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ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for ffmpy: filename=ffmpy-0.3.2-py3-none-any.whl size=5584 sha256=da73faaa3cee3e9001eed296dc6ed7bbc459ee4b0eec21b0971b178b46d95d01\n", " Stored in directory: /root/.cache/pip/wheels/bd/65/9a/671fc6dcde07d4418df0c592f8df512b26d7a0029c2a23dd81\n", "Successfully built llmtuner fire ffmpy\n", "Installing collected packages: pydub, ffmpy, xxhash, websockets, tomlkit, shtab, shellingham, semantic-version, ruff, python-multipart, orjson, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, h11, fire, einops, dill, colorama, aiofiles, uvicorn, starlette, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, httpcore, tyro, sse-starlette, nvidia-cusolver-cu12, httpx, fastapi, gradio-client, datasets, gradio, accelerate, trl, peft, llmtuner\n", "Successfully installed accelerate-0.29.1 aiofiles-23.2.1 colorama-0.4.6 datasets-2.18.0 dill-0.3.8 einops-0.7.0 fastapi-0.110.1 ffmpy-0.3.2 fire-0.6.0 gradio-4.21.0 gradio-client-0.12.0 h11-0.14.0 httpcore-1.0.5 httpx-0.27.0 llmtuner-0.6.2.dev0 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 orjson-3.10.0 peft-0.10.0 pydub-0.25.1 python-multipart-0.0.9 ruff-0.3.5 semantic-version-2.10.0 shellingham-1.5.4 shtab-1.7.1 sse-starlette-2.1.0 starlette-0.37.2 tomlkit-0.12.0 trl-0.8.1 tyro-0.8.2 uvicorn-0.29.0 websockets-11.0.3 xxhash-3.4.1\n" ] } ], "source": [ "%rm -rf LLaMA-Factory\n", "!git clone https://github.com/hiyouga/LLaMA-Factory.git\n", "%cd LLaMA-Factory\n", "%ls\n", "!pip install ." ] }, { "cell_type": "markdown", "source": [ "### Check GPU environment" ], "metadata": { "id": "H9RXn_YQnn9f" } }, { "cell_type": "code", "source": [ "import torch\n", "try:\n", " assert torch.cuda.is_available() is True\n", "except AssertionError:\n", " print(\"Please set up a GPU before using LLaMA Factory: https://medium.com/mlearning-ai/training-yolov4-on-google-colab-316f8fff99c6\")" ], "metadata": { "id": "ZkN-ktlsnrdU" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Log in with Hugging Face account to upload model (Optional)" ], "metadata": { "id": "okkbTMoZCQNf" } }, { "cell_type": "code", "source": [ "# @title Standardtext für Titel\n", "!huggingface-cli login" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6OIm0O7oA5sy", "outputId": "4adbdc1a-5748-4ca1-9586-236761980293" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", " _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|\n", " _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n", " _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n", " _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n", " _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n", "\n", " To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n", "Token: \n", "Add token as git credential? (Y/n) y\n", "Token is valid (permission: write).\n", "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n", "You might have to re-authenticate when pushing to the Hugging Face Hub.\n", "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n", "\n", "git config --global credential.helper store\n", "\n", "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n", "Token has not been saved to git credential helper.\n", "Your token has been saved to /root/.cache/huggingface/token\n", "Login successful\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Fine-tune model via LLaMA Board" ], "metadata": { "id": "2QiXcvdzzW3Y" } }, { "cell_type": "code", "source": [ "from llmtuner import create_ui\n", "\n", "create_ui().queue().launch(share=True)" ], "metadata": { "id": "YLsdS6V5yUMy" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Fine-tune model via Command Line" ], "metadata": { "id": "rgR3UFhB0Ifq" } }, { "cell_type": "code", "source": [ "from llmtuner import run_exp\n", "run_exp(dict(\n", " stage=\"sft\",\n", " do_train=True,\n", " model_name_or_path=\"mistralai/Mistral-7B-v0.1\",\n", " dataset=\"oaast_sft\",\n", " template=\"mistral\",\n", " finetuning_type=\"lora\",\n", " lora_target=\"all\",\n", " output_dir=\"test_identity\",\n", " per_device_train_batch_size=4,\n", " gradient_accumulation_steps=4,\n", " lr_scheduler_type=\"cosine\",\n", " logging_steps=10,\n", " save_steps=100,\n", " learning_rate=1e-4,\n", " num_train_epochs=5.0,\n", " max_samples=500,\n", " max_grad_norm=1.0,\n", " fp16=True,\n", "))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "f95ce7e75ebc4e20905fed28cad2f5c3", "2ec65871301e461abaeaa378b971530b", "91b90a9bd5a24705b922afedf691f2b8", "d816934e6db644eea770054078c2147c", 