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"source": [ + "from google.colab import drive\n", + "drive.mount('/content/gdrive')\n", + "#using model.save_weights() to save the weights of the model in HDF5 format\n", + "# model.save_weights(\"/content/gdrive/My Drive/weights.h5\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AHIZxIoBzJot", + "outputId": "c32cb46c-1c5b-4792-b6c4-6f15a6e1c41b" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/gdrive\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install -q -U trl transformers accelerate git+https://github.com/huggingface/peft.git\n", + "!pip install -q datasets bitsandbytes einops\n", + "\n", + "from datasets import load_dataset\n", + "from random import randrange\n", + "\n", + "import torch\n", + "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, TrainingArguments\n", + "from peft import LoraConfig, prepare_model_for_kbit_training, get_peft_model, AutoPeftModelForCausalLM\n", + "\n", + "from trl import SFTTrainer\n", + "\n", + "from huggingface_hub import login" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qECk5hXDqptD", + "outputId": "85bbd289-54e2-492c-c2d2-c9ba8b4edde4" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + " Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m18.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.6/44.6 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cextension.py:34: UserWarning: The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.\n", + " warn(\"The installed version of bitsandbytes was compiled without GPU support. \"\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cpu.so: undefined symbol: cadam32bit_grad_fp32\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/trl/trainer/ppo_config.py:141: UserWarning: The `optimize_cuda_cache` arguement will be deprecated soon, please use `optimize_device_cache` instead.\n", + " warnings.warn(\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "model_name = \"Salesforce/codegen-350M-mono\"\n", + "dataset_name = \"iamtarun/python_code_instructions_18k_alpaca\"\n", + "device_map = {\"\": 0}" + ], + "metadata": { + "id": "wp2S1uRUqXjN" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "peft_config = LoraConfig(\n", + " lora_alpha=16,\n", + " lora_dropout=0.1,\n", + " r=64,\n", + " bias=\"none\",\n", + " task_type=\"CAUSAL_LM\",\n", + ")" + ], + "metadata": { + "id": "MGfEnsuiUut-" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "bnb_config = BitsAndBytesConfig(\n", + " load_in_4bit=True,\n", + " bnb_4bit_use_double_quant=True,\n", + " bnb_4bit_quant_type=\"nf4\",\n", + " bnb_4bit_compute_dtype=\"float16\"\n", + ")" + ], + "metadata": { + "id": "ndzH96J5Uxy_" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from huggingface_hub import notebook_login\n", + "# Log in to HF Hub\n", + "notebook_login()" + ], + "metadata": { + "id": "7HTg8RBHpGl4", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "4c76a21ea3974ceebf0c614bd8beac8b", + "c4928c2ec4434520b9edba8139c324f4", + "f1073fcdc3d2499287538971cff2f1c4", + "e4c0263ca2ef4058bc6a1ef902b26f69", + "53647abc98004bfcbdd96b89cb96d3d3", + "6e33f0a07c804b16a5d7eb442ad76eda", + "19273831c148426d8f2676409d3509a2", + "6747cc8744974492b494cdea12165ffb", + "1971543f27f44811a0625d7f7513963f", + "82326d4d224a4e11b805ebe37481d2ba", + "2ab7c684d11f488d89fa781beef08040", + "f9b525bdbd92497e9de9d2480ab879df", + "e0ae2927e21a438ca79de9399e5f718e", + "28c670e25b47421ba05d5da1c7ff3d51", + "1325e486667c49299d78ed040af3268a", + "1fd402e8a55c4933ba0109b0d52320fb", + "44ff28c40f70430895