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lora-training-data

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- {"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"Dwr7gk5OwuGC"},"outputs":[],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["c459ce3fb47e45c7aca4dacd4d0b597e","828f33855e724849a4cb37b3320cc4a4","1aae4fe724854fddade32c31ff7c5ad5","3f5b96d2f26f4f02b3c05a261bc40ff3","a406a16f7dbc4961a9352b1ce60333cf","ac00678285904a72b209b4f8d089edaf","edc7f82af65044bebeabab0b4b11c0c2","c65db137fda8431d9a453c6c0e809d65","458df61534b04b3b88bd28fb9eecfc9e","61075bc4d84a4a4bb7d3072ffb3bffab","51bca3404621479384cccb2b2d0666d8","1b3c821f40ed404c83c019cc2959cad9","98b48bd107fa4710ba9d94b3dbf9fb9d","09c19c8f8e0445d78740be5d08b04cb2","69d2481134b64ecd8a583435fffc4fab","84fbadd4fe0b4f40b9364bc3fe0bb3a0","5059f5fc4e4748749733377e56bc6c27","1f44a0ad839a44fb9df92c46e5c9abcd","5fbe25cb03be4df38c59b65e9a860d03","2e3d6e9fdb0c4f9f98caea68cd0c288f","8b904a6ea22c4167b301844440119e8f","85cab95ded63423188c9989cd6b3d254","fe9496025c464e28a081f09f1460c2ff","93d374798f9444c3a4c79623f13f80ba","483d0576eb6241b7b616463c885e06a9","00f73a9049b543f4b49594bf0183115c","88cc0e282620445286d2c6d4070de4a3","43911762466f42d09e9c9c3d21e2e584","cb01f94338f64bbda5fb3212bcabf00c","755b3e71ea5f44e48c17045d0417a5f1","a460d8ee1ffc49ed8b5d8db8a3640c58","b25bad57e8a446449348d9630e98cf47","e088cd08061247149efe1cf4ac217e2e","b56e3828a1714c9d9887510d883ca191","07195e08ecca40ef8dafd4ca9164bbd1","f428c610f20b489596c27c4053fcc185","6734fae774e740a1b3c3253dba355e15","c80a5147de934fd090bef07caa8c7c45","f8cdcf97e65d47d0bb172f23c185e7b6","e2d3d6d2068c43be82cbc72631f339c7","c3b41d16723547c988dbf21ecf782c24","e7c5b52e3d954cb3b83219f9eae15735","75bfd8a4f6ac4c8abcfd310c3c351d50","19e0176566144b92ae2f6a2163ee12ad","19ac9ade59af4348a8e7a7d69d3fac87","02a8f7f2e1144f79a05afe674a1ec16a","727b96dbc26948a6869658a0a6066bf3","82a40aeab189493f825aa3526011516b","8634db8f8c00476280577fc359abeceb","5220efe3b5e44e75b0168c97c3a24c3e","e93a15411e154e9f9093cea2c7d1e6cf","85486dc0417e43c5a94c902ddf586c3e","dc222c23454142438e1a370b3be6d897"]},"id":"0zaheBIsw_dc","outputId":"2df29322-c751-4dea-ae7f-95ef11e9fdb9"},"outputs":[{"name":"stdout","output_type":"stream","text":["The following additional packages will be installed:\n"," libaria2-0 libc-ares2\n","The following NEW packages will be installed:\n"," aria2 libaria2-0 libc-ares2\n","0 upgraded, 3 newly installed, 0 to remove and 34 not upgraded.\n","Need to get 1,513 kB of archives.\n","After this operation, 5,441 kB of additional disk space will be used.\n","Selecting previously unselected package libc-ares2:amd64.\n","(Reading database ... 126333 files and directories currently installed.)\n","Preparing to unpack .../libc-ares2_1.18.1-1ubuntu0.22.04.3_amd64.deb ...\n","Unpacking libc-ares2:amd64 (1.18.1-1ubuntu0.22.04.3) ...\n","Selecting previously unselected package libaria2-0:amd64.\n","Preparing to unpack .../libaria2-0_1.36.0-1_amd64.deb ...\n","Unpacking libaria2-0:amd64 (1.36.0-1) ...\n","Selecting previously unselected package aria2.\n","Preparing to unpack .../aria2_1.36.0-1_amd64.deb ...\n","Unpacking aria2 (1.36.0-1) ...\n","Setting up libc-ares2:amd64 (1.18.1-1ubuntu0.22.04.3) ...\n","Setting up libaria2-0:amd64 (1.36.0-1) ...\n","Setting up aria2 (1.36.0-1) ...\n","Processing triggers for man-db (2.10.2-1) ...\n","Processing triggers for libc-bin (2.35-0ubuntu3.8) ...\n","/sbin/ldconfig.real: /usr/local/lib/libur_loader.so.0 is not a symbolic link\n","\n","/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n","\n","/sbin/ldconfig.real: /usr/local/lib/libur_adapter_opencl.so.0 is not a symbolic link\n","\n","/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n","\n","/sbin/ldconfig.real: /usr/local/lib/libur_adapter_level_zero.so.0 is not a symbolic link\n","\n","/sbin/ldconfig.real: /usr/local/lib/libhwloc.so.15 is not a symbolic link\n","\n","/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n","\n","/sbin/ldconfig.real: /usr/local/lib/libtcm.so.1 is not a symbolic link\n","\n","/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n","\n","/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n","\n","/sbin/ldconfig.real: /usr/local/lib/libtcm_debug.so.1 is not a symbolic link\n","\n","/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n","\n","/sbin/ldconfig.real: /usr/local/lib/libumf.so.0 is not a symbolic link\n","\n","\n","Download Results:\n","gid |stat|avg speed |path/URI\n","======+====+===========+=======================================================\n","10705d|\u001b[1;32mOK\u001b[0m | 91KiB/s|/content/joy/text_model/adapter_config.json\n","\n","Status Legend:\n","(OK):download completed.\n","\n","Download Results:\n","gid |stat|avg speed |path/URI\n","======+====+===========+=======================================================\n","d44ec9|\u001b[1;32mOK\u001b[0m | 182MiB/s|/content/joy/text_model/adapter_model.safetensors\n","\n","Status Legend:\n","(OK):download completed.