{"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 . 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 4\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 5\u001b[0m \u001b[39m# tokenizer.pad_token = tokenizer.eos_token\u001b[39;00m\n\u001b[1;32m 7\u001b[0m base_model \u001b[39m=\u001b[39m AutoModelForCausalLM\u001b[39m.\u001b[39;49mfrom_pretrained(\n\u001b[1;32m 8\u001b[0m base_model_name_or_path,\n\u001b[1;32m 9\u001b[0m \u001b[39m# load_in_4bit=True,\u001b[39;49;00m\n\u001b[1;32m 10\u001b[0m torch_dtype\u001b[39m=\u001b[39;49mtorch\u001b[39m.\u001b[39;49mfloat16,\n\u001b[1;32m 11\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 12\u001b[0m load_in_8bit\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,\n\u001b[1;32m 13\u001b[0m \u001b[39m# trust_remote_code=True,\u001b[39;49;00m\n\u001b[1;32m 14\u001b[0m \u001b[39m# low_cpu_mem_usage=True,\u001b[39;49;00m\n\u001b[0;32m---> 15\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..add_warning..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 3\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 4\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mGustrd/open-llama-13b-lora-cabra-adapter\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m----> 5\u001b[0m model \u001b[39m=\u001b[39m PeftModel\u001b[39m.\u001b[39;49mfrom_pretrained(model, lora_model_name_or_path)\n\u001b[1;32m 7\u001b[0m model\u001b[39m.\u001b[39meval()\n\u001b[1;32m 9\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 1\u001b[0m \u001b[39m# instruct(\"Invente uma desculpa criativa pra dizer que não preciso ir à festa.\")\u001b[39;00m\n\u001b[0;32m----> 2\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 3\u001b[0m \u001b[39m# \"Gustrd/open-llama-13b-lora-cabra-adapter\":\u001b[39;00m\n\u001b[1;32m 4\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 5\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[39m###temp:0.4, top_p=0.85, no_repeat_ngram_size=2\u001b[39;00m\n\u001b[1;32m 17\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 36\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 37\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---> 38\u001b[0m generation_output \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39;49mgenerate(\\\n\u001b[1;32m 39\u001b[0m input_ids\u001b[39m=\u001b[39minput_ids,\n\u001b[1;32m 40\u001b[0m generation_config\u001b[39m=\u001b[39mgeneration_config,\n\u001b[1;32m 41\u001b[0m return_dict_in_generate\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n\u001b[1;32m 42\u001b[0m output_scores\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n\u001b[1;32m 43\u001b[0m \u001b[39m# max_new_tokens=256\u001b[39;00m\n\u001b[1;32m 44\u001b[0m )\n\u001b[1;32m 45\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39minstruction:\u001b[39m\u001b[39m\"\u001b[39m, instruction)\n\u001b[1;32m 46\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. 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