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docker-pycreds-0.4.0 einops-0.6.1 fastapi-0.97.0 ffmpy-0.3.0 frozenlist-1.3.3 gitdb-4.0.10 gradio-3.23.0 h11-0.14.0 httpcore-0.17.2 httpx-0.24.1 huggingface-hub-0.15.1 kollava-0.1.0 linkify-it-py-2.0.2 markdown2-2.4.8 mdit-py-plugins-0.3.3 multidict-6.0.4 orjson-3.9.1 pathtools-0.1.2 pydub-0.25.1 python-multipart-0.0.6 semantic-version-2.10.0 sentencepiece-0.1.99 sentry-sdk-1.25.1 setproctitle-1.3.2 smmap-5.0.0 starlette-0.27.0 svgwrite-1.4.3 tokenizers-0.12.1 transformers-4.28.0.dev0 uc-micro-py-1.0.2 uvicorn-0.22.0 wandb-0.15.4 wavedrom-2.0.3.post3 websockets-11.0.3 yarl-1.9.2\n"]}]},{"cell_type":"code","source":["from transformers import AutoTokenizer, AutoModelForCausalLM\n","import torch\n","import os\n","from llava.conversation import conv_templates, SeparatorStyle\n","from llava.utils import disable_torch_init\n","from transformers import CLIPVisionModel, CLIPImageProcessor, StoppingCriteria\n","from llava.model import *\n","from llava.model.utils import KeywordsStoppingCriteria\n","\n","from PIL import Image\n","\n","import os\n","import requests\n","from PIL import Image\n","from io import BytesIO\n","\n","\n","DEFAULT_IMAGE_TOKEN = \"<image>\"\n","DEFAULT_IMAGE_PATCH_TOKEN = \"<im_patch>\"\n","DEFAULT_IM_START_TOKEN = \"<im_start>\"\n","DEFAULT_IM_END_TOKEN = \"<im_end>\""],"metadata":{"id":"3wLIbEIjVIPc"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def load_image(image_file):\n","    if image_file.startswith('http') or image_file.startswith('https'):\n","        response = requests.get(image_file)\n","        image = Image.open(BytesIO(response.content)).convert('RGB')\n","    else:\n","        image = Image.open(image_file).convert('RGB')\n","    return image"],"metadata":{"id":"nPPA-XjacKCl"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model_name = 'tabtoyou/KoLLaVA-KoVicuna-7b'\n","\n","# Load Model\n","tokenizer = AutoTokenizer.from_pretrained(model_name)\n","model = LlavaLlamaForCausalLM.from_pretrained(model_name, low_cpu_mem_usage=True, torch_dtype=torch.float16, use_cache=True).cuda()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":537,"referenced_widgets":["d3c0d77e52c841c5b3645a39d9200498","0a83286b39c842d4a5dfc9d4e912ebb3","f4d3468bb2c24e1898079865c2a52a39","309d3180777045cfbfcdc7c9c4deaed9","c3f572b2fc1c4f72836f1d1c476170d5","26df516b07464965840e4d3dd7d1c4f1","0bbbd17e356e4784bc27e4d3cf7aae49","5797f22e19ff48de9ed0b373fd5cb322","953f009a94f24384985468a1f7a1730f","660c5825c89a4893a46bef423c15997e","ceab2b30ed904455b8e722a07c6164c5","71c0fd75299643bfa760f9bac038884c","fb6c95e74604438084d876e2f8fae250","cb306e40e3dc4a178df00abb9ea54083","e6bd7f4962594cd1a3e8a99d52191fc8","34638afa52b44b26bb6fe2772ea42bc9","c7096db2ff00496a863ba9fcb585168a","7e4230d6776942b3873893d4c7e7e0ca","798f2e8a0dbc4476847494ca61e83a34","79db90f686394ff1a2e319e33d4fda55","86547c089f8b4153887c170fb5957324","38060f4a55ec41f49301aa26d0fafbe3","e60a66224d1a4a74b879c04baad9eb30","34fc5b796921472d8f014db57bee3979","30d046fad3e646fabd20d316e754c331","8376ab3d88f947e384da157292