Upload finetune.ipynb

finetune.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "E1zyZkJbdFuH"
+   },
+   "source": [
+    "# How to Finetune Mistral AI 7B LLM with Hugging Face AutoTrain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "j4yghPpHcxkG",
+    "outputId": "9c13119a-9c4b-4b24-ab1e-ae0695c9c863"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
+      "\u001b[0m"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install -U autotrain-advanced -q\n",
+    "!pip install datasets transformers -q"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
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+    "outputId": "bbc01be1-17d3-4c8f-8b11-3b2a4c1d4d3f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b138a11c06c447789ddbe117128c2ca3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading readme:   0%|          | 0.00/7.47k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d5ecfb10bcb643b485e67ab2650ea831",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e7f4c5df5478472297ab427f21ef1df9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/24.2M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95fa808517c04f55b99aaf138640302c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91bdba1d5ce7493ab466c0c6f6b948e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating train split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Load the dataset\n",
+    "train= load_dataset(\"tatsu-lab/alpaca\",split='train[:10%]')\n",
+    "train = pd.DataFrame(train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "13NK0qgZeQkN"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-Kb_0gjcddbT"
+   },
+   "source": [
+    "The dataset already contains the text columns with a format we need to fine-tune our LLM model. That’s why we don’t need to perform anything. However, I would provide a code if you have another dataset that needs the formatting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "rt09VgS2dehn"
+   },
+   "outputs": [],
+   "source": [
+    "def text_formatting(data):\n",
+    "\n",
+    "    # If the input column is not empty\n",
+    "    if data['input']:\n",
+    "\n",
+    "        text = f\"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\n{data[\"instruction\"]} \\n\\n### Input:\\n{data[\"input\"]}\\n\\n### Response:\\n{data[\"output\"]}\"\"\"\n",
+    "\n",
+    "    else:\n",
+    "\n",
+    "        text = f\"\"\"Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n\\n### Instruction:\\n{data[\"instruction\"]}\\n\\n### Response:\\n{data[\"output\"]}\"\"\"\n",
+    "\n",
+    "    return text\n",
+    "\n",
+    "train['text'] = train.apply(text_formatting, axis =1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "y5OqQjqRdokC"
+   },
+   "source": [
+    "For the Hugging Face AutoTrain, we would need the data in the CSV format so that we would save the data with the following code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "QMaTqtpfdjoj"
+   },
+   "outputs": [],
+   "source": [
+    "train.to_csv('train.csv', index = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "UpGg8NRKdsEQ"
+   },
+   "source": [
+    "If you want to fine-tune the Mistral 7B Instruct v0.1 for conversation and question answering, we need to follow the chat template format provided by Mistral, shown in the code block below.\n",
+    "\n",
+    "```\n",
+    "<s>[INST] Instruction [/INST] Model answer</s>[INST] Follow-up instruction [/INST]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zUkxtYZ-eDxR"
+   },
+   "source": [
+    "We would use only the data without any input for the chat model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "pMduUpJmdrKg"
+   },
+   "outputs": [],
+   "source": [
+    "train_chat = train[train['input'] == ''].reset_index(drop = True).copy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "m_jN0bDEeRba"
+   },
+   "source": [
+    "Then, we could reformat the data with the following code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "y5d5KjKseRxs"
+   },
+   "outputs": [],
+   "source": [
+    "def chat_formatting(data):\n",
+    "\n",
+    "  text = f\"<s>[INST] {data['instruction']} [/INST] {data['output']} </s>\"\n",
+    "\n",
+    "  return text\n",
+    "\n",
+    "train_chat['text'] = train_chat.apply(chat_formatting, axis =1)\n",
+    "train_chat.to_csv('train_chat.csv', index =False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uPxeXTsweZor"
+   },
+   "source": [
+    "# Training and Fine-tuning\n",
+    "\n",
+    "\n",
+    "Let’s set up the Hugging Face AutoTrain environment to fine-tune the Mistral model. First, let’s run the AutoTrain setup using the following command."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "du0G6-fCeUI4",
+    "outputId": "309254c2-1210-4b6f-ea61-3fa642db07e6"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> \u001b[1mINFO    Installing latest xformers\u001b[0m\n",
+      "> \u001b[1mINFO    Successfully installed latest xformers\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!autotrain setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "id": "TkxXDYnFgl1d"
