[1;32m   2167\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mto\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m   2168\u001b[0m     \u001b[39m# Checks if the model has been loaded in 8-bit\u001b[39;00m\n\u001b[1;32m   2169\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mquantization_method\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m) \u001b[39m==\u001b[39m QuantizationMethod\u001b[39m.\u001b[39mBITS_AND_BYTES:\n\u001b[0;32m-> 2170\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m   2171\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39m`.to` is not supported for `4-bit` or `8-bit` bitsandbytes models. Please use the model as it is, since the\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   2172\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39m model has already been set to the correct devices and casted to the correct `dtype`.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   2173\u001b[0m         )\n\u001b[1;32m   2174\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   2175\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39mto(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n","\u001b[0;31mValueError\u001b[0m: `.to` is not supported for `4-bit` or `8-bit` bitsandbytes models. Please use the model as it is, since the model has already been set to the correct devices and casted to the correct `dtype`."]}],"source":["base_model_name_or_path = '''openlm-research/open_llama_3b_v2'''\n","\"\"\"VMware/open-llama-13b-open-instruct\"\"\"\n","\n","tokenizer = AutoTokenizer.from_pretrained(base_model_name_or_path, padding_side=\"right\", use_fast=False)\n","# tokenizer.pad_token = tokenizer.eos_token\n","\n","base_model = AutoModelForCausalLM.from_pretrained(\n","    base_model_name_or_path,\n","    # load_in_4bit=True,\n","    torch_dtype=torch.float16,\n","    # device_map=\"auto\",\n","    # load_in_8bit=True,\n","    # trust_remote_code=True,\n","    # low_cpu_mem_usage=True,\n",").to(\"cuda:0\")"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-07-19T19:59:44.808375Z","iopub.status.busy":"2023-07-19T19:59:44.808010Z","iopub.status.idle":"2023-07-19T20:00:04.653858Z","shell.execute_reply":"2023-07-19T20:00:04.652843Z","shell.execute_reply.started":"2023-07-19T19:59:44.808336Z"},"trusted":true},"outputs":[{"ename":"AttributeError","evalue":"'function' object has no attribute 'named_modules'","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","\u001b[1;32m/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb Cell 6\u001b[0m line \u001b[0;36m5\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#W5sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m lora_model_name_or_path \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m./open-llama-3bv2-lora-cabra-adapter-120steps\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#W5sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mGustrd/open-llama-13b-lora-cabra-adapter\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#W5sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m model \u001b[39m=\u001b[39m PeftModel\u001b[39m.\u001b[39;49mfrom_pretrained(model, lora_model_name_or_path)\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#W5sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m model\u001b[39m.\u001b[39meval()\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#W5sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m \u001b[39mif\u001b[39;00m torch\u001b[39m.\u001b[39m__version__ \u001b[39m>\u001b[39m\u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m2\u001b[39m\u001b[39m\"\u001b[39m:\n","File \u001b[0;32m~/anaconda3/envs/llama/lib/python3.10/site-packages/peft/peft_model.py:292\u001b[0m, in \u001b[0;36mPeftModel.from_pretrained\u001b[0;34m(cls, model, model_id, adapter_name, is_trainable, config, **kwargs)\u001b[0m\n\u001b[1;32m    290\u001b[0m     model \u001b[39m=\u001b[39m \u001b[39mcls\u001b[39m(model, config, adapter_name)\n\u001b[1;32m    291\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 292\u001b[0m     model \u001b[39m=\u001b[39m MODEL_TYPE_TO_PEFT_MODEL_MAPPING[config\u001b[39m.\u001b[39;49mtask_type](model, config, adapter_name)\n\u001b[1;32m    293\u001b[0m model\u001b[39m.