060cb7","50c65c285c864115b77ec7e98d014eec","c3bf50bc91b5499bbe209bd6756f3808","37183d44e428493687bbbdea9197218b","b18c642fdc614ea7a0f6435d771c5554","6f1ee5a581d344a8a5ed18c33b2bebc7","7ddc6a11b9684f2f93d6cc8b401b5906","4a72ea8a8ce44bb49996e20d81819fd7","f6c6b1bc7cef4ea480e1fbddeff9f2c4","3215bac0220b4e64973f5c57427d8de9","4666ae9768c44f738381bf636d8e5e6b","26b6db6265a94777a352e72a8e68bba4","23eb9e87a7be48d393149e89c3728cd4","fa71a91a62b7487abb6a94be3ce70656","53893107be4b48e8bda18284df1b4380","367bd6bd66194571ad54282a07a69e46","b5d8ea3a7f744c1592c6b5a46bb740a7","7170791c06d344fa924b517ce3624055","f98a59f955574ce0b6fbffdf5e627a52","e9e3a87169f54d8c876150ed3032000e","eaae7e8e67f34314ae2756c379ff7418","f36a65cfd1404978a9b3d3638562d57e","74effb1dc5e54a9c8eb5c11529192a0f","f41e0c3bfbb346308a9aa3e28e14b4b4","8dbb4f38217f47de99ef5f68f66783f8","67346b09ebdf4bb3bb08c252a01f9a7b","352d23ecee024d8c923059445bfcab43","d300a958c042418ca6b34e9bec029a97","790c894a16ac420aa5e0875e4c383b94","3c671d76d47c4a8793ba0d15da91f66f","8137eeadc9a14ddcaa8e2248e19433ae","b01d03a083444d7090df06d771baa01e","77feee58b91a473cbb369d3cbda15e9b","1738c401aabf4210a7063c763c33baa9","f6a1c75d071e4df4b38cd91956561954","cd1544d742b34307ae6756dc80122120","9554e1abedf744f3be629376f860cb7a","71f05442ab744ac29dee70cc4ff093bd","25d4ddeed4914dbca6b0c63e684d137e","fc7adab0daa749139b9ae5f4df7be707","7f15070790494194bea6d287e693374b","4829867999d9428d977d89626729869d","7e4e490c8b8c44d09ce0b075ea82554c","7fed0ea89cd046158ae19be633f2c593","0f3dc5ae71c04b71aca2c7a2368045f0","ef62d1b9e5cd414ea10d3bd5aa2c5aa6","5f61199c50f246cbb333f47e8d483cbb","2c04909fdfd74e6783e5f958b63e9767","895c344702984f8eb4e157d7a2a05fd8","9ab1614e4567456d9161145560634095","723a4ca995bb48efbf8445cbdeac1e33","3a1ec24c2d084bbca8484bbd97472d1f","8ace04ea170545079cb8b5163dad55e3","f62e6c5c15c74384b0409e2939695ab5","d68ceb02f0d64d50882953d7664af229","e179e08c98ee47a0ba99b32cda7730e1","605952069f5448cf9c2213ffd2ed7695","cfff50b604eb43d090278d302975d7cc","2a474b806d3e4bfe9138878970ac39d9","8502a010d1ac404cb87bc5ef60c838ff","d149795c1bca4c918b08d639f08e0203","0fbfaec7386443e481b9b433f7bece05","d00a2eecd7da4d1693583622245b2b6b","2a1684c5c80d45d0b7be04ceee0ef50e","f424169881e44c98bbf27625b174d528","fc9e4fc9de2c40abb7be950d320bcd01","067468294ed64a11b70a04a229db54b3","3dbaa8fc3c8b4e39a7a9765649318192","3f477e2986ae4734a74b210562b881ee","6c2036e560c047c29fb030bd0fedca1a","bfc6f273dddb4d7894d5e02c6d8b7429","ee29f2b426d340a5bb8df2f9a3e6e1c2","afee08f2577f4031a20a42917990885d","db7c27850c7c42329a3f68cb9c2e4185","5b34ea55986745a0aaf95351a8812014","617cc120294b473a9e67c1d041a98461","e098ec07fb114623b3602ae0764ee172","6d84ee8d95ef4ed0be98508e738f903f","7428d1efe30f4e47809c81e3b1fc69cd","a6e362dbae49433d8d9a9b8763641aee","6545e402d03b481cbf7970cfbfc7d750","4ee761128e764185ab9ebee83a8b1ffb","450122b8687742c2b6b28a9196af232d","303ccc69818643bf8ea8052d40bac1a4","19225835148a487985eb465a6a1f73f0","a4b9589dd803455b8c2e5578f4a74df3","3d25d15314744f3db4100a035de02ffd","3266d2fd29c644a08d9a6cd11764003d","ad2f7533d2fc461ebb6cb7f567177ee9","