Fine_tune.ipynb

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            "Collecting autotrain-advanced\n",
            "  Downloading autotrain_advanced-0.6.44-py3-none-any.whl (131 kB)\n",
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            "\u001b[?25hCollecting sacremoses==0.0.53 (from autotrain-advanced)\n",
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            "Collecting scikit-learn==1.3.0 (from autotrain-advanced)\n",
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            "\u001b[?25hCollecting diffusers==0.21.4 (from autotrain-advanced)\n",
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            "Collecting h11>=0.8 (from uvicorn>=0.14.0->gradio==3.41.0->autotrain-advanced)\n",
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            "\u001b[?25hCollecting types-python-dateutil>=2.8.10 (from arrow->codecarbon==2.2.3->autotrain-advanced)\n",
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            "\u001b[?25hCollecting typing-extensions~=4.0 (from gradio==3.41.0->autotrain-advanced)\n",
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            "Building wheels for collected packages: ipadic, rouge-score, sacremoses, ffmpy\n",
            "  Building wheel for ipadic (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ipadic: filename=ipadic-1.0.0-py3-none-any.whl size=13556704 sha256=aceb5478945528665db16af0bd9a1327f6432b8ef6919310841831fdac4151f2\n",
            "  Stored in directory: /root/.cache/pip/wheels/5b/ea/e3/2f6e0860a327daba3b030853fce4483ed37468bbf1101c59c3\n",
            "  Building wheel for rouge-score (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for rouge-score: filename=rouge_score-0.1.2-py3-none-any.whl size=24933 sha256=be22a9467e3a2368559ee0eda3640299f08902916a7da126ad30cd02c7098216\n",
            "  Stored in directory: /root/.cache/pip/wheels/5f/dd/89/461065a73be61a532ff8599a28e9beef17985c9e9c31e541b4\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.53-py3-none-any.whl size=895241 sha256=964ecd5abf1180cf7a4cf0efb2c7ba3c26a3ab420cf5848ca602af2b2cdcd56d\n",
            "  Stored in directory: /root/.cache/pip/wheels/00/24/97/a2ea5324f36bc626e1ea0267f33db6aa80d157ee977e9e42fb\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ffmpy: filename=ffmpy-0.3.1-py3-none-any.whl size=5579 sha256=47e118107453e2e8cc6c7f812481eec579a0487cc8e8c3607677efaebbcfe471\n",
            "  Stored in directory: /root/.cache/pip/wheels/01/a6/d1/1c0828c304a4283b2c1639a09ad86f83d7c487ef34c6b4a1bf\n",
            "Successfully built ipadic rouge-score sacremoses ffmpy\n",
            "Installing collected packages: types-python-dateutil, texttable, sentencepiece, pydub, ipadic, fuzzywuzzy, ffmpy, brotli, bitsandbytes, werkzeug, websockets, typing-extensions, tqdm, shtab, semantic-version, rapidfuzz, pyzstd, python-multipart, pyppmd, pynvml, pycryptodomex, pybcj, pyarrow-hotfix, protobuf, Pillow, packaging, orjson, multivolumefile, Mako, loguru, joblib, inflate64, h11, einops, docstring-parser, dill, colorlog, cmaes, aiofiles, xgboost, uvicorn, tiktoken, starlette, scikit-learn, sacremoses, responses, pydantic, py7zr, multiprocess, jiwer, huggingface-hub, httpcore, arrow, tyro, tokenizers, rouge-score, invisible-watermark, httpx, fastapi, diffusers, codecarbon, alembic, accelerate, transformers, optuna, gradio-client, datasets, trl, peft, gradio, evaluate, autotrain-advanced\n",
            "  Attempting uninstall: werkzeug\n",
            "    Found existing installation: Werkzeug 3.0.1\n",
            "    Uninstalling Werkzeug-3.0.1:\n",
            "      Successfully uninstalled Werkzeug-3.0.1\n",
            "  Attempting uninstall: typing-extensions\n",
