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"bar_color": null, - "description_width": "" - } - }, - "a50b9c5cbc9d491da25bee196f99935f": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "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", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "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, - "grid_column": null, - "grid_gap": null, - "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, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "77fb67ace148429e875f47a97f265ae3": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - } - } - } - }, - "cells": [ - { - "cell_type": "code", - "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", - 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}, - "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