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"0ca77f0a650446db9d52cf228d7cd610", "54f227c080b340e6a10fc86823dcae7f", "cdc60571f14a40e89bfdb894973eb408", "900a50758c5d4c82993d65ef940a24d7", "0a1b5a63a6da42a6a4d9145d6782d3a0", "afe4c5d8e9694ddd838e622bcbdb7b76", "ce8389a7a56e4229b548da25de6d3dfd", "13a6d6e2c85e497f99dab8f0e90796c0", "e2d2c569765140bbb92adc8dea169d95", "9169e2eb72fa4d949b1ad31e18851fc1", "78c1753419d14545a1f23b64f21df6cd", "63e53148010642e18b4af6f7ca566ed2", "9f73a969648d4636882dcbc7349d8b25", "5b3ae5ea41fc4bb6884c881c993fddcc", "1090cb2ed1c74dbc818bf64f8f89e761", "0a275e74c45d471ba6efce60f6170b3a", "b23d2e0455774b1ba42db3ac1bad9c69", "de0d22ef70e8490286cd3cb35a23bd9c", "9aa8d77e15eb4d549ec9d2579486528c", "1960bc2f403d4d4e9ca0c49c5b191ff0", "144ddbb6ed7745348cda9fe695269b0d", "3e7e570583b548bbb84a1c8b290f2234", "7d78cba869fe469c839d11b54180d9a5", "f1f0a8a751c44294900c997175a5e052", "bb22ba5411684a9babfe752dd00d0ea6", "7ef769cb6a3646e4b058eace7866b32b", "f2d3c9fdf75a4b2d8db817293cf46af5", "cd4e951c119f406c93221f9bec663792", "fcad30d4033f4c64a0a8efeb7feabd91", "6c8fc51a084444d8ab98b7ec58105ce5", "169366de76ba4cf4ac7f61fc90494aa1", "eff454fe170c4a4283301ede36d1c77c", "c97b3bba56c34fb196f4776ce9469738", "b058e907e5f2450dae4c4c23d12cb09b", "8fef758e915c433bb1f2101601d24f7d", "595362c21c804a80a05102d069502e45", "f3dd024cf93348f99300f66c6ed74426", "64d9b7b9bead4c63a79850f812a65725", "e2077ca32a48446ab8a598cfd24560ac", "e03f20206724468ba155dde1a5e4c84f", "5c28873d8a7548deb1cfe1d4f980ad4b", "90c0eb3147074d8798ad2ccfa4eb9dd3", "0a9a4e707f604881873acc0f4d7a42c0", "76cf1a5a96e140459d2de7d881144f88", "5e08f61a9fc44792bd3a43116e38d804", "fcca954f131d4fe1b047f999df5ea5a3", "bd521cdb23e84164b76a844037b5e202", "1911489caa474fc7aeede1e94cf673b2", "e43f16a9a7eb4396a73e47dbe86d86dd", "8ada8f039d38464b9ee5c9fc19d58d86", "8a56009da9114d7c89b28196eda64fde", "7df19c3a808144a99c366c157630a227", "3311434267ca41a6980c903b63e1cb9b", "ffa60fc9bc164af2b79bd6212dcb46a0", "0174ba072a7b4d0aae5c81a71f021f56", "313b26430be34cf79cffcadf2a25b804", "6f8f413a8286463097ae9c6f4abe6018", "43ed32f70e834038a88e60bad7ee312d", "a26d44fa1e5f4b7bbdd8031bc5399240", "e78c7a6ad68d4f418581e6dddfb1d299", "713ef5f962a94e4fbdb59b7ff44e532b", "04bd33cea6cd49cf9c723a26de46c26c", "f10ae252772c42f6ab84e517967922cb", "b86d1631f0494048b3c45a8278dae598", "9638f2fe85e4416195fad7be5ea51e28", "320520e310914745a7a77cbfc41e856a", "bd29d07316d141b7afb8971ebc468668", "73e1f901f3be40159693451332e52fe1", "6f7241c2ce2140b3b59bd2d7b8db5276" ] }, "id": "CS0Qk5OR0i4Q", "outputId": "79a2419f-3d71-4ff2-b935-4a6cbaf9418d" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "[INFO|training_args.py:1902] 2024-04-07 15:17:58,474 >> PyTorch: setting up devices\n", "[INFO|training_args.py:1611] 2024-04-07 15:17:58,514 >> The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "04/07/2024 15:17:58 - INFO - llmtuner.hparams.parser - Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: False, compute dtype: torch.float16\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "INFO:llmtuner.hparams.parser:Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: False, compute dtype: torch.float16\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/967 [00:00> loading file tokenizer.model from cache at /root/.cache/huggingface/hub/models--mistralai--Mistral-7B-v0.1/snapshots/26bca36bde8333b5d7f72e9ed20ccda6a618af24/tokenizer.model\n", "[INFO|tokenization_utils_base.py:2046] 2024-04-07 15:18:05,098 >> loading file added_tokens.json from cache at None\n", "[INFO|tokenization_utils_base.py:2046] 2024-04-07 15:18:05,100 >> loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--mistralai--Mistral-7B-v0.1/snapshots/26bca36bde8333b5d7f72e9ed20ccda6a618af24/special_tokens_map.json\n", "[INFO|tokenization_utils_base.py:2046] 2024-04-07 15:18:05,102 >> loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--mistralai--Mistral-7B-v0.1/snapshots/26bca36bde8333b5d7f72e9ed20ccda6a618af24/tokenizer_config.json\n", "[INFO|tokenization_utils_base.py:2046] 2024-04-07 15:18:05,103 >> loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--mistralai--Mistral-7B-v0.1/snapshots/26bca36bde8333b5d7f72e9ed20ccda6a618af24/tokenizer.json\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "04/07/2024 15:18:05 - INFO - llmtuner.data.template - Add pad token: \n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "INFO:llmtuner.data.template:Add pad token: \n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "04/07/2024 15:18:05 - INFO - llmtuner.data.loader - Loading dataset oaast_sft.json...