            "    Found existing installation: typing_extensions 4.5.0\n",
            "    Uninstalling typing_extensions-4.5.0:\n",
            "      Successfully uninstalled typing_extensions-4.5.0\n",
            "  Attempting uninstall: tqdm\n",
            "    Found existing installation: tqdm 4.66.1\n",
            "    Uninstalling tqdm-4.66.1:\n",
            "      Successfully uninstalled tqdm-4.66.1\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 3.20.3\n",
            "    Uninstalling protobuf-3.20.3:\n",
            "      Successfully uninstalled protobuf-3.20.3\n",
            "  Attempting uninstall: Pillow\n",
            "    Found existing installation: Pillow 9.4.0\n",
            "    Uninstalling Pillow-9.4.0:\n",
            "      Successfully uninstalled Pillow-9.4.0\n",
            "  Attempting uninstall: packaging\n",
            "    Found existing installation: packaging 23.2\n",
            "    Uninstalling packaging-23.2:\n",
            "      Successfully uninstalled packaging-23.2\n",
            "  Attempting uninstall: joblib\n",
            "    Found existing installation: joblib 1.3.2\n",
            "    Uninstalling joblib-1.3.2:\n",
            "      Successfully uninstalled joblib-1.3.2\n",
            "  Attempting uninstall: xgboost\n",
            "    Found existing installation: xgboost 2.0.2\n",
            "    Uninstalling xgboost-2.0.2:\n",
            "      Successfully uninstalled xgboost-2.0.2\n",
            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 1.2.2\n",
            "    Uninstalling scikit-learn-1.2.2:\n",
            "      Successfully uninstalled scikit-learn-1.2.2\n",
            "  Attempting uninstall: pydantic\n",
            "    Found existing installation: pydantic 1.10.13\n",
            "    Uninstalling pydantic-1.10.13:\n",
            "      Successfully uninstalled pydantic-1.10.13\n",
            "  Attempting uninstall: huggingface-hub\n",
            "    Found existing installation: huggingface-hub 0.19.4\n",
            "    Uninstalling huggingface-hub-0.19.4:\n",
            "      Successfully uninstalled huggingface-hub-0.19.4\n",
            "  Attempting uninstall: tokenizers\n",
            "    Found existing installation: tokenizers 0.15.0\n",
            "    Uninstalling tokenizers-0.15.0:\n",
            "      Successfully uninstalled tokenizers-0.15.0\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.35.2\n",
            "    Uninstalling transformers-4.35.2:\n",
            "      Successfully uninstalled transformers-4.35.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "llmx 0.0.15a0 requires cohere, which is not installed.\n",
            "llmx 0.0.15a0 requires openai, which is not installed.\n",
            "tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 4.23.4 which is incompatible.\n",
            "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.8.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed Mako-1.3.0 Pillow-10.0.0 accelerate-0.24.0 aiofiles-23.2.1 alembic-1.12.1 arrow-1.3.0 autotrain-advanced-0.6.44 bitsandbytes-0.41.0 brotli-1.1.0 cmaes-0.10.0 codecarbon-2.2.3 colorlog-6.7.0 datasets-2.14.7 diffusers-0.21.4 dill-0.3.7 docstring-parser-0.15 einops-0.6.1 evaluate-0.3.0 fastapi-0.104.1 ffmpy-0.3.1 fuzzywuzzy-0.18.0 gradio-3.41.0 gradio-client-0.5.0 h11-0.14.0 httpcore-1.0.2 httpx-0.25.2 huggingface-hub-0.17.3 inflate64-1.0.0 invisible-watermark-0.2.0 ipadic-1.0.0 jiwer-3.0.2 joblib-1.3.1 loguru-0.7.0 multiprocess-0.70.15 multivolumefile-0.2.3 optuna-3.3.0 orjson-3.9.10 packaging-23.1 peft-0.5.0 protobuf-4.23.4 py7zr-0.20.6 pyarrow-hotfix-0.6 pybcj-1.0.2 pycryptodomex-3.19.0 pydantic-1.10.11 pydub-0.25.1 pynvml-11.5.0 pyppmd-1.0.0 python-multipart-0.0.6 pyzstd-0.15.9 rapidfuzz-2.13.7 responses-0.18.0 rouge-score-0.1.2 sacremoses-0.0.53 scikit-learn-1.3.0 semantic-version-2.10.0 sentencepiece-0.1.99 shtab-1.6.4 starlette-0.27.0 texttable-1.7.0 tiktoken-0.5.1 tokenizers-0.14.1 tqdm-4.65.0 transformers-4.34.1 trl-0.7.2 types-python-dateutil-2.8.19.14 typing-extensions-4.8.0 tyro-0.5.18 uvicorn-0.24.0.post1 websockets-11.0.3 werkzeug-2.3.6 xgboost-1.7.6\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "PIL",