\u001b[39mload_adapter(model_id, adapter_name, is_trainable\u001b[39m=\u001b[39mis_trainable, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m    294\u001b[0m \u001b[39mreturn\u001b[39;00m model\n","File \u001b[0;32m~/anaconda3/envs/llama/lib/python3.10/site-packages/peft/peft_model.py:916\u001b[0m, in \u001b[0;36mPeftModelForCausalLM.__init__\u001b[0;34m(self, model, peft_config, adapter_name)\u001b[0m\n\u001b[1;32m    915\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\u001b[39mself\u001b[39m, model, peft_config: PeftConfig, adapter_name\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mdefault\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m--> 916\u001b[0m     \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__init__\u001b[39;49m(model, peft_config, adapter_name)\n\u001b[1;32m    917\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbase_model_prepare_inputs_for_generation \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbase_model\u001b[39m.\u001b[39mprepare_inputs_for_generation\n","File \u001b[0;32m~/anaconda3/envs/llama/lib/python3.10/site-packages/peft/peft_model.py:116\u001b[0m, in \u001b[0;36mPeftModel.__init__\u001b[0;34m(self, model, peft_config, adapter_name)\u001b[0m\n\u001b[1;32m    114\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_peft_config \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    115\u001b[0m     \u001b[39mcls\u001b[39m \u001b[39m=\u001b[39m PEFT_TYPE_TO_MODEL_MAPPING[peft_config\u001b[39m.\u001b[39mpeft_type]\n\u001b[0;32m--> 116\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbase_model \u001b[39m=\u001b[39m \u001b[39mcls\u001b[39;49m(model, {adapter_name: peft_config}, adapter_name)\n\u001b[1;32m    117\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mset_additional_trainable_modules(peft_config, adapter_name)\n\u001b[1;32m    119\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconfig \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbase_model, \u001b[39m\"\u001b[39m\u001b[39mconfig\u001b[39m\u001b[39m\"\u001b[39m, {\u001b[39m\"\u001b[39m\u001b[39mmodel_type\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39mcustom\u001b[39m\u001b[39m\"\u001b[39m})\n","File \u001b[0;32m~/anaconda3/envs/llama/lib/python3.10/site-packages/peft/tuners/lora/model.py:110\u001b[0m, in \u001b[0;36mLoraModel.__init__\u001b[0;34m(self, model, config, adapter_name)\u001b[0m\n\u001b[1;32m    109\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\u001b[39mself\u001b[39m, model, config, adapter_name) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 110\u001b[0m     \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__init__\u001b[39;49m(model, config, adapter_name)\n","File \u001b[0;32m~/anaconda3/envs/llama/lib/python3.10/site-packages/peft/tuners/tuners_utils.py:90\u001b[0m, in \u001b[0;36mBaseTuner.__init__\u001b[0;34m(self, model, peft_config, adapter_name)\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mhasattr\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mconfig\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m     88\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconfig \u001b[39m=\u001b[39m {\u001b[39m\"\u001b[39m\u001b[39mmodel_type\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39mcustom\u001b[39m\u001b[39m\"\u001b[39m}\n\u001b[0;32m---> 90\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49minject_adapter(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmodel, adapter_name)\n\u001b[1;32m     92\u001b[0m \u001b[39m# Copy the peft_config in the injected model.\u001b[39;00m\n\u001b[1;32m     93\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel\u001b[39m.\u001b[39mpeft_config \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpeft_config\n","File \u001b[0;32m~/anaconda3/envs/llama/lib/python3.10/site-packages/peft/tuners/tuners_utils.py:208\u001b[0m, in \u001b[0;36mBaseTuner.inject_adapter\u001b[0;34m(self, model, adapter_name)\u001b[0m\n\u001b[1;32m    205\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_new_adapter_config(peft_config)\n\u001b[1;32m    207\u001b[0m is_target_modules_in_base_model \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[0;32m--> 208\u001b[0m key_list \u001b[39m=\u001b[39m [key \u001b[39mfor\u001b[39;00m key, _ \u001b[39min\u001b[39;00m model\u001b[39m.