diff --git "a/code_gpt_ut.ipynb" "b/code_gpt_ut.ipynb"
new file mode 100644--- /dev/null
+++ "b/code_gpt_ut.ipynb"
@@ -0,0 +1,4852 @@
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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"
+ },
+ "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": [
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+ "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, ?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