+   },
+   "outputs": [],
+   "source": [
+    "!mkdir data\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "YAlexnSeelKL"
+   },
+   "source": [
+    "let’s use the Mistral 7B Instruct v0.1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "id": "gomc9H3zemN-"
+   },
+   "outputs": [],
+   "source": [
+    "project_name = 'my_autotrain_llm'\n",
+    "model_name = 'mistralai/Mistral-7B-Instruct-v0.1'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qg_3MVYAeti5"
+   },
+   "source": [
+    "Then, we would add the Hugging Face information if you want to push your model to the repository."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "8_AHiHnWeoNI"
+   },
+   "outputs": [],
+   "source": [
+    "push_to_hub = True\n",
+    "hf_token = \"\"\n",
+    "repo_id = \"Andyrasika/mistral_autotrain_llm\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_YSh92jHfGc7"
+   },
+   "source": [
+    "Lastly, we would initiate the model parameter information in the variables below. You can change them to see if the result is good."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "4CgrMR_LfBEp"
+   },
+   "outputs": [],
+   "source": [
+    "learning_rate = 2e-4\n",
+    "num_epochs = 4\n",
+    "batch_size = 1\n",
+    "block_size = 1024\n",
+    "trainer = \"sft\"\n",
+    "warmup_ratio = 0.1\n",
+    "weight_decay = 0.01\n",
+    "gradient_accumulation = 4\n",
+    "use_fp16 = True\n",
+    "use_peft = True\n",
+    "use_int4 = True\n",
+    "lora_r = 16\n",
+    "lora_alpha = 32\n",
+    "lora_dropout = 0.045"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "oSzTX6CIfSsQ"
+   },
+   "source": [
+    "When all the information is ready, we will set up the environment to accept all the information we have set up previously."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "id": "D2hxzc4VfE2r"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"PROJECT_NAME\"] = project_name\n",
+    "os.environ[\"MODEL_NAME\"] = model_name\n",
+    "os.environ[\"PUSH_TO_HUB\"] = str(push_to_hub)\n",
+    "os.environ[\"HF_TOKEN\"] = hf_token\n",
+    "os.environ[\"REPO_ID\"] = repo_id\n",
+    "os.environ[\"LEARNING_RATE\"] = str(learning_rate)\n",
+    "os.environ[\"NUM_EPOCHS\"] = str(num_epochs)\n",
+    "os.environ[\"BATCH_SIZE\"] = str(batch_size)\n",
+    "os.environ[\"BLOCK_SIZE\"] = str(block_size)\n",
+    "os.environ[\"WARMUP_RATIO\"] = str(warmup_ratio)\n",
+    "os.environ[\"WEIGHT_DECAY\"] = str(weight_decay)\n",
+    "os.environ[\"GRADIENT_ACCUMULATION\"] = str(gradient_accumulation)\n",
+    "os.environ[\"USE_FP16\"] = str(use_fp16)\n",
+    "os.environ[\"USE_PEFT\"] = str(use_peft)\n",
+    "os.environ[\"USE_INT4\"] = str(use_int4)\n",
+    "os.environ[\"LORA_R\"] = str(lora_r)\n",
+    "os.environ[\"LORA_ALPHA\"] = str(lora_alpha)\n",
+    "os.environ[\"LORA_DROPOUT\"] = str(lora_dropout)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "1Aea0SPPfU-o",
+    "outputId": "866a7737-ee30-4807-9ac2-cea83f7c9759"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> \u001b[1mINFO    Running LLM\u001b[0m\n",
+      "> \u001b[1mINFO    Params: Namespace(version=False, train=True, deploy=False, inference=False, data_path='data/', train_split='train', valid_split=None, text_column='text', rejected_text_column='rejected', prompt_text_column='prompt', model='mistralai/Mistral-7B-Instruct-v0.1', model_ref=None, learning_rate=0.0002, num_train_epochs=4, train_batch_size=1, warmup_ratio=0.1, gradient_accumulation_steps=4, optimizer='adamw_torch', scheduler='linear', weight_decay=0.01, max_grad_norm=1.0, seed=42, add_eos_token=False, block_size=1024, use_peft=True, lora_r=16, lora_alpha=32, lora_dropout=0.045, logging_steps=-1, project_name='my_autotrain_llm', evaluation_strategy='epoch', save_total_limit=1, save_strategy='epoch', auto_find_batch_size=False, fp16=True, push_to_hub=True, use_int8=False, model_max_length=1024, repo_id='Andyrasika/mistral_autotrain_llm', use_int4=True, trainer='default', target_modules='q_proj,v_proj', 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 0x7fd29fe97b50>)\u001b[0m\n",
+      "Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n",
+      "Loading checkpoint shards: 100%|██████████████████| 2/2 [00:10<00:00,  5.27s/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",
+      "> \u001b[1mINFO    Using block size 1024\u001b[0m\n",
+      "Running tokenizer on train dataset: 100%|█| 2919/2919 [00:00<00:00, 16401.43 exa\n",
+      "Grouping texts in chunks of 1024 (num_proc=4): 100%|█| 2919/2919 [00:00<00:00, 8\n",
+      "/usr/local/lib/python3.10/dist-packages/datasets/table.py:1395: FutureWarning: promote has been superseded by mode='default'.\n",
+      "  block_group = [InMemoryTable(cls._concat_blocks(list(block_group), axis=axis))]\n",
+      "/usr/local/lib/python3.10/dist-packages/datasets/table.py:1421: FutureWarning: promote has been superseded by mode='default'.\n",
+      "  table = cls._concat_blocks(blocks, axis=0)\n",