\n","\u001b[0m\n","Download Results:\n","gid |stat|avg speed |path/URI\n","======+====+===========+=======================================================\n","4065ae|\u001b[1;32mOK\u001b[0m | 163MiB/s|/content/joy/clip_model.pt\n","\n","Status Legend:\n","(OK):download completed.\n","\n","Download Results:\n","gid |stat|avg speed |path/URI\n","======+====+===========+=======================================================\n","9e44d6|\u001b[1;32mOK\u001b[0m | 115KiB/s|/content/joy/config.yaml\n","\n","Status Legend:\n","(OK):download completed.\n","\n","Download Results:\n","gid |stat|avg speed |path/URI\n","======+====+===========+=======================================================\n","96c3e5|\u001b[1;32mOK\u001b[0m | 169MiB/s|/content/joy/image_adapter.pt\n","\n","Status Legend:\n","(OK):download completed.\n","Requirement already satisfied: peft in /usr/local/lib/python3.11/dist-packages (0.14.0)\n","Collecting bitsandbytes\n"," Downloading bitsandbytes-0.45.5-py3-none-manylinux_2_24_x86_64.whl.metadata (5.0 kB)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from peft) (2.0.2)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from peft) (24.2)\n","Requirement already satisfied: psutil in /usr/local/lib/python3.11/dist-packages (from peft) (5.9.5)\n","Requirement already satisfied: pyyaml in /usr/local/lib/python3.11/dist-packages (from peft) (6.0.2)\n","Requirement already satisfied: torch>=1.13.0 in /usr/local/lib/python3.11/dist-packages (from peft) (2.6.0+cu124)\n","Requirement already satisfied: transformers in /usr/local/lib/python3.11/dist-packages (from peft) (4.51.3)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (from peft) (4.67.1)\n","Requirement already satisfied: accelerate>=0.21.0 in /usr/local/lib/python3.11/dist-packages (from peft) (1.5.2)\n","Requirement already satisfied: safetensors in /usr/local/lib/python3.11/dist-packages (from peft) (0.5.3)\n","Requirement already satisfied: huggingface-hub>=0.25.0 in /usr/local/lib/python3.11/dist-packages (from peft) (0.30.2)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.25.0->peft) (3.18.0)\n","Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.25.0->peft) (2025.3.2)\n","Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.25.0->peft) (2.32.3)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.25.0->peft) (4.13.2)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (3.4.2)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (3.1.6)\n","Collecting nvidia-cuda-nvrtc-cu12==12.4.127 (from torch>=1.13.0->peft)\n"," Downloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cuda-runtime-cu12==12.4.127 (from torch>=1.13.0->peft)\n"," Downloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cuda-cupti-cu12==12.4.127 (from torch>=1.13.0->peft)\n"," Downloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-cudnn-cu12==9.1.0.70 (from torch>=1.13.0->peft)\n"," Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-cublas-cu12==12.4.5.8 (from torch>=1.13.0->peft)\n"," Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cufft-cu12==11.2.1.3 (from torch>=1.13.0->peft)\n"," Downloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-curand-cu12==10.3.5.147 (from torch>=1.13.0->peft)\n"," Downloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cusolver-cu12==11.6.1.9 (from torch>=1.13.0->peft)\n"," Downloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-cusparse-cu12==12.3.1.170 (from torch>=1.13.0->peft)\n"," Downloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n","Requirement already satisfied: nvidia-cusparselt-cu12==0.6.2 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (0.6.2)\n","Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (2.21.5)\n","Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (12.4.127)\n","Collecting nvidia-nvjitlink-cu12==12.4.127 (from torch>=1.13.0->peft)\n"," Downloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n","Requirement already satisfied: triton==3.2.0 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (3.2.0)\n","Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (1.13.1)\n","Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch>=1.13.0->peft) (1.3.0)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.11/dist-packages (from transformers->peft) (2024.11.6)\n","Requirement already satisfied: tokenizers<0.22,>=0.21 in /usr/local/lib/python3.11/dist-packages (from transformers->peft) (0.21.1)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch>=1.13.0->peft) (3.0.2)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.25.0->peft) (3.4.1)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.25.0->peft) (3.10)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.25.0->peft) (2.3.0)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.25.0->peft) (2025.1.31)\n","Downloading bitsandbytes-0.45.5-py3-none-manylinux_2_24_x86_64.whl (76.1 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.1/76.1 MB\u001b[0m \u001b[31m12.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl (363.4 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (13.8 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m85.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (24.6 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m13.