e935ab0377d298", + "78cb49238dec42bf9e11db2d6cbe611a", + "40ee1674ff6b4d818d2b19420f9c7afb", + "a6b839e74026490ca7d0daff00f20c2c", + "426c7ae125ac4824a284d8c9967eb9da", + "5321844ea5064ed4bf815d2b57ae79d1", + "74b89f08ddb74bb1bdc0a32824d2575e", + "e7a6ccca5b3745da8b2933e9af71a09f", + "cad51a052d67457ba9cb690dedcf9886", + "9ef46d7a08e643ce8760d18e1b98c157", + "0edbbbb9d4e54d3e9351fe0dfab9e60e", + "f9b0a55172544c818774a6683330a320", + "3a69b066b6d3459aae921bb4aeda8d2d", + "c9ce5f51654347b8ac79dfde1d85b544", + "4428458aa93641f7bc6f117da75583be", + "e7919750884f4977bcdc0b81ed9e1bf9" + ] + }, + "outputId": "04e79c4c-f2f3-4577-fc43-eb4ee83ced94" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(HTML(value='
2048). Running this sequence through the model will result in indexing errors\n", + "You're using a CodeGenTokenizerFast 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" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "{'loss': 1.1666, 'learning_rate': 2.6315789473684212e-05, 'epoch': 0.01}\n", + "{'loss': 1.174, 'learning_rate': 5.2631578947368424e-05, 'epoch': 0.02}\n", + "{'loss': 1.1239, 'learning_rate': 7.894736842105263e-05, 'epoch': 0.02}\n", + "{'loss': 1.0462, 'learning_rate': 0.00010526315789473685, 'epoch': 0.03}\n", + "{'loss': 0.9808, 'learning_rate': 0.00013157894736842108, 'epoch': 0.04}\n", + "{'loss': 0.9396, 'learning_rate': 0.00015789473684210527, 'epoch': 0.05}\n", + "{'loss': 0.9461, 'learning_rate': 0.00018421052631578948, 'epoch': 0.06}\n", + "{'loss': 0.8908, 'learning_rate': 0.00019999865623437013, 'epoch': 0.06}\n", + "{'loss': 0.816, 'learning_rate': 0.00019998353928577919, 'epoch': 0.07}\n", + "{'loss': 0.7835, 'learning_rate': 0.00019995162822919883, 'epoch': 0.08}\n", + "{'loss': 0.7965, 'learning_rate': 0.00019990292842468868, 'epoch': 0.09}\n", + "{'loss': 0.7807, 'learning_rate': 0.00019983744805229296, 'epoch': 0.1}\n", + "{'loss': 0.8367, 'learning_rate': 0.00019975519811066663, 'epoch': 0.1}\n", + "{'loss': 0.7388, 'learning_rate': 0.0001996561924152278, 'epoch': 0.11}\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/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" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "{'loss': 0.786, 'learning_rate': 0.0001995404475958373, 'epoch': 1.0}\n", + "{'loss': 0.7248, 'learning_rate': 0.00019940798309400526, 'epoch': 1.01}\n", + "{'loss': 0.659, 'learning_rate': 0.00019925882115962568, 'epoch': 1.02}\n", + "{'loss': 0.7834, 'learning_rate': 0.00019909298684723904, 'epoch': 1.03}\n", + "{'loss': 0.7091, 'learning_rate': 0.000198910508011824, 'epoch': 1.03}\n", + "{'loss': 0.6875, 'learning_rate': 0.00019871141530411853, 'epoch': 1.04}\n", + "{'loss': 0.7292, 'learning_rate': 0.00019849574216547171, 'epoch': 1.05}\n", + "{'loss': 0.6792, 'learning_rate': 0.00019826352482222638, 'epoch': 1.06}\n", + "{'loss': 0.7476, 'learning_rate': 0.0001980148022796345, 'epoch': 1.07}\n", + "{'loss': 0.6446, 'learning_rate': 0.00019774961631530545, 'epoch': 1.07}\n", + "{'loss': 0.6659, 'learning_rate': 0.00019746801147218842, 'epoch': 1.08}\n", + "{'loss': 0.7153, 'learning_rate': 0.00019717003505109095, 'epoch': 1.09}\n", + "{'loss': 0.7151, 'learning_rate': 0.00019685573710273376, 'epoch': 1.1}\n", + "{'loss': 0.782, 'learning_rate': 0.00019652517041934356, 'epoch': 1.11}\n", + "{'loss': 0.6864, 'learning_rate': 0.00019617839052578603, 'epoch': 1.11}\n", + "{'train_runtime': 2697.457, 'train_samples_per_second': 3.707, 'train_steps_per_second': 0.463, 'train_loss': 0.8149129047587111, 'epoch': 