\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "INFO:llmtuner.data.loader:Loading dataset oaast_sft.json...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Generating train split: 0 examples [00:00, ? examples/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "9d445d452abf4bddb831eecaddb39304" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Converting format of dataset: 0%| | 0/500 [00:00 [INST] Can you write a short introduction about the relevance of the term \"monopsony\" in economics? Please use examples related to potential monopsonies in the labour market and cite relevant research. [/INST] Monopsony refers to a market structure where there is only one buyer of a good or service. In the context of labor markets, a monopsony occurs when there is only one employer in a particular geographic area or industry, giving them significant power to set wages and working conditions. Research has shown that the existence of monopsonies can lead to lower wages and reduced job opportunities for workers. For example, a study by the National Bureau of Economic Research found that in industries with high levels of concentration, workers earn lower wages and are less likely to receive benefits such as health insurance. [INST] What can be done at a regulatory level to ensure, in a monopsony, that the power over the employees isn't abused? List a few options and focus on the organs that should act. [/INST] Here are some potential regulatory options to address monopsony power over employees:\n", "\n", "- Antitrust enforcement agencies like the FTC or DOJ could investigate and take action against dominant employers that are abusing their market power to suppress wages or worsen working conditions. They could force divestitures or other structural remedies.\n", "- Labor standards agencies could set industry-specific minimum wage floors or other regulations around compensation and working conditions. This would reduce the ability of dominant employers to push wages/conditions below certain thresholds.\n", "- Sector-specific regulators could impose requirements around how companies hire, compensate, and treat workers. For example, banking regulators could prohibit abusive practices by dominant banks with regard to their employees.\n", "- Legislatures could pass laws\n", "label_ids:\n", "[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 3217, 410, 1265, 28724, 15654, 298, 264, 2668, 4693, 970, 736, 349, 865, 624, 24861, 302, 264, 1179, 442, 2372, 28723, 560, 272, 2758, 302, 7579, 11574, 28725, 264, 1326, 410, 1265, 28724, 12022, 739, 736, 349, 865, 624, 18646, 297, 264, 2830, 2970, 12293, 2698, 442, 4779, 28725, 5239, 706, 5864, 1982, 298, 808, 24110, 304, 2739, 4331, 28723, 7982, 659, 4894, 369, 272, 9025, 302, 1326, 410, 1265, 497, 541, 1736, 298, 3889, 24110, 304, 9397, 2389, 8812, 354, 7433, 28723, 1263, 2757, 28725, 264, 3881, 486, 272, 3610, 18052, 302, 20451, 7982, 1419, 369, 297, 17909, 395, 1486, 6157, 302, 15987, 28725, 7433, 6384, 3889, 24110, 304, 460, 2108, 3917, 298, 5556, 7196, 1259, 390, 2528, 6919, 28723, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 4003, 460, 741, 4628, 25251, 2877, 298, 2962, 1326, 410, 1265, 28724, 1982, 754, 7896, 28747, 13, 13, 28733, 3821, 279, 19842, 19046, 15467, 737, 272, 401, 8386, 442, 9317, 28798, 829, 17214, 304, 1388, 2992, 1835, 18669, 22271, 369, 460, 534, 8521, 652, 2668, 1982, 298, 22555, 24110, 442, 275, 734, 269, 2739, 4331, 28723, 1306, 829, 4274, 2901, 374, 279, 1238, 442, 799, 21431, 1003, 286, 497, 28723, 13, 28733, 14160, 9890, 15467, 829, 808, 4779, 28733, 15590, 7968, 21062, 21264, 442, 799, 15885, 1401, 19204, 304, 2739, 4331, 28723, 851, 682, 7643, 272, 5537, 302, 18669, 22271, 298, 5696, 24110, 28748, 24952, 3624, 2552, 306, 2874, 4858, 28723, 13, 28733, 318, 3776, 28733, 15590, 15991, 3117, 829, 2824, 645, 8296, 1401, 910, 4799, 15270, 28725, 12613, 380, 28725, 304, 3363, 7433, 28723, 1263, 2757, 28725, 22265, 15991, 3117, 829, 16128, 279, 534, 6657, 10879, 486, 18669, 13283, 395, 4166, 298, 652, 7896, 28723, 13, 28733, 26216, 2863, 829, 1455, 8427, 2]\n", "labels:\n", " Monopsony refers to a market structure where there is only one buyer of a good or service. In the context of labor markets, a monopsony occurs when there is only one employer in a particular geographic area or industry, giving them significant power to set wages and working conditions. Research has shown that the existence of monopsonies can lead to lower wages and reduced job opportunities for workers. For example, a study by the National Bureau of Economic Research found that in industries with high levels of concentration, workers earn lower wages and are less likely to receive benefits such as health insurance. Here are some potential regulatory options to address monopsony power over employees:\n", "\n", "- Antitrust enforcement agencies like the FTC or DOJ could investigate and take action against dominant employers that are abusing their market power to suppress wages or worsen working conditions. They could force divestitures or other structural remedies.