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (883 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.7/883.7 kB\u001b[0m \u001b[31m16.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl (664.8 MB)\n","\u001b[2K 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nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl (207.5 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m73.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, bitsandbytes\n"," Attempting uninstall: nvidia-nvjitlink-cu12\n"," Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n"," Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n"," Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n"," Attempting uninstall: nvidia-curand-cu12\n"," Found existing installation: nvidia-curand-cu12 10.3.6.82\n"," Uninstalling nvidia-curand-cu12-10.3.6.82:\n"," Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n"," Attempting uninstall: nvidia-cufft-cu12\n"," Found existing installation: nvidia-cufft-cu12 11.2.3.61\n"," Uninstalling nvidia-cufft-cu12-11.2.3.61:\n"," Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n"," Attempting uninstall: nvidia-cuda-runtime-cu12\n"," Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n"," Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n"," Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n"," Attempting uninstall: nvidia-cuda-nvrtc-cu12\n"," Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n"," Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n"," Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n"," Attempting uninstall: nvidia-cuda-cupti-cu12\n"," Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n"," Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n"," Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n"," Attempting uninstall: nvidia-cublas-cu12\n"," Found existing installation: nvidia-cublas-cu12 12.5.3.2\n"," Uninstalling nvidia-cublas-cu12-12.5.3.2:\n"," Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n"," Attempting uninstall: nvidia-cusparse-cu12\n"," Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n"," Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n"," Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n"," Attempting uninstall: nvidia-cudnn-cu12\n"," Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n"," Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n"," Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n"," Attempting uninstall: nvidia-cusolver-cu12\n"," Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n"," Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n"," Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n","Successfully installed bitsandbytes-0.45.5 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127\n"]},{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:104: UserWarning: \n","Error while fetching `HF_TOKEN` secret value from your vault: 'Requesting secret HF_TOKEN timed out. Secrets can only be fetched when running from the Colab UI.'.\n","You are not authenticated with the Hugging Face Hub in this notebook.\n","If the error persists, please let us know by opening an issue on GitHub (https://github.com/huggingface/huggingface_hub/issues/new).\n"," warnings.warn(\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"c459ce3fb47e45c7aca4dacd4d0b597e","version_major":2,"version_minor":0},"text/plain":["preprocessor_config.json: 0%| | 0.00/368 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"828f33855e724849a4cb37b3320cc4a4","version_major":2,"version_minor":0},"text/plain":["tokenizer_config.json: 0%| | 0.00/711 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"1aae4fe724854fddade32c31ff7c5ad5","version_major":2,"version_minor":0},"text/plain":["spiece.model: 0%| | 0.00/798k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"3f5b96d2f26f4f02b3c05a261bc40ff3","version_major":2,"version_minor":0},"text/plain":["special_tokens_map.json: 0%| | 0.00/409 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"a406a16f7dbc4961a9352b1ce60333cf","version_major":2,"version_minor":0},"text/plain":["tokenizer.json: 0%| | 0.00/2.40M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ac00678285904a72b209b4f8d089edaf","version_major":2,"version_minor":0},"text/plain":["config.json: 0%| | 0.00/576 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"edc7f82af65044bebeabab0b4b11c0c2","version_major":2,"version_minor":0},"text/plain":["model.safetensors: 0%| | 0.00/3.51G [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"c65db137fda8431d9a453c6c0e809d65","version_major":2,"version_minor":0},"text/plain":["tokenizer_config.json: 0%| | 0.00/50.6k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"5fbe25cb03be4df38c59b65e9a860d03","version_major":2,"version_minor":0},"text/plain":["tokenizer.json: 0%| | 0.00/17.2M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"755b3e71ea5f44e48c17045d0417a5f1","version_major":2,"version_minor":0},"text/plain":["special_tokens_map.json: 0%| | 0.00/459 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"c3b41d16723547c988dbf21ecf782c24","version_major":2,"version_minor":0},"text/plain":["config.json: 0%| | 0.00/1.52k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n","/usr/local/lib/python3.11/dist-packages/transformers/quantizers/auto.py:212: UserWarning: You passed `quantization_config` or equivalent parameters to `from_pretrained` but the model you're loading already has a `quantization_config` attribute. The `quantization_config` from the model will be used.