\u001b[39;49mnamed_modules()]\n\u001b[1;32m    210\u001b[0m model_config \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(model, \u001b[39m\"\u001b[39m\u001b[39mconfig\u001b[39m\u001b[39m\"\u001b[39m, {\u001b[39m\"\u001b[39m\u001b[39mmodel_type\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39mcustom\u001b[39m\u001b[39m\"\u001b[39m})\n\u001b[1;32m    211\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(model_config, \u001b[39m\"\u001b[39m\u001b[39mto_dict\u001b[39m\u001b[39m\"\u001b[39m):\n","\u001b[0;31mAttributeError\u001b[0m: 'function' object has no attribute 'named_modules'"]}],"source":["# Load the LoRA adapter\n","\n","lora_model_name_or_path = \"./open-llama-3bv2-lora-cabra-adapter-120steps\"\n","\"Gustrd/open-llama-13b-lora-cabra-adapter\"\n","model = PeftModel.from_pretrained(base_model, lora_model_name_or_path)\n","\n","model.eval()\n","\n","if torch.__version__ >= \"2\":\n","    model = torch.compile(model)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-07-19T20:00:04.655727Z","iopub.status.busy":"2023-07-19T20:00:04.655336Z","iopub.status.idle":"2023-07-19T20:00:04.663159Z","shell.execute_reply":"2023-07-19T20:00:04.661256Z","shell.execute_reply.started":"2023-07-19T20:00:04.655690Z"},"trusted":true},"outputs":[],"source":["def generate_prompt(instruction, input=None):\n","    if input:\n","        return f\"\"\"Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido.\n","\n","### Instrução:\n","{instruction}\n","\n","### Entrada:\n","{input}\n","\n","### Resposta:\"\"\"\n","    else:\n","        return f\"\"\"Abaixo está uma instrução que descreve uma tarefa. Escreva uma resposta que complete adequadamente o pedido.\n","\n","### Instrução:\n","{instruction}\n","\n","### Resposta:\"\"\""]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-07-19T20:09:38.275204Z","iopub.status.busy":"2023-07-19T20:09:38.274752Z","iopub.status.idle":"2023-07-19T20:09:38.283810Z","shell.execute_reply":"2023-07-19T20:09:38.282663Z","shell.execute_reply.started":"2023-07-19T20:09:38.275168Z"},"id":"FCKf170Mkhmr","trusted":true},"outputs":[],"source":["# def instruct(\n","#     instruction ,\n","#     temperature=0.3,\n","#     top_p=0.9,\n","#     max_new_tokens=128,\n","#     input=None,\n","#     **kwargs,\n","# ):\n","#     prompt = generate_prompt(instruction, input)\n","#     input_ids = tokenizer(prompt, return_tensors=\"pt\")[\"input_ids\"].to(model.device)\n","#     input_length = input_ids.shape[1]\n","\n","#     generated_outputs = model.generate(\n","#         input_ids=input_ids,\n","#         generation_config=GenerationConfig(\n","#             temperature=temperature,\n","#             top_p=top_p,\n","#             do_sample=temperature > 0.0,\n","#             max_new_tokens=max_new_tokens,\n","#             **kwargs,\n","#           ),\n","#         return_dict_in_generate=True,\n","#     )\n","#     generated_tokens = generated_outputs.sequences[0, input_length:]\n","#     generated_text = tokenizer.decode(generated_tokens, skip_special_tokens=True)\n","#     return generated_text\n","# 120steps, temp=0.5, top_p=0.90, ngram_size=2, do_sample=True\n","generation_config=GenerationConfig(\\\n","            temperature=0.5,\n","            top_p=0.90,\n","            do_sample=0.3 > 0.0,\n","            max_new_tokens=70,\n","            no_repeat_ngram_size=2,\n","          )\n","def evaluate(instruction, input=None):\n","    prompt = generate_prompt(instruction, input)\n","    inputs = tokenizer(prompt, return_tensors=\"pt\")\n","    input_ids = inputs[\"input_ids\"].cuda()\n","    generation_output = model.generate(\\\n","        input_ids=input_ids,\n","        generation_config=generation_config,\n","        return_dict_in_generate=True,\n","        output_scores=True,\n","        # max_new_tokens=256\n","    )\n","    print(\"instruction:\", instruction)\n","    print(\"input:\", input)\n","    for s in generation_output.sequences:\n","        output = tokenizer.decode(s)\n","        print(\"Resposta:\", output.split(\"### Resposta:\")[1].strip())"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-07-19T20:02:15.845265Z","iopub.status.busy":"2023-07-19T20:02:15.844461Z","iopub.status.idle":"2023-07-19T20:02:59.098821Z","shell.execute_reply":"2023-07-19T20:02:59.097542Z","shell.execute_reply.started":"2023-07-19T20:02:15.845217Z"},"id":"E2s4Jvkoo_sK","trusted":true},"outputs":[{"ename":"AttributeError","evalue":"'function' object has no attribute 'generate'","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","\u001b[1;32m/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb Cell 9\u001b[0m line \u001b[0;36m2\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#X11sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# instruct(\"Invente uma desculpa criativa pra dizer que não preciso ir à festa.