+      "> \u001b[1mINFO    creating trainer\u001b[0m\n",
+      "Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n",
+      "  0%|                                                   | 0/272 [00:00<?, ?it/s]/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': 1.6221, 'learning_rate': 0.00017868852459016393, 'epoch': 0.79}        \n",
+      " 25%|██████████▌                               | 68/272 [06:12<18:36,  5.47s/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': 1.1985, 'learning_rate': 0.00013442622950819673, 'epoch': 1.58}        \n",
+      " 50%|████████████████████▋                    | 137/272 [12:30<12:19,  5.48s/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': 1.1718, 'learning_rate': 9.016393442622952e-05, 'epoch': 2.36}         \n",
+      " 75%|██████████████████████████████▉          | 205/272 [18:43<06:06,  5.48s/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': 1.1195, 'learning_rate': 4.672131147540984e-05, 'epoch': 3.15}         \n",
+      "{'loss': 1.0857, 'learning_rate': 2.459016393442623e-06, 'epoch': 3.94}         \n",
+      "{'train_runtime': 1490.6488, 'train_samples_per_second': 0.735, 'train_steps_per_second': 0.182, 'train_loss': 1.2388554951723885, 'epoch': 3.97}\n",
+      "100%|█████████████████████████████████████████| 272/272 [24:50<00:00,  5.48s/it]\n",
+      "> \u001b[1mINFO    Finished training, saving model...\u001b[0m\n",
+      "> \u001b[1mINFO    Pushing model to hub...\u001b[0m\n",
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+      "\n",
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+      "\n",
+      "\n",
+      "Upload 10 LFS files:   0%|                               | 0/10 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
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+      "\n",
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+      "\n",
+      "\n",
+      "\n",
+      "pytorch_model.bin: 100%|███████████████████████| 888/888 [00:00<00:00, 1.48kB/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "rng_state.pth:  58%|█████████████▏         | 8.19k/14.2k [00:00<00:00, 13.5kB/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "adapter_model.bin:   0%|                   | 8.19k/27.3M [00:00<36:31, 12.5kB/s]\u001b[A\n",
+      "\n",
+      "optimizer.pt:   0%|                      | 8.19k/54.6M [00:00<1:11:58, 12.6kB/s]\u001b[A\u001b[A\n",
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+      "\n",
+      "pytorch_model.bin: 100%|███████████████████████| 888/888 [00:00<00:00, 1.18kB/s]\u001b[A\u001b[A\n",
+      "\n",
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+      "\n",
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+      "\n",
+      "rng_state.pth: 100%|███████████████████████| 14.2k/14.2k [00:01<00:00, 13.2kB/s]\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
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+      "adapter_model.bin:  38%|███████▎           | 10.4M/27.3M [00:01<00:01, 10.6MB/s]\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
+      "\n",
+      "\n",
+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
+      "adapter_model.bin:  62%|███████████▊       | 17.0M/27.3M [00:02<00:01, 8.69MB/s]\u001b[A\u001b[A\n",
+      "\n",
+      "optimizer.pt:  39%|█████████▍              | 21.5M/54.6M [00:02<00:02, 14.6MB/s]\u001b[A\u001b[A\n",
+      "tokenizer.model: 100%|████████████████████████| 493k/493k [00:00<00:00, 881kB/s]\u001b[A\n",
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+      "\n",
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+      "\n",
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+      "\n",
+      "optimizer.pt:  49%|███████████▋            | 26.7M/54.6M [00:02<00:01, 18.6MB/s]\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "adapter_model.bin:  93%|█████████████████▋ | 25.4M/27.3M [00:02<00:00, 16.0MB/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
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+      "\n",
+      "\n",
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+      "\n",
+      "optimizer.pt:  59%|██████████████          | 32.0M/54.6M [00:02<00:01, 18.4MB/s]\u001b[A\u001b[A\n",
+      "adapter_model.bin:  96%|██████████████████▏| 26.2M/27.3M [00:02<00:00, 11.5MB/s]\u001b[A\n",
+      "\n",
+      "training_args.bin: 100%|███████████████████| 4.47k/4.47k [00:00<00:00, 11.7kB/s]\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "tokenizer.model: 100%|████████████████████████| 493k/493k [00:00<00:00, 643kB/s]\u001b[A\u001b[A\n",
+      "adapter_model.bin: 100%|███████████████████| 27.3M/27.3M [00:03<00:00, 8.30MB/s]\n",
+      "\n",
+      "\n",
+      "adapter_model.bin: 100%|███████████████████| 27.3M/27.3M [00:03<00:00, 7.91MB/s]\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "optimizer.pt:  88%|█████████████████████▏  | 48.1M/54.6M [00:03<00:00, 10.6MB/s]\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "optimizer.pt: 100%|████████████████████████| 54.6M/54.6M [00:04<00:00, 11.3MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "Upload 10 LFS files: 100%|██████████████████████| 10/10 [00:05<00:00,  1.91it/s]\u001b[A\u001b[A\u001b[A\n"