1.12}\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "TrainOutput(global_step=148, training_loss=0.8149129047587111, metrics={'train_runtime': 2697.457, 'train_samples_per_second': 3.707, 'train_steps_per_second': 0.463, 'train_loss': 0.8149129047587111, 'epoch': 1.12})" + ] + }, + "metadata": {}, + "execution_count": 15 + } + ] + }, + { + "cell_type": "code", + "source": [ + "trainer.save_model('./saved')" + ], + "metadata": { + "id": "QpFgDfXtmChD" + }, + "execution_count": 16, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "trained_model = AutoModelForCausalLM.from_pretrained('./saved')" + ], + "metadata": { + "id": "OmRFrgpGn9gN" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "trained_tokenizer = AutoTokenizer.from_pretrained('./saved')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LaR1Jk84oI3q", + "outputId": "6070689e-34df-4eb9-9c0a-f2eb94c3e869" + }, + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# merged = trained_model.merge_and_unload()\n", + "# merged.save_pretrained(\"merged\",safe_serialization=True)\n", + "# tokenizer.save_pretrained(\"merged\")\n", + "\n", + "#push merged model to the hub\n", + "trained_model.push_to_hub(\"01GangaPutraBheeshma/colab_code_generator_FT_code_gen_UT\")\n", + "trained_tokenizer.push_to_hub(\"01GangaPutraBheeshma/colab_code_generator_FT_code_gen_UT\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 118, + "referenced_widgets": [ + "cfd2e87b36e941ccb519e2cc662f3bb8", + "8fbedff75016407eb345e2bb83849158", + "6fce20caaa164d9389ec5d15276e000a", + "e10be51bb3e74e4393d8fba1daf482f1", + "ebc565b831d2485faf92d64bb56ef721", + "07192ce262e14d36be5c27d3ea3b55f1", + "88ae623ab18a4f1fb64daa140bf0bd24", + "35078a25d987405fa49e49c35d9988f7", + "0c7b9bbda5014f32ae2ab6e398bc240b", + "a50b9c5cbc9d491da25bee196f99935f", + "77fb67ace148429e875f47a97f265ae3" + ] + }, + "id": "bYE9zarKmCY1", + "outputId": "7827cd21-b779-4592-899e-a3e5bfb641db" + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/transformers/integrations/peft.py:389: FutureWarning: The `active_adapter` method is deprecated and will be removed in a future version.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "adapter_model.safetensors: 0%| | 0.00/21.0M [00:00 target:\n", + " return binary_search(array[:mid], target)\n", + " else:\n", + " return binary_search(array[mid:], target)\n", + "\n", + "# Generate an array of elements\n", + "array = [2, 3, 4, 5]\n", + "\n", + "# Search for the target element\n", + "target = 5\n", + "\n", + "# Find the middle point\n", + "mid = len(array) // 2\n", + "\n", + "# Find the element at the middle point\n", + "result = binary_search(array, target)\n", + "\n", + "# Print the result\n", + "print(result)\n", + " \n", + "-------------------------\n", + "\n", + "\n", + "After Training Response :\n", + "-------------------------\n", + "\n", + "\n", + "Generated instruction:\n", + "def binary_search(array, element):\n", + " start = 0\n", + " end = len(array) - 1\n", + " while start <= end:\n", + " mid = (start + end) // 2\n", + " if array[mid] == element:\n", + " return mid\n", + " elif array[mid] > element:\n", + " end = mid - 1\n", + " else:\n", + " start = mid + 1\n", + " return None\n", + "\n", + "array = [2,3,4,5]\n", + "element = 5\n", + "result = binary_search(array, element)\n", + "print(\"Element found at index\", result)\n", + " \n", + "-------------------------\n", + "\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "instruction=\"Write a Python program to reverse a binary tree.