\n", "- Labor standards agencies could set industry-specific minimum wage floors or other regulations around compensation and working conditions. This would reduce the ability of dominant employers to push wages/conditions below certain thresholds.\n", "- Sector-specific regulators could impose requirements around how companies hire, compensate, and treat workers. For example, banking regulators could prohibit abusive practices by dominant banks with regard to their employees.\n", "- Legislatures could pass laws\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/571 [00:00> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--mistralai--Mistral-7B-v0.1/snapshots/26bca36bde8333b5d7f72e9ed20ccda6a618af24/config.json\n", "[INFO|configuration_utils.py:791] 2024-04-07 15:18:08,511 >> Model config MistralConfig {\n", " \"_name_or_path\": \"mistralai/Mistral-7B-v0.1\",\n", " \"architectures\": [\n", " \"MistralForCausalLM\"\n", " ],\n", " \"attention_dropout\": 0.0,\n", " \"bos_token_id\": 1,\n", " \"eos_token_id\": 2,\n", " \"hidden_act\": \"silu\",\n", " \"hidden_size\": 4096,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 14336,\n", " \"max_position_embeddings\": 32768,\n", " \"model_type\": \"mistral\",\n", " \"num_attention_heads\": 32,\n", " \"num_hidden_layers\": 32,\n", " \"num_key_value_heads\": 8,\n", " \"rms_norm_eps\": 1e-05,\n", " \"rope_theta\": 10000.0,\n", " \"sliding_window\": 4096,\n", " \"tie_word_embeddings\": false,\n", " \"torch_dtype\": \"bfloat16\",\n", " \"transformers_version\": \"4.38.2\",\n", " \"use_cache\": true,\n", " \"vocab_size\": 32000\n", "}\n", "\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "model.safetensors.index.json: 0%| | 0.00/25.1k [00:00> loading weights file model.safetensors from cache at /root/.cache/huggingface/hub/models--mistralai--Mistral-7B-v0.1/snapshots/26bca36bde8333b5d7f72e9ed20ccda6a618af24/model.safetensors.index.json\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Downloading shards: 0%| | 0/2 [00:00> Instantiating MistralForCausalLM model under default dtype torch.float16.\n", "[INFO|configuration_utils.py:845] 2024-04-07 15:19:40,392 >> Generate config GenerationConfig {\n", " \"bos_token_id\": 1,\n", " \"eos_token_id\": 2\n", "}\n", "\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading checkpoint shards: 0%| | 0/2 [00:00> All model checkpoint weights were used when initializing MistralForCausalLM.\n", "\n", "[INFO|modeling_utils.py:4000] 2024-04-07 15:20:51,823 >> All the weights of MistralForCausalLM were initialized from the model checkpoint at mistralai/Mistral-7B-v0.1.\n", "If your task is similar to the task the model of the checkpoint was trained on, you can already use MistralForCausalLM for predictions without further training.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "generation_config.json: 0%| | 0.00/116 [00:00> loading configuration file generation_config.json from cache at /root/.cache/huggingface/hub/models--mistralai--Mistral-7B-v0.1/snapshots/26bca36bde8333b5d7f72e9ed20ccda6a618af24/generation_config.json\n", "[INFO|configuration_utils.py:845] 2024-04-07 15:20:52,254 >> Generate config GenerationConfig {\n", " \"bos_token_id\": 1,\n", " \"eos_token_id\": 2\n", "}\n", "\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "04/07/2024 15:20:52 - INFO - llmtuner.model.patcher - Gradient checkpointing enabled.\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "INFO:llmtuner.model.patcher:Gradient checkpointing enabled.