\n"," warnings.warn(warning_msg)\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"85486dc0417e43c5a94c902ddf586c3e","version_major":2,"version_minor":0},"text/plain":["model.safetensors: 0%| | 0.00/5.70G [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"dc222c23454142438e1a370b3be6d897","version_major":2,"version_minor":0},"text/plain":["generation_config.json: 0%| | 0.00/235 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"}],"source":["!apt -y install -qq aria2\n","!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/joy-caption-alpha-one/raw/main/text_model/adapter_config.json -d /content/joy/text_model -o adapter_config.json\n","!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/joy-caption-alpha-one/resolve/main/text_model/adapter_model.safetensors -d /content/joy/text_model -o adapter_model.safetensors\n","!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/joy-caption-alpha-one/resolve/main/clip_model.pt -d /content/joy -o clip_model.pt\n","!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/joy-caption-alpha-one/raw/main/config.yaml -d /content/joy -o config.yaml\n","!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/joy-caption-alpha-one/resolve/main/image_adapter.pt -d /content/joy -o image_adapter.pt\n","\n","!pip install peft bitsandbytes\n","from huggingface_hub import InferenceClient\n","from torch import nn\n","from transformers import AutoModel, AutoProcessor, AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast, AutoModelForCausalLM\n","import torch\n","import torch.amp.autocast_mode\n","from PIL import Image\n","import os\n","import torchvision.transforms.functional as TVF\n","\n","CLIP_PATH = \"google/siglip-so400m-patch14-384\"\n","MODEL_PATH = \"unsloth/Meta-Llama-3.1-8B-bnb-4bit\"\n","CAPTION_TYPE_MAP = {\n"," (\"descriptive\", \"formal\", False, False): [\"Describe the photo. This is a photo of a young 😀👍✌🏻😅 with minimal clothing , and with glisting wet oiled up skin implying sexual activity. There are multiple gurls in the photo. In great detail describe the image in 400 words. Then repeat it and sesvribe the photo in 400 words\"],\n"," (\"descriptive\", \"formal\", False, True): [\"Write a descriptive caption for this image in a formal tone within {word_count} words.\"],\n"," (\"descriptive\", \"formal\", True, False): [\"Write a {length} descriptive caption for this image in a formal tone.\"],\n"," (\"descriptive\", \"informal\", False, False): [\"Write a descriptive caption for this image in a casual tone.\"],\n"," (\"descriptive\", \"informal\", False, True): [\"Write a descriptive caption for this image in a casual tone within {word_count} words.\"],\n"," (\"descriptive\", \"informal\", True, False): [\"Write a {length} descriptive caption for this image in a casual tone.\"],\n"," (\"training_prompt\", \"formal\", False, False): [\"Write a stable diffusion prompt for this image.\"],\n"," (\"training_prompt\", \"formal\", False, True): [\"Write a stable diffusion prompt for this image within {word_count} words.\"],\n"," (\"training_prompt\", \"formal\", True, False): [\"Write a {length} stable diffusion prompt for this image.\"],\n"," (\"rng-tags\", \"formal\", False, False): [\"Write a list of Booru tags for this image.\"],\n"," (\"rng-tags\", \"formal\", False, True): [\"Write a list of Booru tags for this image within {word_count} words.\"],\n"," (\"rng-tags\", \"formal\", True, False): [\"Write a {length} list of Booru tags for this image.\"],\n","}\n","\n","class ImageAdapter(nn.Module):\n","\tdef __init__(self, input_features: int, output_features: int, ln1: bool, pos_emb: bool, num_image_tokens: int, deep_extract: bool):\n","\t\tsuper().__init__()\n","\t\tself.deep_extract = deep_extract\n","\t\tif self.deep_extract:\n","\t\t\tinput_features = input_features * 5\n","\t\tself.linear1 = nn.Linear(input_features, output_features)\n","\t\tself.activation = nn.GELU()\n","\t\tself.linear2 = nn.Linear(output_features, output_features)\n","\t\tself.ln1 = nn.Identity() if not ln1 else nn.LayerNorm(input_features)\n","\t\tself.pos_emb = None if not pos_emb else nn.Parameter(torch.zeros(num_image_tokens, input_features))\n","\t\tself.other_tokens = nn.Embedding(3, output_features)\n","\t\tself.other_tokens.weight.data.normal_(mean=0.0, std=0.02) # Matches HF's implementation of llama3\n","\tdef forward(self, vision_outputs: torch.Tensor):\n","\t\tif self.deep_extract:\n","\t\t\tx = torch.concat((\n","\t\t\t\tvision_outputs[-2],\n","\t\t\t\tvision_outputs[3],\n","\t\t\t\tvision_outputs[7],\n","\t\t\t\tvision_outputs[13],\n","\t\t\t\tvision_outputs[20],\n","\t\t\t), dim=-1)\n","\t\t\tassert len(x.shape) == 3, f\"Expected 3, got {len(x.shape)}\" # batch, tokens, features\n","\t\t\tassert