                  "huggingface_hub",
                  "joblib",
                  "pydantic",
                  "sklearn",
                  "tqdm",
                  "transformers"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2023.7.22)\n"
          ]
        }
      ],
      "source": [
        "!pip install autotrain-advanced\n",
        "!pip install huggingface_hub\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!autotrain setup --update-torch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GOa9Wpjfphrj",
        "outputId": "81e2aa2d-4564-4a62-e35d-87d6aa3eef53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "> \u001b[1mINFO    Installing latest xformers\u001b[0m\n",
            "> \u001b[1mINFO    Successfully installed latest xformers\u001b[0m\n",
            "> \u001b[1mINFO    Installing latest PyTorch\u001b[0m\n",
            "> \u001b[1mINFO    Successfully installed latest PyTorch\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "c8039fc361b04887ac1b1c4b73b7d7ed",
            "a02f79a1f55a417489e0dfbe0bf03d4b",
            "be37421a00e74d728c754aeb2e71d93a",
            "ee54623ae4f04b9dbf23f2d9966ca089",
            "638f7318efa04c508b2961f8d40a173e",
            "2f2b3f180d024a6ca33ba42f3606c6c2",
            "27f97928c14d41ed92ed31664410d505",
            "3401b391fff54d61aea06f78e2000a6f",
            "3dfcddfc05a3493386be69b844318d80",
            "386ca95def1f43eba644195bda36398e",
            "af8ddade6b0647ccbb6272d6e2966dec",
            "d4800ef85f6143b3a680450014b1a31b",
            "91b7bfbd99f24f05922c61f4faf7b548",
            "ce93972d516f432989cde1f4360ce1dd",
            "9c156bfdf97b4eada8bc29d9099bc26b",
            "1c1f9ce7b485448998c17ee42ef3c2dc",
            "659918b14f874293b4c94377aad17949",
            "193ce568809747818365288c9cada7b6",
            "60ca7007df33411da3bd65695fc8f420",
            "a009e487895343868a74dc3c0c54e8f7",
            "a8cfdeebf38446d5bb836d96bba0cb3a",
            "8fb14d767752422581a76d5a2cad8f98",
            "3c664642fe494923903c46ca882d7584",
            "a35c38efd8cd416a8b4f6bbf24c30255",
            "d32dd98c5683423cb0f01f12dc4b8015",
            "157d13dd08754777acad4fcd274b8bf6",
            "55dd2719ea9f4bd6a16a6b31dda8f7e6",
            "0cb4b540f5744560bd9f78fc03386b23",
            "62698445c3b24ed4ac002f40a6787411",
            "552f35768d484817959a0dc8cc513fab",
            "248759c70a934c1399ca0480192f9bbf",
            "ac75da56c7444c5f8dedbfee5fad002b"
          ]
        },
        "id": "-0Y94-HyqEyb",
        "outputId": "439bc420-6b84-434b-bfcf-8e7dfcdb1c50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c8039fc361b04887ac1b1c4b73b7d7ed"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!autotrain llm\\\n",
        " --train\\\n",
        " --project_name OPI3\\\n",
        " --model NousResearch/Llama-2-7b-chat-hf\\\n",
        " --data_path .\\\n",
        "  --use_peft\\\n",
        "  --use_int4\\\n",
        "  --learning_rate 2e-4 \\\n",
        "  --num_train_epochs 20\\\n",
        "  --trainer sft\\\n",
        "  --push_to_hub --repo_id TimelyFormulation74/AskAPhil3 --token"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "53BbgQaZcY2I",