+     ]
+    }
+   ],
+   "source": [
+    "!autotrain llm \\\n",
+    "--train \\\n",
+    "--model ${MODEL_NAME} \\\n",
+    "--project-name ${PROJECT_NAME} \\\n",
+    "--data-path data/ \\\n",
+    "--text-column text \\\n",
+    "--lr ${LEARNING_RATE} \\\n",
+    "--batch-size ${BATCH_SIZE} \\\n",
+    "--epochs ${NUM_EPOCHS} \\\n",
+    "--block-size ${BLOCK_SIZE} \\\n",
+    "--warmup-ratio ${WARMUP_RATIO} \\\n",
+    "--lora-r ${LORA_R} \\\n",
+    "--lora-alpha ${LORA_ALPHA} \\\n",
+    "--lora-dropout ${LORA_DROPOUT} \\\n",
+    "--target_modules q_proj,v_proj \\\n",
+    "--weight-decay ${WEIGHT_DECAY} \\\n",
+    "--gradient-accumulation ${GRADIENT_ACCUMULATION} \\\n",
+    "$( [[ \"$USE_FP16\" == \"True\" ]] && echo \"--fp16\" ) \\\n",
+    "$( [[ \"$USE_PEFT\" == \"True\" ]] && echo \"--use-peft\" ) \\\n",
+    "$( [[ \"$USE_INT4\" == \"True\" ]] && echo \"--use-int4\" ) \\\n",
+    "$( [[ \"$PUSH_TO_HUB\" == \"True\" ]] && echo \"--push-to-hub --token ${HF_TOKEN} --repo-id ${REPO_ID}\" )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df8a23a00d324ce9b75ab46be15c45c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from huggingface_hub import notebook_login\n",
+    "notebook_login()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "id": "7KCWb68ufYNx"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "52b6379edab4461ea5e6ac2b1a38648a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)okenizer_config.json:   0%|          | 0.00/1.39k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f5d783597eb84c628a9a72023ac94806",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading tokenizer.model:   0%|          | 0.00/493k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "adef9f06343b4549b22cd565152a0f98",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.80M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a674d46e0084d61abebde36e42c0774",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/437 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aed414ba3d4544178e159b9840f1f4b6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)/adapter_config.json:   0%|          | 0.00/458 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "65f87796eb044b73859b43bf5e435759",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "23dccfc550ad436a9f53f909478f2e26",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading adapter_model.bin:   0%|          | 0.00/27.3M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "\n",
+    "model_path = \"Andyrasika/mistral_autotrain_llm\"\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_path)\n",
+    "model = AutoModelForCausalLM.from_pretrained(model_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
+      "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1260: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<s> Health benefits of regular exercise include improved cardiovascular health, increased strength and flexibility, improved mental\n"
+     ]
+    }
+   ],
+   "source": [
+    "input_text = \"Health benefits of regular exercise\"\n",
+    "input_ids = tokenizer.encode(input_text, return_tensors=\"pt\")\n",
+    "output = model.generate(input_ids)\n",
+    "predicted_text = tokenizer.decode(output[0], skip_special_tokens=False)\n",
+    "print(predicted_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "0246b46f672247dab14d0ddd0c0a25ec": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
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+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
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+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
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+      "max_height": null,
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+      "min_width": null,
+      "object_fit": null,
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+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "03469a3882de440cb6a8c36b5a4568d6": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_d7708c22d8a34082b80a14302983159e",
+      "placeholder": "​",
+      "style": "IPY_MODEL_c59da8b062a84afebfad5da6ef5a496a",
+      "value": " 1/1 [00:01&lt;00:00,  1.00s/it]"
+     }
+    },
+    "067ebf5a471e4546bc631f290411a664": {
+     "model_module": "@jupyter-widgets/controls",
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+     "state": {
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+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_6385ed14895f414c8eda670301ebead9",
+      "placeholder": "​",
+      "style": "IPY_MODEL_f902ed42a30e476a8d508c50fa9fc602",
+      "value": "Extracting data files: 100%"
+     }
+    },
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+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
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+      "_model_module_version": "1.2.0",
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