8cc2e9179c134b9f8dd7025d9655c4e5","15c8157498f5409999618df5dfe496d1","6ce17ef0ac1b4338beb1ea4e26061abb","5719e21079aa4760b7c9f44cee32ed30","fd1f038eaaad429294168bc8936d9a3a","af7f8f8db4e047d1b1d1930e546902f9","4c57abb5911d46e593f943ba85a02fce","dbec3ea849be4d71a9a2248d3a2dec2b","720ebdeef2624cd988cf726a3b336ae1","b8aec6b8d9214094b83161cbb1d66b95","e862afdfa72f422ebffa6f580436beaa","af13b79d6b184f6ba74db7778fae924a","207e6ffbfc5d49f1b282bd2e9c8bf5e5","5aa370b366f444a3894b2ffffd972508","fa87063353ec47ce81bd60ed945c8324","8ff4b5c941394516a255967f054192ab","529d9c9f605c42a0957abf5e0d005b26","88310f93871b4f89a6373c020be5c88a","d6bcd8b946cc45878afca13fd2f12a80","ace03fdc7cdc4aac9d8d6dfc001d6545","13784693d5e34691a40a62592a946df0","cc0502acc7cd475faf34212a55f835a3","c25d2ec31566445db65ef5b160e0ffff","f3d14a1e752e44d6a02c8c5ccde1a9e9","ae92c064d96f446aac9221f2f7062df2","ba047be97bfb428c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'text_model.encoder.layers.9.mlp.fc1.bias', 'text_model.encoder.layers.9.layer_norm1.bias', 'text_model.encoder.layers.1.self_attn.k_proj.weight', 'text_model.final_layer_norm.bias', 'text_model.encoder.layers.6.self_attn.q_proj.weight', 'text_model.encoder.layers.7.self_attn.k_proj.weight', 'text_model.encoder.layers.2.self_attn.out_proj.weight', 'text_model.encoder.layers.3.self_attn.out_proj.weight', 'text_model.encoder.layers.9.self_attn.out_proj.weight', 'text_model.encoder.layers.3.self_attn.k_proj.weight', 'text_model.encoder.layers.4.self_attn.q_proj.bias', 'text_model.encoder.layers.0.mlp.fc2.bias', 'text_model.encoder.layers.10.self_attn.v_proj.bias', 'text_model.encoder.layers.9.mlp.fc1.weight', 'text_model.encoder.layers.11.self_attn.q_proj.weight', 'text_model.encoder.layers.10.mlp.fc2.weight', 'text_model.encoder.layers.1.mlp.fc1.weight', 'text_model.encoder.layers.4.mlp.fc2.weight', 'text_model.encoder.layers.5.mlp.fc2.bias', 'text_model.encoder.layers.1.mlp.fc2.weight', 'text_model.final_layer_norm.weight', 'text_model.encoder.layers.10.self_attn.k_proj.weight', 'text_model.encoder.layers.4.self_attn.q_proj.weight', 'text_model.encoder.layers.6.layer_norm1.bias', 'text_model.encoder.layers.8.self_attn.k_proj.bias', 'text_model.encoder.layers.9.self_attn.out_proj.bias', 'text_model.embeddings.position_ids', 'text_model.encoder.layers.0.self_attn.v_proj.weight', 'text_model.encoder.layers.9.mlp.fc2.bias', 'text_model.encoder.layers.6.self_attn.v_proj.weight', 'text_model.encoder.layers.4.mlp.fc2.bias', 'text_model.encoder.layers.7.self_attn.out_proj.weight', 'text_model.encoder.layers.10.layer_norm2.bias', 'text_model.encoder.layers.3.self_attn.v_proj.weight', 'text_model.encoder.layers.9.self_attn.q_proj.bias', 'text_model.encoder.layers.5.mlp.fc1.weight', 'text_model.encoder.layers.9.layer_norm1.weight', 'text_model.encoder.layers.10.self_attn.k_proj.bias', 'text_model.encoder.layers.2.layer_norm2.bias', 'text_model.encoder.layers.5.self_attn.v_proj.weight', 'text_model.encoder.layers.8.layer_norm1.bias', 'text_model.encoder.layers.3.self_attn.v_proj.bias', 'text_model.encoder.layers.7.mlp.fc1.bias', 