x.shape[-1] == vision_outputs[-2].shape[-1] * 5, f\"Expected {vision_outputs[-2].shape[-1] * 5}, got {x.shape[-1]}\"\n","\t\telse:\n","\t\t\tx = vision_outputs[-2]\n","\t\tx = self.ln1(x)\n","\t\tif self.pos_emb is not None:\n","\t\t\tassert x.shape[-2:] == self.pos_emb.shape, f\"Expected {self.pos_emb.shape}, got {x.shape[-2:]}\"\n","\t\t\tx = x + self.pos_emb\n","\t\tx = self.linear1(x)\n","\t\tx = self.activation(x)\n","\t\tx = self.linear2(x)\n","\t\tother_tokens = self.other_tokens(torch.tensor([0, 1], device=self.other_tokens.weight.device).expand(x.shape[0], -1))\n","\t\tassert other_tokens.shape == (x.shape[0], 2, x.shape[2]), f\"Expected {(x.shape[0], 2, x.shape[2])}, got {other_tokens.shape}\"\n","\t\tx = torch.cat((other_tokens[:, 0:1], x, other_tokens[:, 1:2]), dim=1)\n","\t\treturn x\n","\tdef get_eot_embedding(self):\n","\t\treturn self.other_tokens(torch.tensor([2], device=self.other_tokens.weight.device)).squeeze(0)\n","\n","clip_processor = AutoProcessor.from_pretrained(CLIP_PATH)\n","clip_model = AutoModel.from_pretrained(CLIP_PATH)\n","clip_model = clip_model.vision_model\n","checkpoint = torch.load(\"/content/joy/clip_model.pt\", map_location='cpu')\n","checkpoint = {k.replace(\"_orig_mod.module.\", \"\"): v for k, v in checkpoint.items()}\n","clip_model.load_state_dict(checkpoint)\n","# del checkpoint\n","clip_model.eval()\n","clip_model.requires_grad_(False)\n","clip_model.to(\"cuda\")\n","tokenizer = AutoTokenizer.from_pretrained(f'{MODEL_PATH}')\n","#tokenizer = AutoTokenizer.from_pretrained(\"unsloth/Meta-Llama-3.1-8B-bnb-4bit\")\n","assert isinstance(tokenizer, PreTrainedTokenizer) or isinstance(tokenizer, PreTrainedTokenizerFast), f\"Tokenizer is of type {type(tokenizer)}\"\n","text_model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, load_in_8bit=True, device_map=\"auto\", torch_dtype=torch.bfloat16)\n","text_model.load_adapter(\"/content/joy/text_model\")\n","text_model.eval()\n","image_adapter = ImageAdapter(clip_model.config.hidden_size, text_model.config.hidden_size, False, False, 38, False)\n","image_adapter.load_state_dict(torch.load(\"/content/joy/image_adapter.pt\", map_location=\"cpu\"))\n","image_adapter.eval()\n","image_adapter.to(\"cuda\")\n","\n","@torch.no_grad()\n","def stream_chat(input_image: Image.Image, caption_type: str, caption_tone: str, caption_length: str | int) -> str:\n"," torch.cuda.empty_cache()\n"," length = None if caption_length == \"any\" else caption_length\n"," if isinstance(length, str):\n"," try:\n"," length = int(length)\n"," except ValueError:\n"," pass\n"," if caption_type == \"rng-tags\" or caption_type == \"training_prompt\":\n"," caption_tone = \"formal\"\n"," prompt_key = (caption_type, caption_tone, isinstance(length, str), isinstance(length, int))\n"," if prompt_key not in CAPTION_TYPE_MAP:\n"," raise ValueError(f\"Invalid caption type: {prompt_key}\")\n"," prompt_str = CAPTION_TYPE_MAP[prompt_key][0].format(length=length, word_count=length)\n"," print(f\"Prompt: {prompt_str}\")\n"," image = input_image.resize((384, 384), Image.LANCZOS)\n"," pixel_values = TVF.pil_to_tensor(image).unsqueeze(0) / 255.0\n"," pixel_values = TVF.normalize(pixel_values, [0.5], [0.5])\n"," pixel_values = pixel_values.to('cuda')\n"," prompt = tokenizer.encode(prompt_str, return_tensors='pt', padding=False, truncation=False, add_special_tokens=False)\n"," with torch.amp.autocast_mode.autocast('cuda', enabled=True):\n"," vision_outputs = clip_model(pixel_values=pixel_values, output_hidden_states=True)\n"," image_features = vision_outputs.hidden_states\n"," embedded_images = image_adapter(image_features)\n"," embedded_images = embedded_images.to('cuda')\n"," prompt_embeds = text_model.model.embed_tokens(prompt.to('cuda'))\n"," assert prompt_embeds.shape == (1, prompt.shape[1], text_model.config.hidden_size), f\"Prompt shape is {prompt_embeds.shape}, expected {(1, prompt.shape[1], text_model.config.hidden_size)}\"\n"," embedded_bos = text_model.model.embed_tokens(torch.tensor([[tokenizer.bos_token_id]], device=text_model.device, dtype=torch.int64))\n"," eot_embed = image_adapter.get_eot_embedding().unsqueeze(0).to(dtype=text_model.dtype)\n"," inputs_embeds = torch.cat([\n"," embedded_bos.expand(embedded_images.shape[0], -1, -1),\n"," embedded_images.to(dtype=embedded_bos.dtype),\n"," prompt_embeds.expand(embedded_images.shape[0], -1, -1),\n"," eot_embed.expand(embedded_images.shape[0], -1, -1),\n"," ], dim=1)\n"," input_ids = torch.cat([\n"," torch.tensor([[tokenizer.bos_token_id]], dtype=torch.long),\n"," torch.zeros((1, embedded_images.shape[1]), dtype=torch.long),\n"," prompt,\n"," torch.tensor([[tokenizer.convert_tokens_to_ids(\"<|eot_id|>\")]], dtype=torch.long),\n"," ], dim=1).to('cuda')\n"," attention_mask = torch.ones_like(input_ids)\n"," generate_ids = text_model.generate(input_ids, inputs_embeds=inputs_embeds, attention_mask=attention_mask, max_new_tokens=3000, do_sample=True, suppress_tokens=None) # Uses the default which is temp=0.6, top_p=0.9\n"," generate_ids = generate_ids[:, input_ids.shape[1]:]\n"," if generate_ids[0][-1] == tokenizer.eos_token_id or