        "outputId": "f47d2bb8-bfa6-4525-edab-8c4935d5d6b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "> \u001b[1mINFO    Running LLM\u001b[0m\n",
            "> \u001b[1mINFO    Params: Namespace(version=False, train=True, deploy=False, inference=False, data_path='chrishayuk/test', train_split='train', valid_split=None, text_column='text', rejected_text_column='rejected', prompt_text_column='prompt', model='NousResearch/Llama-2-7b-chat-hf', model_ref=None, learning_rate=0.0002, num_train_epochs=20, train_batch_size=2, warmup_ratio=0.1, gradient_accumulation_steps=1, optimizer='adamw_torch', scheduler='linear', weight_decay=0.0, max_grad_norm=1.0, seed=42, add_eos_token=False, block_size=-1, use_peft=True, lora_r=16, lora_alpha=32, lora_dropout=0.05, logging_steps=-1, project_name='OPI3', evaluation_strategy='epoch', save_total_limit=1, save_strategy='epoch', auto_find_batch_size=False, fp16=False, push_to_hub=True, use_int8=False, model_max_length=1024, repo_id='TimelyFormulation74/AskAPhil3', use_int4=True, trainer='sft', target_modules=None, merge_adapter=False, token='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', backend='default', username=None, use_flash_attention_2=False, log='none', disable_gradient_checkpointing=False, dpo_beta=0.1, func=<function run_llm_command_factory at 0x7f53233811b0>)\u001b[0m\n",
            "Downloading readme: 100% 36.0/36.0 [00:00<00:00, 125kB/s]\n",
            "Downloading data files:   0% 0/1 [00:00<?, ?it/s]\n",
            "Downloading data:   0% 0.00/14.0k [00:00<?, ?B/s]\u001b[A\n",
            "Downloading data: 100% 14.0k/14.0k [00:00<00:00, 64.3kB/s]\n",
            "Downloading data files: 100% 1/1 [00:00<00:00,  4.55it/s]\n",
            "Extracting data files: 100% 1/1 [00:00<00:00, 1593.58it/s]\n",
            "Generating train split: 43 examples [00:00, 2653.29 examples/s]\n",
            "Loading checkpoint shards: 100% 2/2 [00:07<00:00,  3.79s/it]\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py:374: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:362: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:367: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
            "  warnings.warn(\n",
            "> \u001b[1mINFO    Using block size 1024\u001b[0m\n",
            "> \u001b[1mINFO    creating trainer\u001b[0m\n",
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:214: UserWarning: You passed a tokenizer with `padding_side` not equal to `right` to the SFTTrainer. This might lead to some unexpected behaviour due to overflow issues when training a model in half-precision. You might consider adding `tokenizer.padding_side = 'right'` to your code.\n",
            "  warnings.warn(\n",
            "  0% 0/440 [00:00<?, ?it/s]You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "  0% 2/440 [00:13<50:28,  6.91s/it]/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "{'loss': 2.6686, 'learning_rate': 1.8181818181818182e-05, 'epoch': 1.09}\n",
            "  1% 4/440 [00:27<49:59,  6.88s/it]/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "  1% 6/440 [00:41<50:03,  6.92s/it]/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "{'loss': 2.6205, 'learning_rate': 3.6363636363636364e-05, 'epoch': 3.09}\n",
            "  2% 8/440 [00:55<50:14,  6.98s/it]/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "  2% 10/440 [01:09<50:22,  7.03s/it]/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "{'loss': 2.4854, 'learning_rate': 5.4545454545454546e-05, 'epoch': 5.09}\n",
            "  3% 12/440 [01:24<50:37,  7.10s/it]/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "  3% 14/440 [01:38<50:37,  7.13s/it]/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "{'loss': 2.2503, 'learning_rate': 7.272727272727273e-05, 'epoch': 7.09}\n",