'text_model.encoder.layers.5.self_attn.q_proj.weight', 'text_model.encoder.layers.0.layer_norm2.weight', 'text_model.encoder.layers.11.self_attn.q_proj.bias', 'text_model.encoder.layers.2.self_attn.v_proj.bias', 'text_model.encoder.layers.2.layer_norm1.bias', 'text_model.encoder.layers.11.layer_norm2.bias', 'text_model.encoder.layers.8.self_attn.q_proj.weight']\n","- This IS expected if you are initializing CLIPVisionModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing CLIPVisionModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"]},{"output_type":"display_data","data":{"text/plain":["Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d6bcd8b946cc45878afca13fd2f12a80"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)neration_config.json:   0%|          | 0.00/137 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8d5abaf2fd3442a49c105304c0d19c5b"}},"metadata":{}}]},{"cell_type":"code","source":["image_processor = CLIPImageProcessor.from_pretrained(model.config.mm_vision_tower, torch_dtype=torch.float16)\n","\n","mm_use_im_start_end = getattr(model.config, \"mm_use_im_start_end\", False)\n","tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)\n","tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)\n","\n","vision_tower = model.get_model().vision_tower[0]\n","vision_tower = CLIPVisionModel.from_pretrained(vision_tower.config._name_or_path, torch_dtype=torch.float16, low_cpu_mem_usage=True).cuda()\n","model.get_model().vision_tower[0] = vision_tower\n","\n","vision_config = vision_tower.config\n","vision_config.im_patch_token = tokenizer.convert_tokens_to_ids([DEFAULT_IMAGE_PATCH_TOKEN])[0]\n","vision_config.use_im_start_end = mm_use_im_start_end\n","vision_config.im_start_token, vision_config.im_end_token = tokenizer.convert_tokens_to_ids([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN])\n","image_token_len = (vision_config.image_size // vision_config.patch_size) ** 2"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e4FN5mhMbqq6","executionInfo":{"status":"ok","timestamp":1686755086582,"user_tz":-540,"elapsed":12229,"user":{"displayName":"Taebaek Hwang","userId":"08413173915740517335"}},"outputId":"98c53a5c-7776-4af2-87fe-03fbe999072a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at openai/clip-vit-large-patch14 were not used when initializing CLIPVisionModel: ['text_model.encoder.layers.3.self_attn.k_proj.bias', 'text_model.encoder.layers.2.self_attn.q_proj.bias', 'text_model.encoder.layers.0.mlp.fc1.weight', 'text_model.encoder.layers.8.layer_norm2.bias', 'text_model.encoder.layers.8.self_attn.out_proj.bias', 'text_model.encoder.layers.4.self_attn.out_proj.weight', 'text_model.encoder.layers.7.self_attn.q_proj.weight', 'text_model.encoder.layers.7.self_attn.v_proj.weight', 'text_model.encoder.layers.3.layer_norm1.weight', 'text_model.encoder.layers.8.self_attn.k_proj.weight', 'text_model.encoder.layers.7.mlp.fc2.weight', 'text_model.encoder.layers.0.self_attn.q_proj.bias', 'text_model.encoder.layers.11.self_attn.k_proj.bias', 'text_model.encoder.layers.11.layer_norm2.weight', 