\"\n", + "input=\"\"\n", + "\n", + "prompt = f\"\"\"### Instruction:\n", + "Use the Task below and the Input given to write the Response, which is a programming code that can solve the Task.\n", + "\n", + "### Task:\n", + "{instruction}\n", + "\n", + "### Input:\n", + "{input}\n", + "\n", + "### Response:\n", + "\"\"\"\n", + "\n", + "test_model_UT = AutoModelForCausalLM.from_pretrained(\"01GangaPutraBheeshma/colab_code_generator_FT_code_gen_UT\")\n", + "test_tokenizer_UT = AutoTokenizer.from_pretrained(\"01GangaPutraBheeshma/colab_code_generator_FT_code_gen_UT\")\n", + "\n", + "\n", + "input_ids = tokenizer(prompt, return_tensors=\"pt\", truncation=True).input_ids\n", + "\n", + "trained_input_ids = test_tokenizer_UT(prompt, return_tensors=\"pt\", truncation=True).input_ids\n", + "\n", + "# with torch.inference_mode():\n", + "\n", + "print(f\"-------------------------\\n\\n\")\n", + "print(f\"Prompt:\\n{prompt}\\n\")\n", + "print(f\"-------------------------\\n\\n\")\n", + "\n", + "print(f\"Before Training Response :\")\n", + "output_before = model.generate(input_ids=input_ids, max_new_tokens=200, do_sample=True, top_p=0.9,temperature=0.5)\n", + "print(f\"Generated instruction:\\n{tokenizer.batch_decode(output_before.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):]}\")\n", + "print(f\"-------------------------\\n\\n\")\n", + "\n", + "print(f\"After Training Response :\")\n", + "outputs = test_model_UT.generate(input_ids=trained_input_ids, max_new_tokens=500, do_sample=True, top_p=0.9,temperature=0.5)\n", + "\n", + "print(f\"-------------------------\\n\\n\")\n", + "print(f\"Generated instruction:\\n{test_tokenizer_UT.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):]}\")\n", + "print(f\"-------------------------\\n\\n\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pzhsz3Jov4qi", + "outputId": "81571e8d-5906-4078-e0be-1024d299f63d" + }, + "execution_count": 23, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n", + "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`:50256 for open-end generation.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "-------------------------\n", + "\n", + "\n", + "Prompt:\n", + "### Instruction:\n", + "Use the Task below and the Input given to write the Response, which is a programming code that can solve the Task.\n", + "\n", + "### Task:\n", + "Write a Python program to reverse a binary tree.\n", + "\n", + "### Input:\n", + "\n", + "\n", + "### Response:\n", + "\n", + "\n", + "-------------------------\n", + "\n", + "\n", + "Before Training Response :\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "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`:50256 for open-end generation.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generated instruction:\n", + "\n", + "# Definition for a binary tree node.\n", + "# class TreeNode:\n", + "# def __init__(self, val=0, left=None, right=None):\n", + "# self.val = val\n", + "# self.left = left\n", + "# self.right = right\n", + "class Solution:\n", + " def invertTree(self, root: TreeNode) -> TreeNode:\n", + " if not root:\n", + " return None\n", + " \n", + " root.left, root.right = self.invertTree(root.right), self.invertTree(root.left)\n", + " \n", + " return root\n", + " \n", + "-------------------------\n", + "\n", + "\n", + "After Training Response :\n", + "-------------------------\n", + "\n", + "\n", + "Generated instruction:\n", + "class Node:\n", + " def __init__(self, data):\n", + " self.data = data\n", + " self.left = None\n", + " self.right = None\n", + "\n", + "def reverseBinaryTree(root):\n", + " if root is None:\n", + " return