diff --git "a/code_gpt_ut.ipynb" "b/code_gpt_ut.ipynb"
deleted file mode 100644--- "a/code_gpt_ut.ipynb"
+++ /dev/null
@@ -1,4852 +0,0 @@
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Copy a token from your Hugging Face\ntokens page and paste it below.
Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file. "
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- "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"
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- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Mounted at /content/gdrive\n"
- ]
- }
- ]
- },
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- "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()"
- ],
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- "outputId": "04e79c4c-f2f3-4577-fc43-eb4ee83ced94"
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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": [
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- "{'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",
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- "{'loss': 0.7807, 'learning_rate': 0.00019983744805229296, 'epoch': 0.1}\n",
- "{'loss': 0.8367, 'learning_rate': 0.00019975519811066663, 'epoch': 0.1}\n",
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- },
- {
- "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, ?B/s]"
- ],
- "application/vnd.jupyter.widget-view+json": {
- "version_major": 2,
- "version_minor": 0,
- "model_id": "cfd2e87b36e941ccb519e2cc662f3bb8"
- }
- },
- "metadata": {}
- },
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "CommitInfo(commit_url='https://huggingface.co/01GangaPutraBheeshma/colab_code_generator_FT_code_gen_UT/commit/9a228231bbce763a14da7e007f6bddb24f402561', commit_message='Upload tokenizer', commit_description='', oid='9a228231bbce763a14da7e007f6bddb24f402561', pr_url=None, pr_revision=None, pr_num=None)"
- ]
- },
- "metadata": {},
- "execution_count": 19
- }
- ]
- },
- {
- "cell_type": "code",
- "source": [
- "# import math\n",
- "\n",
- "# eval_results = trainer.evaluate()\n",
- "# print(f\"Perplexity: {math.exp(eval_results['eval_loss']):.2f}\")"
- ],
- "metadata": {
- "id": "KarCY_VmZD2r"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "instruction=\"Write a Python program to generate a binary search given an array input and element to be searched.\"\n",
- "input=\"[2,3,4,5], [5]\"\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",
- "input_ids = tokenizer(prompt, return_tensors=\"pt\", truncation=True).input_ids\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 = trained_model.generate(input_ids=input_ids, max_new_tokens=200, do_sample=True, top_p=0.9,temperature=0.5)\n",
- "\n",
- "print(f\"-------------------------\\n\\n\")\n",
- "print(f\"Generated instruction:\\n{trained_tokenizer.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": "9i0lc10vqu0E",
- "outputId": "5e44711e-f9c1-4ff5-a97d-85dc9c443d21"
- },
- "execution_count": 20,
- "outputs": [
- {
- "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": [
- "-------------------------\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 generate a binary search given an array input and element to be searched.\n",
- "\n",
- "### Input:\n",
- "[2,3,4,5], [5]\n",
- "\n",
- "### Response:\n",
- "\n",
- "\n",
- "-------------------------\n",
- "\n",
- "\n",
- "Before Training Response :\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1591: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.\n",
- " warnings.warn(\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",
- "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:61: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
- " warnings.warn(\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": [
- "Generated instruction:\n",
- "# Generate a binary search function\n",
- "def binary_search(array, target):\n",
- " # Initialize the middle point\n",
- " mid = len(array) // 2\n",
- "\n",
- " # Check if the target is present at the middle point\n",
- " if array[mid] == target:\n",
- " return mid\n",
- " elif array[mid] > 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