\")\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#X11sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m evaluate(\u001b[39m\"\u001b[39;49m\u001b[39mInvente uma desculpa engraçada para recusar um convite para uma festa.\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#X11sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39m# \"Gustrd/open-llama-13b-lora-cabra-adapter\":\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#X11sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m     \u001b[39m# '\\nEu estou com uma doença.\\nEu estou comum.\\nEu estou com uma reunião de trabalho.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\n'\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#X11sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#X11sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m     \u001b[39m###temp:0.4, top_p=0.85, no_repeat_ngram_size=2\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#X11sZmlsZQ%3D%3D?line=16'>17</a>\u001b[0m         \u001b[39m#Resposta: \"É um prazer ter a oportunidade de ir a festas, mas estou muito ocupado agora. Se você quer, pode me mandar uma cerveja.\"\u001b[39;00m\n","\u001b[1;32m/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb Cell 9\u001b[0m line \u001b[0;36m3\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#X11sZmlsZQ%3D%3D?line=35'>36</a>\u001b[0m inputs \u001b[39m=\u001b[39m tokenizer(prompt, return_tensors\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mpt\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#X11sZmlsZQ%3D%3D?line=36'>37</a>\u001b[0m input_ids \u001b[39m=\u001b[39m inputs[\u001b[39m\"\u001b[39m\u001b[39minput_ids\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m.\u001b[39mcuda()\n\u001b[0;32m---> <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#X11sZmlsZQ%3D%3D?line=37'>38</a>\u001b[0m generation_output \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39;49mgenerate(\\\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#X11sZmlsZQ%3D%3D?line=38'>39</a>\u001b[0m     input_ids\u001b[39m=\u001b[39minput_ids,\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#X11sZmlsZQ%3D%3D?line=39'>40</a>\u001b[0m     generation_config\u001b[39m=\u001b[39mgeneration_config,\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#X11sZmlsZQ%3D%3D?line=40'>41</a>\u001b[0m     return_dict_in_generate\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#X11sZmlsZQ%3D%3D?line=41'>42</a>\u001b[0m     output_scores\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#X11sZmlsZQ%3D%3D?line=42'>43</a>\u001b[0m     \u001b[39m# max_new_tokens=256\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#X11sZmlsZQ%3D%3D?line=43'>44</a>\u001b[0m )\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#X11sZmlsZQ%3D%3D?line=44'>45</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39minstruction:\u001b[39m\u001b[39m\"\u001b[39m, instruction)\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/r/Documentos/LLAMA/cabra/inference-cabra-kaggle.ipynb#X11sZmlsZQ%3D%3D?line=45'>46</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39minput:\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39minput\u001b[39m)\n","\u001b[0;31mAttributeError\u001b[0m: 'function' object has no attribute 'generate'"]}],"source":["# instruct(\"Invente uma desculpa criativa pra dizer que não preciso ir à festa.\")\n","evaluate(\"Invente uma desculpa engraçada para recusar um convite para uma festa.\")\n","# \"Gustrd/open-llama-13b-lora-cabra-adapter\":\n","    # '\\nEu estou com uma doença.\\nEu estou comum.\\nEu estou com uma reunião de trabalho.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\nEu estou com uma coisa em casa.