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "04/07/2024 15:20:52 - INFO - llmtuner.model.adapter - Fine-tuning method: LoRA\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "INFO:llmtuner.model.adapter:Fine-tuning method: LoRA\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "04/07/2024 15:20:52 - INFO - llmtuner.model.utils - Found linear modules: k_proj,v_proj,gate_proj,up_proj,down_proj,o_proj,q_proj\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "INFO:llmtuner.model.utils:Found linear modules: k_proj,v_proj,gate_proj,up_proj,down_proj,o_proj,q_proj\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "04/07/2024 15:20:53 - INFO - llmtuner.model.loader - trainable params: 20971520 || all params: 7262703616 || trainable%: 0.2888\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "INFO:llmtuner.model.loader:trainable params: 20971520 || all params: 7262703616 || trainable%: 0.2888\n", "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:436: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead: \n", "dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)\n", " warnings.warn(\n", "[INFO|trainer.py:601] 2024-04-07 15:20:53,146 >> Using auto half precision backend\n", "[INFO|trainer.py:1812] 2024-04-07 15:20:53,559 >> ***** Running training *****\n", "[INFO|trainer.py:1813] 2024-04-07 15:20:53,560 >> Num examples = 500\n", "[INFO|trainer.py:1814] 2024-04-07 15:20:53,563 >> Num Epochs = 5\n", "[INFO|trainer.py:1815] 2024-04-07 15:20:53,565 >> Instantaneous batch size per device = 4\n", "[INFO|trainer.py:1818] 2024-04-07 15:20:53,566 >> Total train batch size (w. parallel, distributed & accumulation) = 16\n", "[INFO|trainer.py:1819] 2024-04-07 15:20:53,569 >> Gradient Accumulation steps = 4\n", "[INFO|trainer.py:1820] 2024-04-07 15:20:53,572 >> Total optimization steps = 155\n", "[INFO|trainer.py:1821] 2024-04-07 15:20:53,583 >> Number of trainable parameters = 20,971,520\n" ] }, { "output_type": "error", "ename": "OutOfMemoryError", "evalue": "CUDA out of memory. Tried to allocate 88.00 MiB. GPU 0 has a total capacity of 14.75 GiB of which 73.06 MiB is free. Process 7257 has 14.67 GiB memory in use. Of the allocated memory 14.29 GiB is allocated by PyTorch, and 263.18 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mOutOfMemoryError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mllmtuner\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrun_exp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m run_exp(dict(\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mstage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"sft\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdo_train\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m 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Tried to allocate 88.00 MiB. GPU 0 has a total capacity of 14.75 GiB of which 73.06 MiB is free. Process 7257 has 14.67 GiB memory in use. Of the allocated memory 14.29 GiB is allocated by PyTorch, and 263.18 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)" ] } ] }, { "cell_type": "markdown", "source": [ "### Infer the fine-tuned model" ], "metadata": { "id": "PVNaC-xS5N40" } }, { "cell_type": "code", "source": [ "from llmtuner import ChatModel\n", "chat_model = ChatModel(dict(\n", " model_name_or_path=\"Qwen/Qwen1.5-0.5B-Chat\",\n", " adapter_name_or_path=\"test_identity\",\n", " finetuning_type=\"lora\",\n", " template=\"qwen\",\n", "))\n", "messages = []\n", "while True:\n", " query = input(\"\\nUser: \")\n", " if query.strip() == \"exit\":\n", " break\n", " if query.strip() == \"clear\":\n", " messages = []\n", " continue\n", "\n", " messages.append({\"role\": \"user\", \"content\": query})\n", " print(\"Assistant: \", end=\"\", flush=True)\n", " response = \"\"\n", " for new_text in chat_model.stream_chat(messages):\n", " print(new_text, end=\"\", flush=True)\n", " response += new_text\n", " print()\n", " messages.append({\"role\": \"assistant\", \"content\": response})" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "oh8H9A_25SF9", "outputId": "6730e9db-584a-4485-8b3b-c596749df84a" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "[INFO|tokenization_utils_base.py:2046] 2024-03-02 12:03:09,835 >> loading file vocab.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen1.5-0.5B-Chat/snapshots/6c705984bb8b5591dd4e1a9e66e1a127965fd08d/vocab.json\n", "[INFO|tokenization_utils_base.py:2046] 2024-03-02 12:03:09,837 >> loading file merges.txt from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen1.5-0.5B-Chat/snapshots/6c705984bb8b5591dd4e1a9e66e1a127965fd08d/merges.txt\n", "[INFO|tokenization_utils_base.py:2046] 2024-03-02 12:03:09,839 >> loading file added_tokens.json from cache at None\n", "[INFO|tokenization_utils_base.py:2046] 2024-03-02 12:03:09,841 >> loading file special_tokens_map.json from cache at None\n", "[INFO|tokenization_utils_base.py:2046] 2024-03-02 12:03:09,843 >> loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen1.5-0.5B-Chat/snapshots/6c705984bb8b5591dd4e1a9e66e1a127965fd08d/tokenizer_config.json\n", "[INFO|tokenization_utils_base.py:2046] 2024-03-02 12:03:09,845 >> loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen1.5-0.5B-Chat/snapshots/6c705984bb8b5591dd4e1a9e66e1a127965fd08d/tokenizer.json\n", "[WARNING|logging.py:314] 