'text_model.encoder.layers.4.self_attn.out_proj.bias', 'text_model.encoder.layers.10.layer_norm1.bias', 'text_model.encoder.layers.3.mlp.fc1.weight', 'text_model.encoder.layers.0.self_attn.out_proj.bias', 'text_model.encoder.layers.1.self_attn.out_proj.weight', 'text_model.encoder.layers.2.layer_norm2.weight', 'text_model.encoder.layers.2.self_attn.q_proj.weight', 'text_model.encoder.layers.2.self_attn.k_proj.bias', 'text_model.encoder.layers.11.mlp.fc1.bias', 'text_model.encoder.layers.5.layer_norm1.bias', 'text_model.encoder.layers.10.layer_norm2.weight', 'text_model.encoder.layers.2.self_attn.out_proj.bias', 'text_model.encoder.layers.10.self_attn.out_proj.weight', 'text_model.encoder.layers.6.mlp.fc2.weight', 'text_model.encoder.layers.10.layer_norm1.weight', 'text_model.encoder.layers.8.mlp.fc1.bias', 'text_model.encoder.layers.8.self_attn.v_proj.bias', 'text_model.encoder.layers.5.self_attn.q_proj.bias', 'text_model.encoder.layers.4.self_attn.k_proj.bias', 'text_model.encoder.layers.8.self_attn.out_proj.weight', 'text_model.encoder.layers.10.self_attn.q_proj.weight', 'text_model.encoder.layers.6.self_attn.v_proj.bias', 'text_model.encoder.layers.11.mlp.fc2.bias', 'text_model.encoder.layers.9.self_attn.q_proj.weight', 'text_model.encoder.layers.1.self_attn.v_proj.weight', 'text_model.encoder.layers.7.mlp.fc2.bias', 'text_model.encoder.layers.6.layer_norm2.bias', 'text_model.encoder.layers.4.self_attn.k_proj.weight', 'text_model.encoder.layers.6.self_attn.out_proj.weight', 'text_model.embeddings.token_embedding.weight', 'text_model.encoder.layers.1.layer_norm2.bias', 'text_model.encoder.layers.7.layer_norm2.weight', 'text_model.encoder.layers.8.self_attn.q_proj.bias', 'text_model.encoder.layers.4.layer_norm2.weight', 'text_model.encoder.layers.7.self_attn.q_proj.bias', 'logit_scale', 'text_model.encoder.layers.6.layer_norm2.weight', 'text_model.encoder.layers.11.self_attn.v_proj.weight', 'text_model.encoder.layers.5.self_attn.out_proj.weight', 'text_model.encoder.layers.6.self_attn.out_proj.bias', 'text_model.encoder.layers.6.self_attn.q_proj.bias', 'text_model.encoder.layers.2.mlp.fc2.weight', 'text_model.encoder.layers.9.self_attn.v_proj.bias', 'text_model.encoder.layers.9.mlp.fc2.weight', 'text_model.encoder.layers.0.self_attn.k_proj.weight', 'text_model.encoder.layers.8.layer_norm1.weight', 'text_model.encoder.layers.5.self_attn.k_proj.weight', 'text_model.encoder.layers.2.mlp.fc1.weight', 'text_model.encoder.layers.0.self_attn.k_proj.bias', 'text_model.encoder.layers.6.layer_norm1.weight', 'text_model.encoder.layers.8.self_attn.v_proj.weight', 'text_model.encoder.layers.2.self_attn.k_proj.weight', 'text_model.encoder.layers.2.self_attn.v_proj.weight', 'text_model.encoder.layers.4.layer_norm1.bias', 'text_model.encoder.layers.11.self_attn.k_proj.weight', 'text_model.encoder.layers.8.mlp.fc2.weight', 'text_model.encoder.layers.6.mlp.fc2.bias', 'text_model.encoder.layers.9.layer_norm2.bias', 'text_model.encoder.layers.10.mlp.fc1.bias', 'text_model.encoder.layers.8.mlp.fc1.weight', 'text_model.encoder.layers.0.mlp.fc1.bias', 