\\n'\n","\n","# cabra-3bv2-120steps:\n","    ###temp:0.5\n","        #Resposta: \"Olá, eu não sou muito entusiasmado com este tipo de festa, mas se você quiser, eu poderia ir.\" \n","\n","    ###temp:0.4, top_p=0.65\n","        #Resposta: \"Não preciso ir à festa. Não estou bem. Estou com um problema de saúde. Estou com um problema de saúde. Estou com um problema de saúde. Estou com um problema de saúde.\n","\n","    ###temp:0.4, top_p=0.75, no_repeat_ngram_size=2\n","        #Resposta: \"Não posso ir, eu estou trabalhando.\"\n","        \n","    ###temp:0.4, top_p=0.85, no_repeat_ngram_size=2\n","        #Resposta: \"É um prazer ter a oportunidade de ir a festas, mas estou muito ocupado agora. Se você quer, pode me mandar uma cerveja.\"\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":53},"execution":{"iopub.execute_input":"2023-07-19T20:04:54.378479Z","iopub.status.busy":"2023-07-19T20:04:54.377891Z","iopub.status.idle":"2023-07-19T20:05:33.854815Z","shell.execute_reply":"2023-07-19T20:05:33.853646Z","shell.execute_reply.started":"2023-07-19T20:04:54.378442Z"},"id":"FIKwEszPpR_j","outputId":"aa41d7d3-95a4-4efc-c345-4c694813f273","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["instruction: Como posso começar a carreira como cientista de dados? Escreva na forma de lista.\n","input: None\n","Resposta: - Entender o que é um código de barras\n","o que são dadas de bateria\n","como fazer uma criação de base de dado\n","quando e onde é que você precisa de um banco de conhecimento\n","que tipo d'é que\n"]}],"source":["# instruct(\"Como posso começar a carreira como cientista de dados? Escreva na forma de lista.\")\n","evaluate(\"Como posso começar a carreira como cientista de dados? Escreva na forma de lista.\")\n","# \"Gustrd/open-llama-13b-lora-cabra-adapter\":\n","    # '\\n1. Obtenha um grau de graduação em ciência da computação ou ciência da computação.\\n2. Obtenha um grau de mestrado em ciência da computação ou ciência da computação.\\n3. Obtenha experiência de trabalho em um campo de ciência da computação.\\n4. Escreva um currículo de formação de carreira.\\n5. Comece a pesquisar em empresas de tecnologia.\\n6. Comece a pesquisar em empresas de negócios.\\n7. Comece a pesquisar em empresas de saúde.\\n8. Comece a pesquisar em empresas de serviços públicos.\\n9. Comece a pesquisar em empresas de serviços financeiros.\\n10. Comece a pesquisar em empresas de serviços de consultoria.\\n11. Comece a pesquisar em'\n","# cabra-3bv2-120steps:\n","    #Resposta: Você pode começar a carreira como cientista de dados com um diploma de graduação ou um mestrado em ciência de dados. Você pode também começar a carreira como cientista de dados com um bacharelado em ciência de dados ou um mestrado em ciência de dados.\n","\n","    #     Resposta: 1. Estudar na faculdade\n","    # 2. Aprender a programar\n","    # 3. Trabalhar em um laboratório\n","    # 4. Seguir um curso de ciência de dado\n","    # 5. Começo uma carrera como engenheiro de software\n","    # 6. C"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-07-19T20:11:37.975419Z","iopub.status.busy":"2023-07-19T20:11:37.974999Z","iopub.status.idle":"2023-07-19T20:11:57.930270Z","shell.execute_reply":"2023-07-19T20:11:57.928981Z","shell.execute_reply.started":"2023-07-19T20:11:37.975388Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["instruction: Traduza o seguinte texto do inglês para o português do Brasil.\n","input: The dog is white.\n","Resposta: O cão é branco. 🐶\n","</s>\n"]}],"source":["# instruct(\"Traduza o seguinte texto do inglês para o português do Brasil.\", input=\"The dog is white.\")\n","evaluate(\"Traduza o seguinte texto do inglês para o português do Brasil.\", input=\"The dog is white.\")\n","#\"Gustrd/open-llama-13b-lora-cabra-adapter\":\n","    # 13b'\\nO cão é branco.\\n\\n### Entrada:\\nO cão é branco.\\n\\n### Resposta:\\nO cão é branco.\\n\\n### Entrada:\\nO cão é branco.\\n\\n### Resposta:\\nO cão é branco.\\n\\n### Entrada:\\nO cão é branco.\\n\\n### Resposta:\\nO cão é branco.\\n\\n### Entrada:\\nO cão é branco.\\n\\n### Resposta:\\nO cão'\n","# cabra-3bv2-120steps:\n","    # Resposta: O cão é branco."]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[]},"kernelspec":{"display_name":"Python 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