            "  4% 16/440 [01:53<50:45,  7.18s/it]/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "  4% 18/440 [02:07<51:05,  7.27s/it]/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "{'loss': 1.9565, 'learning_rate': 9.090909090909092e-05, 'epoch': 9.09}\n",
            "  5% 20/440 [02:22<51:29,  7.36s/it]/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "  5% 22/440 [02:37<51:34,  7.40s/it]/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "{'loss': 1.5996, 'learning_rate': 0.00010909090909090909, 'epoch': 11.09}\n",
            "  5% 24/440 [02:53<52:09,  7.52s/it]/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "  6% 26/440 [03:09<53:38,  7.77s/it]/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "{'loss': 1.4021, 'learning_rate': 0.00012727272727272728, 'epoch': 13.09}\n",
            "  6% 28/440 [03:25<54:43,  7.97s/it]/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "  7% 30/440 [03:41<54:19,  7.95s/it]/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "{'loss': 1.3218, 'learning_rate': 0.00014545454545454546, 'epoch': 15.09}\n",
            "  7% 32/440 [03:57<53:19,  7.84s/it]/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "  8% 34/440 [04:12<52:36,  7.77s/it]/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "{'loss': 1.211, 'learning_rate': 0.00016363636363636366, 'epoch': 17.09}\n",
            "  8% 36/440 [04:28<52:28,  7.79s/it]/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "  9% 38/440 [04:44<52:42,  7.87s/it]/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "{'loss': 1.1192, 'learning_rate': 0.00018181818181818183, 'epoch': 19.09}\n",
            "{'train_runtime': 300.3944, 'train_samples_per_second': 2.863, 'train_steps_per_second': 1.465, 'train_loss': 1.8635118365287782, 'epoch': 19.09}\n",
            "  9% 40/440 [05:00<50:03,  7.51s/it]\n",
            "> \u001b[1mINFO    Finished training, saving model...\u001b[0m\n",
            "> \u001b[1mINFO    Pushing model to hub...\u001b[0m\n",
            "adapter_model.bin:   0% 0.00/33.6M [00:00<?, ?B/s]\n",
            "adapter_model.bin:   0% 0.00/33.6M [00:00<?, ?B/s]\u001b[A\n",
            "\n",
            "optimizer.pt:   0% 0.00/67.2M [00:00<?, ?B/s]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "rng_state.pth:   0% 0.00/14.2k [00:00<?, ?B/s]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "scheduler.pt:   0% 0.00/1.06k [00:00<?, ?B/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "adapter_model.bin:   0% 16.4k/33.6M [00:00<21:27, 26.1kB/s]\n",
            "\n",
            "\n",
            "\n",
            "scheduler.pt: 100% 1.06k/1.06k [00:00<00:00, 1.68kB/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "adapter_model.bin:   0% 16.4k/33.6M [00:00<21:56, 25.5kB/s]\u001b[A\n",
            "\n",
            "optimizer.pt:   0% 16.4k/67.2M [00:00<43:43, 25.6kB/s]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "rng_state.pth: 100% 14.2k/14.2k [00:00<00:00, 22.0kB/s]\u001b[A\u001b[A\u001b[A\n",
            "adapter_model.bin:   8% 2.79M/33.6M [00:00<00:06, 4.77MB/s]\u001b[A\n",
            "\n",
            "scheduler.pt: 100% 1.06k/1.06k [00:00<00:00, 1.22kB/s]\n",
            "adapter_model.bin:  16% 5.37M/33.6M [00:01<00:05, 5.38MB/s]\n",
            "rng_state.pth: 100% 14.2k/14.2k [00:01<00:00, 9.47kB/s]\n",
            "\n",
            "\n",
            "optimizer.pt:   5% 3.42M/67.2M [00:01<00:26, 2.37MB/s]\u001b[A\u001b[A\n",
            "adapter_model.bin:  25% 8.40M/33.6M [00:01<00:03, 7.11MB/s]\n",
            "\n",
            "optimizer.pt:   8% 5.70M/67.2M [00:01<00:14, 4.33MB/s]\u001b[A\u001b[A\n",
            "adapter_model.bin:  20% 6.86M/33.6M [00:01<00:04, 5.39MB/s]\u001b[A\n",