2024-03-02 12:03:10,117 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n", "[INFO|configuration_utils.py:728] 2024-03-02 12:03:10,188 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen1.5-0.5B-Chat/snapshots/6c705984bb8b5591dd4e1a9e66e1a127965fd08d/config.json\n", "[INFO|configuration_utils.py:791] 2024-03-02 12:03:10,191 >> Model config Qwen2Config {\n", " \"_name_or_path\": \"Qwen/Qwen1.5-0.5B-Chat\",\n", " \"architectures\": [\n", " \"Qwen2ForCausalLM\"\n", " ],\n", " \"attention_dropout\": 0.0,\n", " \"bos_token_id\": 151643,\n", " \"eos_token_id\": 151645,\n", " \"hidden_act\": \"silu\",\n", " \"hidden_size\": 1024,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 2816,\n", " \"max_position_embeddings\": 32768,\n", " \"max_window_layers\": 21,\n", " \"model_type\": \"qwen2\",\n", " \"num_attention_heads\": 16,\n", " \"num_hidden_layers\": 24,\n", " \"num_key_value_heads\": 16,\n", " \"rms_norm_eps\": 1e-06,\n", " \"rope_theta\": 1000000.0,\n", " \"sliding_window\": 32768,\n", " \"tie_word_embeddings\": true,\n", " \"torch_dtype\": \"bfloat16\",\n", " \"transformers_version\": \"4.38.1\",\n", " \"use_cache\": true,\n", " \"use_sliding_window\": false,\n", " \"vocab_size\": 151936\n", "}\n", "\n", "[INFO|modeling_utils.py:3257] 2024-03-02 12:03:10,196 >> loading weights file model.safetensors from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen1.5-0.5B-Chat/snapshots/6c705984bb8b5591dd4e1a9e66e1a127965fd08d/model.safetensors\n", "[INFO|modeling_utils.py:1400] 2024-03-02 12:03:10,216 >> Instantiating Qwen2ForCausalLM model under default dtype torch.float16.\n", "[INFO|configuration_utils.py:845] 2024-03-02 12:03:10,221 >> Generate config GenerationConfig {\n", " \"bos_token_id\": 151643,\n", " \"eos_token_id\": 151645\n", "}\n", "\n", "[INFO|modeling_utils.py:3992] 2024-03-02 12:03:12,983 >> All model checkpoint weights were used when initializing Qwen2ForCausalLM.\n", "\n", "[INFO|modeling_utils.py:4000] 2024-03-02 12:03:12,984 >> All the weights of Qwen2ForCausalLM were initialized from the model checkpoint at Qwen/Qwen1.5-0.5B-Chat.\n", "If your task is similar to the task the model of the checkpoint was trained on, you can already use Qwen2ForCausalLM for predictions without further training.\n", "[INFO|configuration_utils.py:800] 2024-03-02 12:03:13,059 >> loading configuration file generation_config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen1.5-0.5B-Chat/snapshots/6c705984bb8b5591dd4e1a9e66e1a127965fd08d/generation_config.json\n", "[INFO|configuration_utils.py:845] 2024-03-02 12:03:13,061 >> Generate config GenerationConfig {\n", " \"bos_token_id\": 151643,\n", " \"do_sample\": true,\n", " \"eos_token_id\": [\n", " 151645,\n", " 151643\n", " ],\n", " \"pad_token_id\": 151643,\n", " \"repetition_penalty\": 1.1,\n", " \"top_p\": 0.8\n", "}\n", "\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "03/02/2024 12:03:13 - INFO - llmtuner.model.adapter - Fine-tuning method: LoRA\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "INFO:llmtuner.model.adapter:Fine-tuning method: LoRA\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "03/02/2024 12:03:14 - INFO - llmtuner.model.adapter - Merged 1 adapter(s).\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "INFO:llmtuner.model.adapter:Merged 1 adapter(s).\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "03/02/2024 12:03:14 - INFO - llmtuner.model.adapter - Loaded adapter(s): test_identity\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "INFO:llmtuner.model.adapter:Loaded adapter(s): test_identity\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "03/02/2024 12:03:14 - INFO - llmtuner.model.loader - all params: 463987712\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "INFO:llmtuner.model.loader:all params: 463987712\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "03/02/2024 12:03:14 - INFO - llmtuner.data.template - Replace eos token: <|im_end|>\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "INFO:llmtuner.data.template:Replace eos token: <|im_end|>\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "User: hi\n", "Assistant: Hello! How can I help you today?\n", "\n", "User: who are you\n", "Assistant: I am NAME, an AI assistant developed by AUTHOR. I am here to help you with your queries and provide you with the best possible responses.\n", "\n", "User: give three tips for staying healthy\n", "Assistant: 1. Stay hydrated: Drink plenty of water throughout the day to stay hydrated and keep your body functioning at its best.