'text_model.encoder.layers.11.self_attn.v_proj.bias', 'text_model.encoder.layers.0.self_attn.v_proj.bias', 'text_model.encoder.layers.1.mlp.fc2.bias', 'text_model.encoder.layers.10.self_attn.q_proj.bias', 'text_model.encoder.layers.3.layer_norm2.weight', 'text_model.encoder.layers.8.mlp.fc2.bias', 'text_model.encoder.layers.3.mlp.fc2.weight', 'text_model.encoder.layers.4.self_attn.v_proj.weight', 'text_model.encoder.layers.1.layer_norm2.weight', 'text_model.encoder.layers.7.layer_norm2.bias', 'text_model.encoder.layers.5.layer_norm2.bias', 'text_model.encoder.layers.11.self_attn.out_proj.bias', 'text_model.encoder.layers.10.mlp.fc2.bias', 'text_model.encoder.layers.9.layer_norm2.weight', 'visual_projection.weight', 'text_model.encoder.layers.5.mlp.fc2.weight', 'text_model.encoder.layers.10.self_attn.out_proj.bias', 'text_model.encoder.layers.11.mlp.fc1.weight', 'text_model.encoder.layers.6.mlp.fc1.weight', 'text_model.encoder.layers.11.layer_norm1.bias', 'text_model.encoder.layers.11.self_attn.out_proj.weight', 'text_model.encoder.layers.1.layer_norm1.weight', 'text_model.encoder.layers.4.mlp.fc1.weight', 'text_model.encoder.layers.1.self_attn.k_proj.bias', 'text_model.encoder.layers.6.self_attn.k_proj.bias', 'text_model.encoder.layers.4.layer_norm1.weight', 'text_model.encoder.layers.5.self_attn.v_proj.bias', 'text_model.encoder.layers.9.self_attn.k_proj.weight', 'text_model.encoder.layers.11.mlp.fc2.weight', 'text_model.encoder.layers.6.self_attn.k_proj.weight', 'text_model.encoder.layers.5.layer_norm2.weight', 'text_model.encoder.layers.9.self_attn.k_proj.bias', 'text_model.encoder.layers.3.layer_norm1.bias', 'text_model.encoder.layers.3.self_attn.q_proj.weight', 'text_model.encoder.layers.5.layer_norm1.weight', 'text_model.encoder.layers.3.mlp.fc1.bias', 'text_model.encoder.layers.4.self_attn.v_proj.bias', 'text_model.encoder.layers.4.mlp.fc1.bias', 'text_model.encoder.layers.0.self_attn.out_proj.weight', 'text_model.encoder.layers.7.self_attn.v_proj.bias', 'text_model.encoder.layers.3.mlp.fc2.bias', 'text_model.encoder.layers.0.layer_norm1.bias', 'text_model.encoder.layers.1.self_attn.out_proj.bias', 'text_model.encoder.layers.0.layer_norm1.weight', 'text_model.encoder.layers.3.layer_norm2.bias', 'text_model.encoder.layers.10.mlp.fc1.weight', 'text_model.encoder.layers.1.self_attn.q_proj.bias', 'text_model.encoder.layers.2.mlp.fc1.bias', 'text_model.encoder.layers.2.layer_norm1.weight', 'text_model.encoder.layers.0.self_attn.q_proj.weight', 'text_model.encoder.layers.0.mlp.fc2.weight', 'text_model.encoder.layers.11.layer_norm1.weight', 'text_model.encoder.layers.5.self_attn.k_proj.bias', 'text_model.encoder.layers.8.layer_norm2.weight', 'text_model.encoder.layers.2.mlp.fc2.bias', 'text_model.encoder.layers.9.self_attn.v_proj.weight', 'text_model.encoder.layers.1.mlp.fc1.bias', 'text_model.encoder.layers.3.self_attn.q_proj.bias', 'text_model.encoder.layers.7.layer_norm1.bias', 'text_model.encoder.layers.10.self_attn.v_proj.weight', 'text_model.encoder.layers.6.mlp.fc1.bias', 'text_projection.weight', 'text_model.encoder.layers.1.self_attn.q_proj.weight', 