            "\n",
            "\n",
            "adapter_model.bin:  29% 9.63M/33.6M [00:01<00:03, 7.88MB/s]\n",
            "adapter_model.bin:  27% 8.93M/33.6M [00:01<00:03, 7.69MB/s]\u001b[A\n",
            "\n",
            "optimizer.pt:  11% 7.37M/67.2M [00:01<00:10, 5.65MB/s]\u001b[A\u001b[A\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#!autotrain llm --train --project_name AskAPhil2 --model TinyPixel/Llama-2-7B-bf16-sharded --data_path . --use_peft --use_int4 --learning_rate 2e-4 --train_batch_size 4 --num_train_epochs 10 --trainer sft --model_max_length 2048 --push_to_hub --repo_id TimelyFormulation74/AskAPhil2  lC"
      ],
      "metadata": {
        "id": "8YSNeC0fqNl4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#pip install git+https://github.com/huggingface/peft\n",
        "#pip install git+https://github.com/mymusise/peft.git@chatglm"
      ],
      "metadata": {
        "id": "ogDo8CVQEVps"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yNWV_AJRMofk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#from transformers import AutoTokenizer\n",
        "#from transformers import AutoModelForCausalLM\n",
        "from transformers import pipeline"
      ],
      "metadata": {
        "id": "xmHD5AkVr-sY"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ul-KER4t7R_v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#llm=AutoModelForCausalLM.from_pretrained(\"TimelyFormulation74/AskAPhil2\")\n",
        "\n",
        "#tokenizer = AutoTokenizer.from_pretrained(\"TimelyFormulation74/AskAPhil2\")"
      ],
      "metadata": {
        "id": "_fzgqKJf7YLM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_x19PnVe7mE7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#transcriber = pipeline(model=\"TinyPixel/Llama-2-7B-bf16-sharded\")\n",
        "#transcriber=pipeline(model=\"NousResearch/Llama-2-7b-chat-hf\",max_new_tokens=100)\n",
        "transcriber=pipeline(model=\"TimelyFormulation74/AskAPhil3\",max_new_tokens=100)\n"
      ],
      "metadata": {
        "id": "cD9ydgcrFztW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138,
          "referenced_widgets": [
            "2c9f6dae143943bea817f89b3de7c8d7",
            "986c6c045d764d6b958344c24ae871a1",
            "72b6ac418b0742dfa64384e96d84ee5a",
            "edba6a6a52da4598b36be05e486705eb",
            "4ab405cc93d04a1b90c930736cef9421",
            "e5a1e90e82bc4fcb8f573badfebd7088",
            "b7b46807d8a64b91b4901d199bbdf9d2",
            "346e5ce984164c238a1fb1357e6ab5f6",
            "e4ae381bf6044ec4954c4df1398faa44",
            "82e313390508441987aaed7752d0f96b",
            "593a4ab9929447ff937c79e059673f9a"
          ]
        },
        "outputId": "7d7ad8f5-eeb0-48da-ecd9-083c787e0e89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2c9f6dae143943bea817f89b3de7c8d7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:362: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:367: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#transcriber(\"Instruction: What is Aristotle's approach to logic?\")\n",
        "#transcriber(f\"[INST] {prompt} [/INST]\")"
      ],
      "metadata": {
        "id": "XAmGoDOJGcXk"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "del transcriber"
      ],
      "metadata": {
        "id": "LdXrebAbGkgw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VrlUsiG-jwDg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ofqXkiCoJe9X"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "###################################################################"
      ],
      "metadata": {
        "id": "8xVJS1wElNlS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Fpebc1L7kMe3"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}