\n", "2. Exercise regularly: Exercise is one of the best ways to stay healthy. Aim for at least 30 minutes of moderate-intensity exercise most days of the week.\n", "3. Eat a healthy diet: Eating a balanced and nutritious diet is crucial for maintaining good health. Focus on whole, unprocessed foods, plenty of fruits, vegetables, and lean proteins.\n", "\n", "User: exit\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Merge LoRA weights" ], "metadata": { "id": "flmc2i3Z7Bl7" } }, { "cell_type": "code", "source": [ "from llmtuner import export_model\n", "export_model(dict(\n", " model_name_or_path=\"mistralai/Mistral-7B-v0.1\",\n", " adapter_name_or_path=\"Artples/LAI-Paca-7b\",\n", " finetuning_type=\"lora\",\n", " template=\"mistral\",\n", " export_dir=\"test_exported\",\n", " export_hub_model_id=\"Artples/LAI-Paca-7b-Merged\",\n", "))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "775689b53d674875af95dacb5a804c1d", "83ac0f982b664dd7ab1bf4003b649f7c", "93385c2627614f39a8cdaef41770d5dc", "6a68f4f93136476ba5dda0240ca5239a", "a52bd50efc7c4a51ba349df39ca8934e", "4c3db643b3cf49ba8faba63e21d1aa42", "c1b46640ed8f453292dde7f7bc0be403", "f45df54413f543caa78f8f44474d600c", "957f211021b043d0a8da929b5b3e92c4", "63b452a4cbc546f0a6f956e2a5f5c0b9", "5f7f24bf6b0b4797b019e8122d37fffe", "2542ffb4be9846888c31c3b28d1fc8ee", "36125ba0485d4736896dbe41b5332273", "55a3d7ba21984835b0ccbeacb00cd74b", "ce80e04267264ca295383372b23d756e", "8bd42cd71c55476dbc94e4e3b1cbcf67", "a928892a06694dd09e61b2c6db9707b3", "422a58f5fa7646ed9003810b846d7868", "feff6dd972f842bf99c7f907971175a1", "8428dd401aac4812af6de9a840f3fbc8", "c54bf007b00143b4b21232d940043adc", "f05412ea9aca4e158fc02755ca47f1b2", "818c371aa671498397a3a4c16e737f07", "b8707b2d77314eadba4ff5d77d12fead", "36c8c7b46fa74ea191e1b863b6b67773", "61ee25e87bbe4e8abcab805c97f3ac68", "facd485690bd4aaab13fef6568fb03ae", "78a7c76072bd4090976ddc2b7de874d8", "9e1ac20517484497ae9a0ea565093858", "03684e6c259f46c684efe940626be82c", "8ca6e75fec51431eb1934c29c6c1b45f", "f7b4a6ea97c641cbbef74f26bbe132e1", "3c6097f3850a4e7bb282ac4cfd0a5db3" ] }, "id": "g0fVaJsj7GC-", "outputId": "1c670ce5-1661-484c-8b69-14842cd83220" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "[INFO|tokenization_utils_base.py:2046] 2024-04-07 15:22:20,992 >> loading file tokenizer.model from cache at /root/.cache/huggingface/hub/models--mistralai--Mistral-7B-v0.1/snapshots/26bca36bde8333b5d7f72e9ed20ccda6a618af24/tokenizer.model\n", "[INFO|tokenization_utils_base.py:2046] 2024-04-07 15:22:20,993 >> loading file added_tokens.json from cache at None\n", "[INFO|tokenization_utils_base.py:2046] 2024-04-07 15:22:20,996 >> loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--mistralai--Mistral-7B-v0.1/snapshots/26bca36bde8333b5d7f72e9ed20ccda6a618af24/special_tokens_map.json\n", "[INFO|tokenization_utils_base.py:2046] 2024-04-07 15:22:20,998 >> loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--mistralai--Mistral-7B-v0.1/snapshots/26bca36bde8333b5d7f72e9ed20ccda6a618af24/tokenizer_config.json\n", "[INFO|tokenization_utils_base.py:2046] 2024-04-07 15:22:20,999 >> loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--mistralai--Mistral-7B-v0.1/snapshots/26bca36bde8333b5d7f72e9ed20ccda6a618af24/tokenizer.json\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "04/07/2024 15:22:21 - INFO - llmtuner.data.template - Add pad token: \n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "INFO:llmtuner.data.template:Add pad token: \n", "[INFO|configuration_utils.py:728] 2024-04-07 15:22:21,159 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--mistralai--Mistral-7B-v0.1/snapshots/26bca36bde8333b5d7f72e9ed20ccda6a618af24/config.json\n", "[INFO|configuration_utils.py:791] 2024-04-07 15:22:21,161 >> Model config MistralConfig {\n", " \"_name_or_path\": \"mistralai/Mistral-7B-v0.1\",\n", " \"architectures\": [\n", " \"MistralForCausalLM\"\n", " ],\n", " \"attention_dropout\": 0.0,\n", " \"bos_token_id\": 1,\n", " \"eos_token_id\": 2,\n", " \"hidden_act\": \"silu\",\n", " \"hidden_size\": 4096,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 14336,\n", " \"max_position_embeddings\": 32768,\n", " \"model_type\": \"mistral\",\n", " \"num_attention_heads\": 32,\n", " \"num_hidden_layers\": 32,\n", " \"num_key_value_heads\": 8,\n", " \"rms_norm_eps\": 1e-05,\n", " \"rope_theta\": 10000.0,\n", " \"sliding_window\": 4096,\n", " \"tie_word_embeddings\": false,\n", " \"torch_dtype\": \"bfloat16\",\n", " \"transformers_version\": \"4.38.2\",\n", " \"use_cache\": true,\n", " \"vocab_size\": 32000\n", "}\n", "\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "04/07/2024 15:22:21 - INFO - llmtuner.model.patcher - Using KV cache for faster generation.