'text_model.encoder.layers.3.self_attn.out_proj.bias', 'text_model.encoder.layers.1.self_attn.v_proj.bias', 'text_model.encoder.layers.4.layer_norm2.bias', 'text_model.encoder.layers.7.self_attn.out_proj.bias', 'text_model.encoder.layers.1.layer_norm1.bias', 'text_model.encoder.layers.7.mlp.fc1.weight', 'text_model.embeddings.position_embedding.weight', 'text_model.encoder.layers.7.layer_norm1.weight', 'text_model.encoder.layers.7.self_attn.k_proj.bias', 'text_model.encoder.layers.5.mlp.fc1.bias', 'text_model.encoder.layers.5.self_attn.out_proj.bias', 'text_model.encoder.layers.0.layer_norm2.bias', 'text_model.encoder.layers.9.mlp.fc1.bias', 'text_model.encoder.layers.9.layer_norm1.bias', 'text_model.encoder.layers.1.self_attn.k_proj.weight', 'text_model.final_layer_norm.bias', 'text_model.encoder.layers.6.self_attn.q_proj.weight', 'text_model.encoder.layers.7.self_attn.k_proj.weight', 'text_model.encoder.layers.2.self_attn.out_proj.weight', 'text_model.encoder.layers.3.self_attn.out_proj.weight', 'text_model.encoder.layers.9.self_attn.out_proj.weight', 'text_model.encoder.layers.3.self_attn.k_proj.weight', 'text_model.encoder.layers.4.self_attn.q_proj.bias', 'text_model.encoder.layers.0.mlp.fc2.bias', 'text_model.encoder.layers.10.self_attn.v_proj.bias', 'text_model.encoder.layers.9.mlp.fc1.weight', 'text_model.encoder.layers.11.self_attn.q_proj.weight', 'text_model.encoder.layers.10.mlp.fc2.weight', 'text_model.encoder.layers.1.mlp.fc1.weight', 'text_model.encoder.layers.4.mlp.fc2.weight', 'text_model.encoder.layers.5.mlp.fc2.bias', 'text_model.encoder.layers.1.mlp.fc2.weight', 'text_model.final_layer_norm.weight', 'text_model.encoder.layers.10.self_attn.k_proj.weight', 'text_model.encoder.layers.4.self_attn.q_proj.weight', 'text_model.encoder.layers.6.layer_norm1.bias', 'text_model.encoder.layers.8.self_attn.k_proj.bias', 'text_model.encoder.layers.9.self_attn.out_proj.bias', 'text_model.embeddings.position_ids', 'text_model.encoder.layers.0.self_attn.v_proj.weight', 'text_model.encoder.layers.9.mlp.fc2.bias', 'text_model.encoder.layers.6.self_attn.v_proj.weight', 'text_model.encoder.layers.4.mlp.fc2.bias', 'text_model.encoder.layers.7.self_attn.out_proj.weight', 'text_model.encoder.layers.10.layer_norm2.bias', 'text_model.encoder.layers.3.self_attn.v_proj.weight', 'text_model.encoder.layers.9.self_attn.q_proj.bias', 'text_model.encoder.layers.5.mlp.fc1.weight', 'text_model.encoder.layers.9.layer_norm1.weight', 'text_model.encoder.layers.10.self_attn.k_proj.bias', 'text_model.encoder.layers.2.layer_norm2.bias', 'text_model.encoder.layers.5.self_attn.v_proj.weight', 'text_model.encoder.layers.8.layer_norm1.bias', 'text_model.encoder.layers.3.self_attn.v_proj.bias', 'text_model.encoder.layers.7.mlp.fc1.bias', 'text_model.encoder.layers.5.self_attn.q_proj.weight', 'text_model.encoder.layers.0.layer_norm2.weight', 'text_model.encoder.layers.11.self_attn.q_proj.bias', 'text_model.encoder.layers.2.self_attn.v_proj.bias', 'text_model.encoder.layers.2.layer_norm1.bias', 'text_model.encoder.layers.11.layer_norm2.bias', 