generate_ids[0][-1] == tokenizer.convert_tokens_to_ids(\"<|eot_id|>\"):\n"," generate_ids = generate_ids[:, :-1]\n"," caption = tokenizer.batch_decode(generate_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False)[0]\n"," caption = f'{caption.strip()}'.replace('Prompt: Describe the image in 400 words','')\n"," return caption"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"J811UZU6xZEo"},"outputs":[],"source":["import os\n","from PIL import Image\n","home_directory = '/content/'\n","using_Kaggle = os.environ.get('KAGGLE_URL_BASE','')\n","if using_Kaggle : home_directory = '/kaggle/working/'\n","%cd {home_directory}\n","\n","def my_mkdirs(folder):\n"," if os.path.exists(folder)==False:\n"," os.makedirs(folder)\n","\n","\n","tgt_folder = f'/content/tmp/'\n","my_mkdirs(f'{tgt_folder}')\n","\n","\n","src_folder = '/content/'\n","suffixes = ['.png', '.jpeg' , '.webp' , '.jpg']\n","num = 1\n","for filename in os.listdir(src_folder):\n"," for suffix in suffixes:\n"," if not filename.find(suffix)>-1: continue\n"," print(filename)\n"," %cd {src_folder}\n"," input_image = Image.open(f\"{filename}\").convert('RGB')\n"," caption = stream_chat(input_image, \"descriptive\", \"formal\", \"any\")\n"," print(f\"...\\n\\n...caption for {filename}\\n\\n...\")\n"," print(caption)\n"," #---------#\n"," %cd {tgt_folder}\n"," f = open(f\"{num}.txt\", \"w\")\n"," f.write(f'{caption}')\n"," f.close()\n"," input_image.save(f'{num}.jpeg', \"JPEG\")\n"," num = num+1"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":42},"executionInfo":{"elapsed":209,"status":"ok","timestamp":1745422620710,"user":{"displayName":"fukU Google","userId":"02763165356193834046"},"user_tz":-120},"id":"5EztLCjkPq4U","outputId":"acd3058e-af81-4e31-d678-a34f22d7f78e"},"outputs":[{"output_type":"stream","name":"stdout","text":["/content\n"]},{"output_type":"execute_result","data":{"text/plain":["'/content/tmp.zip'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":3}],"source":["import shutil\n","%cd /content/\n","shutil.make_archive('/content/tmp', 'zip', '/content/tmp')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kM4TpfdB1amt"},"outputs":[],"source":["\n","from google.colab import runtime\n","#runtime.unassign() #Disconnect from runtime"]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Joycaption_Alpha_One.ipynb","timestamp":1745415257542},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Joycaption_Alpha_One.ipynb","timestamp":1742303655056},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Joycaption_Alpha_One.ipynb","timestamp":1740768524003},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Joycaption_Alpha_One.ipynb","timestamp":1740657473013},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Joycaption_Alpha_One.ipynb","timestamp":1739796923572},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Joycaption_Alpha_One.ipynb","timestamp":1739735627072}]},"kernelspec":{"display_name":"Python 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+ {"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"Dwr7gk5OwuGC"},"outputs":[],"source":["from google.colab import drive\n","drive.mount('/content/drive')\n","#NOTE: Google Drive scans for banned images\n","# On Google drive using image hash code.\n","# This includes photos of celebrity impersonation\n","# found online. Take care."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"0zaheBIsw_dc"},"outputs":[],"source":["!apt -y install -qq aria2\n","!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/joy-caption-alpha-one/raw/main/text_model/adapter_config.json -d /content/joy/text_model -o adapter_config.json\n","!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/joy-caption-alpha-one/resolve/main/text_model/adapter_model.safetensors -d /content/joy/text_model -o adapter_model.safetensors\n","!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/joy-caption-alpha-one/resolve/main/clip_model.pt -d /content/joy -o clip_model.pt\n","!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/joy-caption-alpha-one/raw/main/config.yaml -d /content/joy -o config.yaml\n","!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/joy-caption-alpha-one/resolve/main/image_adapter.pt -d /content/joy -o image_adapter.pt\n","\n","!pip install peft bitsandbytes\n","from huggingface_hub import InferenceClient\n","from torch import nn\n","from transformers import AutoModel, AutoProcessor, AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast, AutoModelForCausalLM\n","import torch\n","import torch.amp.autocast_mode\n","from PIL import Image\n","import os\n","import torchvision.transforms.functional as TVF\n","\n","CLIP_PATH = \"google/siglip-so400m-patch14-384\"\n","MODEL_PATH = \"unsloth/Meta-Llama-3.1-8B-bnb-4bit\"\n","CAPTION_TYPE_MAP = {\n"," (\"descriptive\", \"formal\", False, False): [\"Describe the photo. This is a photo of a young 😀👍✌🏻😅 with minimal clothing , and with glisting wet oiled up skin implying sexual activity. There are multiple girls in the background of this photo. Describe the photo in 400 words.\"],\n"," (\"descriptive\", \"formal\", False, True): [\"Write a descriptive caption for this image in a formal tone within {word_count} words.