\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "INFO:llmtuner.model.patcher:Using KV cache for faster generation.\n", "[INFO|modeling_utils.py:3257] 2024-04-07 15:22:21,169 >> loading weights file model.safetensors from cache at /root/.cache/huggingface/hub/models--mistralai--Mistral-7B-v0.1/snapshots/26bca36bde8333b5d7f72e9ed20ccda6a618af24/model.safetensors.index.json\n", "[INFO|modeling_utils.py:1400] 2024-04-07 15:22:21,173 >> Instantiating MistralForCausalLM model under default dtype torch.float16.\n", "[INFO|configuration_utils.py:845] 2024-04-07 15:22:21,176 >> Generate config GenerationConfig {\n", " \"bos_token_id\": 1,\n", " \"eos_token_id\": 2\n", "}\n", "\n", "/usr/local/lib/python3.10/dist-packages/accelerate/utils/modeling.py:1363: UserWarning: Current model requires 536875008 bytes of buffer for offloaded layers, which seems does not fit any GPU's remaining memory. If you are experiencing a OOM later, please consider using offload_buffers=True.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading checkpoint shards: 0%| | 0/2 [00:00> All model checkpoint weights were used when initializing MistralForCausalLM.\n", "\n", "[INFO|modeling_utils.py:4000] 2024-04-07 15:23:06,331 >> All the weights of MistralForCausalLM were initialized from the model checkpoint at mistralai/Mistral-7B-v0.1.\n", "If your task is similar to the task the model of the checkpoint was trained on, you can already use MistralForCausalLM for predictions without further training.\n", "[INFO|configuration_utils.py:800] 2024-04-07 15:23:06,435 >> loading configuration file generation_config.json from cache at /root/.cache/huggingface/hub/models--mistralai--Mistral-7B-v0.1/snapshots/26bca36bde8333b5d7f72e9ed20ccda6a618af24/generation_config.json\n", "[INFO|configuration_utils.py:845] 2024-04-07 15:23:06,438 >> Generate config GenerationConfig {\n", " \"bos_token_id\": 1,\n", " \"eos_token_id\": 2\n", "}\n", "\n", "WARNING:root:Some parameters are on the meta device device because they were offloaded to the cpu and disk.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "04/07/2024 15:23:06 - INFO - llmtuner.model.adapter - Fine-tuning method: LoRA\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "INFO:llmtuner.model.adapter:Fine-tuning method: LoRA\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "adapter_config.json: 0%| | 0.00/728 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mllmtuner\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mexport_model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m export_model(dict(\n\u001b[0m\u001b[1;32m 3\u001b[0m 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111\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffload_folder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel_args\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moffload_folder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 112\u001b[0m )\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/peft_model.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, model, model_id, adapter_name, is_trainable, config, **kwargs)\u001b[0m\n\u001b[1;32m 354\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 355\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMODEL_TYPE_TO_PEFT_MODEL_MAPPING\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtask_type\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m 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**kwargs)\u001b[0m\n\u001b[1;32m 725\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_adapter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madapter_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpeft_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 726\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m \u001b[0madapters_weights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_peft_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch_device\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mhf_hub_download_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 729\u001b[0m \u001b[0;31m# load the weights into the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 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Tried to allocate 2.00 MiB. GPU 0 has a total capacity of 14.75 GiB of which 1.06 MiB is free. Process 7257 has 14.74 GiB memory in use. Of the allocated memory 14.36 GiB is allocated by PyTorch, and 263.37 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)" ] } ] } ] }