None\n", + " \n", + " left = reverseBinaryTree(root.left)\n", + " right = reverseBinaryTree(root.right)\n", + "\n", + " root.left = right\n", + " root.right = left\n", + "\n", + " return root\n", + "\n", + "root = Node(1)\n", + "root.left = Node(2)\n", + "root.right = Node(3)\n", + "\n", + "print(reverseBinaryTree(root))\n", + " \n", + "-------------------------\n", + "\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "instruction=\"Write a Python Script to search a filename within a list of filenames and provide me the time complexity of the algorithm\"\n", + "input=\"\"\n", + "\n", + "prompt = f\"\"\"### Instruction:\n", + "Use the Task below and the Input given to write the Response, which is a programming code that can solve the Task.\n", + "\n", + "### Task:\n", + "{instruction}\n", + "\n", + "### Input:\n", + "{input}\n", + "\n", + "### Response:\n", + "\"\"\"\n", + "\n", + "from peft import AutoPeftModelForCausalLM\n", + "\n", + "test_model_UT = AutoPeftModelForCausalLM.from_pretrained(\"01GangaPutraBheeshma/colab_code_generator_FT_code_gen_UT\")\n", + "test_tokenizer_UT = AutoTokenizer.from_pretrained(\"01GangaPutraBheeshma/colab_code_generator_FT_code_gen_UT\")\n", + "\n", + "\n", + "# input_ids = tokenizer(prompt, return_tensors=\"pt\", truncation=True).input_ids\n", + "\n", + "trained_input_ids = test_tokenizer_UT(prompt, return_tensors=\"pt\", truncation=True).input_ids\n", + "\n", + "# with torch.inference_mode():\n", + "\n", + "# print(f\"-------------------------\\n\\n\")\n", + "# print(f\"Prompt:\\n{prompt}\\n\")\n", + "# print(f\"-------------------------\\n\\n\")\n", + "\n", + "# print(f\"Before Training Response :\")\n", + "# output_before = model.generate(input_ids=input_ids, max_new_tokens=200, do_sample=True, top_p=0.9,temperature=0.5)\n", + "# print(f\"Generated instruction:\\n{tokenizer.batch_decode(output_before.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):]}\")\n", + "# print(f\"-------------------------\\n\\n\")\n", + "\n", + "print(f\"After Training Response :\")\n", + "outputs = test_model_UT.generate(input_ids=trained_input_ids, max_new_tokens=500, do_sample=True, top_p=0.9,temperature=0.5)\n", + "\n", + "print(f\"-------------------------\\n\\n\")\n", + "print(f\"Generated instruction:\\n{test_tokenizer_UT.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):]}\")\n", + "print(f\"-------------------------\\n\\n\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Cy-NcYo8yzZe", + "outputId": "d13bf7df-38bd-4513-d8fd-7fd618ec962a" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n", + "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`:50256 for open-end generation.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "After Training Response :\n", + "-------------------------\n", + "\n", + "\n", + "Generated instruction:\n", + "\n", + "import os\n", + "import time\n", + "\n", + "def search_file(file_name, filenames):\n", + " \"\"\"\n", + " Search a file in a list of filenames\n", + " \"\"\"\n", + " for filename in filenames:\n", + " if os.path.isfile(filename):\n", + " file_time = os.path.getmtime(filename)\n", + " if file_time >= os.path.getmtime(file_name):\n", + " return file_time\n", + " return -1\n", + "\n", + "filenames = ['test.txt', 'another_file.txt']\n", + "\n", + "print(search_file('test.txt', filenames))\n", + "print(search_file('another_file.txt', filenames))\n", + " \n", + "-------------------------\n", + "\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "eZ9oGHS3yzWs" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file