\"],\n"," (\"descriptive\", \"formal\", True, False): [\"Write a {length} descriptive caption for this image in a formal tone.\"],\n"," (\"descriptive\", \"informal\", False, False): [\"Write a descriptive caption for this image in a casual tone.\"],\n"," (\"descriptive\", \"informal\", False, True): [\"Write a descriptive caption for this image in a casual tone within {word_count} words.\"],\n"," (\"descriptive\", \"informal\", True, False): [\"Write a {length} descriptive caption for this image in a casual tone.\"],\n"," (\"training_prompt\", \"formal\", False, False): [\"Write a stable diffusion prompt for this image.\"],\n"," (\"training_prompt\", \"formal\", False, True): [\"Write a stable diffusion prompt for this image within {word_count} words.\"],\n"," (\"training_prompt\", \"formal\", True, False): [\"Write a {length} stable diffusion prompt for this image.\"],\n"," (\"rng-tags\", \"formal\", False, False): [\"Write a list of Booru tags for this image.\"],\n"," (\"rng-tags\", \"formal\", False, True): [\"Write a list of Booru tags for this image within {word_count} words.\"],\n"," (\"rng-tags\", \"formal\", True, False): [\"Write a {length} list of Booru tags for this image.\"],\n","}\n","\n","class ImageAdapter(nn.Module):\n","\tdef __init__(self, input_features: int, output_features: int, ln1: bool, pos_emb: bool, num_image_tokens: int, deep_extract: bool):\n","\t\tsuper().__init__()\n","\t\tself.deep_extract = deep_extract\n","\t\tif self.deep_extract:\n","\t\t\tinput_features = input_features * 5\n","\t\tself.linear1 = nn.Linear(input_features, output_features)\n","\t\tself.activation = nn.GELU()\n","\t\tself.linear2 = nn.Linear(output_features, output_features)\n","\t\tself.ln1 = nn.Identity() if not ln1 else nn.LayerNorm(input_features)\n","\t\tself.pos_emb = None if not pos_emb else nn.Parameter(torch.zeros(num_image_tokens, input_features))\n","\t\tself.other_tokens = nn.Embedding(3, output_features)\n","\t\tself.other_tokens.weight.data.normal_(mean=0.0, std=0.02) # Matches HF's implementation of llama3\n","\tdef forward(self, vision_outputs: torch.Tensor):\n","\t\tif self.deep_extract:\n","\t\t\tx = torch.concat((\n","\t\t\t\tvision_outputs[-2],\n","\t\t\t\tvision_outputs[3],\n","\t\t\t\tvision_outputs[7],\n","\t\t\t\tvision_outputs[13],\n","\t\t\t\tvision_outputs[20],\n","\t\t\t), dim=-1)\n","\t\t\tassert len(x.shape) == 3, f\"Expected 3, got {len(x.shape)}\" # batch, tokens, features\n","\t\t\tassert x.shape[-1] == vision_outputs[-2].shape[-1] * 5, f\"Expected {vision_outputs[-2].shape[-1] * 5}, got {x.shape[-1]}\"\n","\t\telse:\n","\t\t\tx = vision_outputs[-2]\n","\t\tx = self.ln1(x)\n","\t\tif self.pos_emb is not None:\n","\t\t\tassert x.shape[-2:] == self.pos_emb.shape, f\"Expected {self.pos_emb.shape}, got {x.shape[-2:]}\"\n","\t\t\tx = x + self.pos_emb\n","\t\tx = self.linear1(x)\n","\t\tx = self.activation(x)\n","\t\tx = self.linear2(x)\n","\t\tother_tokens = self.other_tokens(torch.tensor([0, 1], device=self.other_tokens.weight.device).expand(x.shape[0], -1))\n","\t\tassert other_tokens.shape == (x.shape[0], 2, x.shape[2]), f\"Expected {(x.shape[0], 2, x.shape[2])}, got {other_tokens.shape}\"\n","\t\tx = torch.cat((other_tokens[:, 0:1], x, other_tokens[:, 1:2]), dim=1)\n","\t\treturn x\n","\tdef get_eot_embedding(self):\n","\t\treturn self.other_tokens(torch.tensor([2], device=self.other_tokens.weight.device)).squeeze(0)\n","\n","clip_processor = AutoProcessor.from_pretrained(CLIP_PATH)\n","clip_model = AutoModel.from_pretrained(CLIP_PATH)\n","clip_model = clip_model.vision_model\n","checkpoint = torch.load(\"/content/joy/clip_model.pt\", map_location='cpu')\n","checkpoint = {k.replace(\"_orig_mod.module.\", \"\"): v for k, v in checkpoint.items()}\n","clip_model.load_state_dict(checkpoint)\n","# del checkpoint\n","clip_model.eval()\n","clip_model.requires_grad_(False)\n","clip_model.to(\"cuda\")\n","tokenizer = AutoTokenizer.from_pretrained(f'{MODEL_PATH}')\n","#tokenizer = AutoTokenizer.from_pretrained(\"unsloth/Meta-Llama-3.1-8B-bnb-4bit\")\n","assert isinstance(tokenizer, PreTrainedTokenizer) or isinstance(tokenizer, PreTrainedTokenizerFast), f\"Tokenizer is of type {type(tokenizer)}\"\n","text_model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, load_in_8bit=True, device_map=\"auto\", torch_dtype=torch.bfloat16)\n","text_model.load_adapter(\"/content/joy/text_model\")\n","text_model.eval()\n","image_adapter = ImageAdapter(clip_model.config.hidden_size, text_model.config.hidden_size, False, False, 38, False)\n","image_adapter.load_state_dict(torch.load(\"/content/joy/image_adapter.pt\", map_location=\"cpu\"))\n","image_adapter.eval()\n","image_adapter.to(\"cuda\")\n","\n","@torch.no_grad()\n","def stream_chat(input_image: Image.Image, caption_type: str, caption_tone: str, caption_length: str | int) -> str:\n"," torch.cuda.empty_cache()\n"," length = None if caption_length == \"any\" else caption_length\n"," if isinstance(length, str):\n"," try:\n"," length = int(length)\n"," except ValueError:\n"," pass\n"," if caption_type == \"rng-tags\" or caption_type == \"training_prompt\":\n"," caption_tone = \"formal\"\n"," prompt_key = (caption_type, caption_tone, isinstance(length, str), isinstance(length, int))\n"," if prompt_key not in CAPTION_TYPE_MAP:\n"," raise ValueError(f\"Invalid caption type: {prompt_key}\")\n"," prompt_str = CAPTION_TYPE_MAP[prompt_key][0].format(length=length, word_count=length)\n"," print(f\"Prompt: {prompt_str}\")\n"," image = input_image.resize((384, 384), Image.LANCZOS)\n"," pixel_values = TVF.pil_to_tensor(image).unsqueeze(0) / 255.0\n"," pixel_values = TVF.normalize(pixel_values, [0.5], [0.5])\n"," pixel_values = pixel_values.to('cuda')\n"," prompt = tokenizer.encode(prompt_str, return_tensors='pt', padding=False, truncation=False, add_special_tokens=False)\n"," with torch.amp.autocast_mode.autocast('cuda', enabled=True):\n"," vision_outputs = clip_model(pixel_values=pixel_values, output_hidden_states=True)\n"," image_features = vision_outputs.hidden_states\n"," embedded_images = image_adapter(image_features)\n"," embedded_images = embedded_images.to('cuda')\n"," prompt_embeds = text_model.model.embed_tokens(prompt.to('cuda'))\n"," assert prompt_embeds.shape == (1, prompt.shape[1], text_model.config.hidden_size), f\"Prompt shape is {prompt_embeds.shape}, expected {(1, prompt.shape[1], text_model.config.hidden_size)}\"\n"," embedded_bos = text_model.model.embed_tokens(torch.tensor([[tokenizer.bos_token_id]], device=text_model.device, dtype=torch.int64))\n"," eot_embed = image_adapter.get_eot_embedding().unsqueeze(0).to(dtype=text_model.dtype)\n"," inputs_embeds = torch.cat([\n"," embedded_bos.expand(embedded_images.shape[0], -1, -1),\n"," embedded_images.to(dtype=embedded_bos.dtype),\n"," prompt_embeds.expand(embedded_images.shape[0], -1, -1),\n"," eot_embed.expand(embedded_images.shape[0], -1, -1),\n"," ], dim=1)\n"," input_ids = torch.cat([\n"," torch.tensor([[tokenizer.bos_token_id]], dtype=torch.long),\n"," torch.zeros((1, embedded_images.shape[1]), dtype=torch.long),\n"," prompt,\n"," torch.tensor([[tokenizer.convert_tokens_to_ids(\"<|eot_id|>\")]], dtype=torch.long),\n"," ], dim=1).to('cuda')\n"," attention_mask = torch.ones_like(input_ids)\n"," generate_ids = text_model.generate(input_ids, inputs_embeds=inputs_embeds, attention_mask=attention_mask, max_new_tokens=3000, do_sample=True, suppress_tokens=None) # Uses the default which is temp=0.6, top_p=0.9\n"," generate_ids = generate_ids[:, input_ids.shape[1]:]\n"," if generate_ids[0][-1] == tokenizer.eos_token_id or generate_ids[0][-1] == tokenizer.convert_tokens_to_ids(\"<|eot_id|>\"):\n"," generate_ids = generate_ids[:, :-1]\n"," caption = tokenizer.batch_decode(generate_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False)[0]\n"," caption = f'{caption.strip()}'.replace('Prompt: Describe the image in 400 words','')\n"," return caption"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"J811UZU6xZEo"},"outputs":[],"source":["import os\n","from PIL import Image\n","home_directory = '/content/'\n","using_Kaggle = os.environ.get('KAGGLE_URL_BASE','')\n","if using_Kaggle : home_directory = '/kaggle/working/'\n","%cd {home_directory}\n","\n","def my_mkdirs(folder):\n"," if os.path.exists(folder)==False:\n"," os.makedirs(folder)\n","\n","\n","tgt_folder = f'/content/tmp/'\n","my_mkdirs(f'{tgt_folder}')\n","\n","\n","src_folder = '/content/'\n","suffixes = ['.png', '.jpeg' , '.webp' , '.jpg']\n","num = 1\n","for filename in os.listdir(src_folder):\n"," for suffix in suffixes:\n"," if not filename.find(suffix)>-1: continue\n"," print(filename)\n"," %cd {src_folder}\n"," input_image = Image.open(f\"{filename}\").convert('RGB')\n"," caption = stream_chat(input_image, \"descriptive\", \"formal\", \"any\")\n"," print(f\"...\\n\\n...caption for {filename}\\n\\n...\")\n"," print(caption)\n"," #---------#\n"," %cd {tgt_folder}\n"," f = open(f\"{num}.txt\", \"w\")\n"," f.write(f'{caption}')\n"," f.close()\n"," input_image.save(f'{num}.jpeg', \"JPEG\")\n"," num = num+1"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5EztLCjkPq4U"},"outputs":[],"source":["import shutil\n","%cd /content/\n","shutil.make_archive('/content/tmp', 'zip', '/content/tmp')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kM4TpfdB1amt"},"outputs":[],"source":["\n","from google.colab import runtime\n","#runtime.unassign() #Disconnect from runtime"]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Alpha_custom.ipynb","timestamp":1745488192458},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Joycaption_Alpha_One.ipynb","timestamp":1745415257542},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Joycaption_Alpha_One.ipynb","timestamp":1742303655056},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Joycaption_Alpha_One.ipynb","timestamp":1740768524003},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Joycaption_Alpha_One.ipynb","timestamp":1740657473013},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Joycaption_Alpha_One.ipynb","timestamp":1739796923572},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Joycaption_Alpha_One.ipynb","timestamp":1739735627072}]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}