'text_model.encoder.layers.8.self_attn.q_proj.weight']\n","- This IS expected if you are initializing CLIPVisionModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing CLIPVisionModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"]}]},{"cell_type":"code","source":["def inference(qs, image_file) :\n","  image = load_image(image_file)\n","\n","  qs = qs + '\\n' + DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_PATCH_TOKEN * image_token_len + DEFAULT_IM_END_TOKEN\n","\n","  conv_mode = \"multimodal\"\n","  conv = conv_templates[conv_mode].copy()\n","  conv.append_message(conv.roles[0], qs)\n","  conv.append_message(conv.roles[1], None)\n","  prompt = conv.get_prompt()\n","  inputs = tokenizer([prompt])\n","\n","  image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]\n","  input_ids = torch.as_tensor(inputs.input_ids).cuda()\n","\n","  stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2\n","  keywords = [stop_str]\n","  stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)\n","\n","  with torch.inference_mode():\n","      output_ids = model.generate(\n","          input_ids,\n","          images=image_tensor.unsqueeze(0).half().cuda(),\n","          do_sample=True,\n","          temperature=0.2,\n","          max_new_tokens=1024,\n","          stopping_criteria=[stopping_criteria])\n","\n","  input_token_len = input_ids.shape[1]\n","  n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()\n","\n","  if n_diff_input_output > 0:\n","      print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')\n","  outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]\n","  outputs = outputs.strip()\n","  if outputs.endswith(stop_str):\n","      outputs = outputs[:-len(stop_str)]\n","  outputs = outputs.strip()\n","  print(outputs)"],"metadata":{"id":"QeE9OKElbJf3"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# image_file = \"https://llava.hliu.cc/file=/nobackup/haotian/code/LLaVA/llava/serve/examples/extreme_ironing.jpg\"\n","image_file = \"/content/냉장고.jpeg\"\n","query = \"이미지 속 재료들로 어떤 음식을 만들 수 있을까요?\"\n","\n","inference(query, image_file)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mdxN9azWcEpy","executionInfo":{"status":"ok","timestamp":1686757995270,"user_tz":-540,"elapsed":38045,"user":{"displayName":"Taebaek Hwang","userId":"08413173915740517335"}},"outputId":"b9b1b416-df94-4e21-eb0a-5d3f1377bd4a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["True\n","이미지에는 다양한 식품이 있는 냉장고가 있습니다. 냉장고에는 과일, 빵, 우유, 물, 주스, 달걀, 치즈, 버터, 베리류, 블루베리, 딸기, 당근 등 다양한 식품이 들어 있습니다. 이러한 재료를 사용하여 다양한 요리를 만들 수 있습니다. 몇 가지 가능한 옵션은 다음과 같습니다:\n","\n","1. 과일 샐러드: 베리류, 딸기, 당근을 섞어 신선하고 건강한 샐러드를 만들 수 있습니다.\n","2. 과일 베이스 스무디: 베리류, 딸기, 당근을 섞어 상큼한 스무디를 만들어 보세요.\n","3. 과일 샌드위치: 베리류, 딸기, 당근을 샌드위치 빵에 얹어 맛있는 샌드위치를 만들어 보세요.\n","4. 과일 디저트: 베리류, 딸기, 당근을 섞어 과일 코블러나 팬케이크와 같은 달콤한 디저트를 만들어 보세요.\n","5. 과일 기반 요리: 베리류, 딸기, 당근을 샐러드나 수프에 넣어 맛있는 요리를 만들어 보세요.\n","\n","이는 몇 가지 예에 불과하며, 냉장고에 있는 다양한 식품을 사용하여 다양한 요리를 만들 수 있는 가능성은 무궁무진합니다.\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"j7xi2itGcF-0"},"execution_count":null,"outputs":[]}]}