{ "cells": [ { "cell_type": "markdown", "id": "33ffc531", "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "papermill": { "duration": 0.006372, "end_time": "2024-09-01T18:10:09.051036", "exception": false, "start_time": "2024-09-01T18:10:09.044664", "status": "completed" }, "tags": [] }, "source": [ "# Finetune LLama 3.1 include Embedding\n", "\n", "medium:\n", "https://medium.com/@tejaswi_kashyap/tailoring-llama-3-harnessing-fine-tuning-for-custom-language-tasks-6f7c61f657e2\n", "\n", "Challenges of Fully Fine-Tuning Token Embeddings and Language Modeling Head\n", "\n", "The drawbacks of fully fine-tuning the token embeddings and the language modeling head are significant. This approach requires substantially more memory than standard (Q)LoRA fine-tuning. Additionally, training a larger number of parameters increases the likelihood of overfitting the training dataset, particularly when using a high learning rate or when the dataset is relatively small\n", "\n", "## What is UltraChat200k? \n", "\n", "UltraChat-200k is an invaluable resource for natural language understanding, generation and dialog system research. With 1.4 million dialogues spanning a variety of topics, this parquet-formatted dataset offers researchers four distinct formats to aid in their studies: test_sft, train_sft, train_gen and test_gen. More details [here](https://www.kaggle.com/datasets/thedevastator/ultrachat-200k-nlp-dataset).\n", "\n", "## Inspiration\n", "\n", "For this notebook, I took inspiration from several sources:\n", "* [Efficiently fine-tune Llama 3 with PyTorch FSDP and Q-Lora](https://www.philschmid.de/fsdp-qlora-llama3) \n", "* [Fine-tuning LLMs using LoRA](https://medium.com/@rajatsharma_33357/fine-tuning-llama-using-lora-fb3f48a557d5) \n", "* [Fine-tuning Llama-3–8B-Instruct QLORA using low cost resources](https://medium.com/@avishekpaul31/fine-tuning-llama-3-8b-instruct-qlora-using-low-cost-resources-89075e0dfa04) \n", "* [Llama2 Fine-Tuning with Low-Rank Adaptations (LoRA) on Gaudi 2 Processors](https://eduand-alvarez.medium.com/llama2-fine-tuning-with-low-rank-adaptations-lora-on-gaudi-2-processors-52cf1ee6ce11) " ] }, { "cell_type": "markdown", "id": "55660f84", "metadata": { "papermill": { "duration": 0.005589, "end_time": "2024-09-01T18:10:09.062578", "exception": false, "start_time": "2024-09-01T18:10:09.056989", "status": "completed" }, "tags": [] }, "source": [ "# Install and import libraries" ] }, { "cell_type": "code", "execution_count": null, "id": "3c119322", "metadata": { "_kg_hide-output": true, "papermill": { "duration": 97.841727, "end_time": "2024-09-01T18:11:46.910129", "exception": false, "start_time": "2024-09-01T18:10:09.068402", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# !pip install -q -U bitsandbytes\n", "# !pip install -q -U transformers\n", "# !pip install -q -U peft\n", "# !pip install -q -U accelerate\n", "# !pip install -q -U datasets\n", "# !pip install -q -U trl" ] }, { "cell_type": "code", "execution_count": null, "id": "d1531b67", "metadata": { "papermill": { "duration": 5.038173, "end_time": "2024-09-01T18:11:51.954668", "exception": false, "start_time": "2024-09-01T18:11:46.916495", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# import pandas as pd\n", "# from torch.utils.data import Dataset\n", "# from torch.utils.data import DataLoader" ] }, { "cell_type": "code", "execution_count": null, "id": "0ecb4a6d", "metadata": { "papermill": { "duration": 4.250075, "end_time": "2024-09-01T18:11:56.211627", "exception": false, "start_time": "2024-09-01T18:11:51.961552", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# from kaggle_secrets import UserSecretsClient\n", "# user_secrets = UserSecretsClient()\n", "# wandb_key = user_secrets.get_secret(\"wandb_api\")\n", "# import wandb\n", "# ! wandb login $wandb_key" ] }, { "cell_type": "code", "execution_count": null, "id": "4ec22411", "metadata": { "_kg_hide-output": true, "papermill": { "duration": 15.420295, "end_time": "2024-09-01T18:12:11.638558", "exception": false, "start_time": "2024-09-01T18:11:56.218263", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import torch\n", "from time import time\n", "import multiprocessing,time\n", "from transformers import AutoConfig,AutoModelForCausalLM,AutoTokenizer,BitsAndBytesConfig,AutoTokenizer,TrainingArguments" ] }, { "cell_type": "code", "execution_count": null, "id": "72e92a97-250a-4ef2-b43a-534d06a2130a", "metadata": {}, "outputs": [], "source": [ "from peft import LoraConfig, PeftModel, prepare_model_for_kbit_training " ] }, { "cell_type": "code", "execution_count": null, "id": "995700c8-0d77-4bb9-a8eb-f6e3452e6168", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "97810f8f", "metadata": { "papermill": { "duration": 0.006192, "end_time": "2024-09-01T18:12:11.651217", "exception": false, "start_time": "2024-09-01T18:12:11.645025", "status": "completed" }, "tags": [] }, "source": [ "# Initialize model\n", "\n", "\n", "The model used is:\n", "\n", "* **Model**: Llama3 \n", "* **Framework**: Transformers \n", "* **Size**: 8B \n", "* **Type**: 8b-chat-hf (hf stands for HuggingFace). \n", "* **Version**: V1 " ] }, { "cell_type": "code", "execution_count": null, "id": "37c64df1", "metadata": { "papermill": { "duration": 0.01295, "end_time": "2024-09-01T18:12:11.670291", "exception": false, "start_time": "2024-09-01T18:12:11.657341", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "model_id = \"unsloth/Meta-Llama-3.1-8B-Instruct\"\n", "\n", "# tokenizer = AutoTokenizer.from_pretrained(model_id)\n", "# #EOS_TOKEN='<|eot_id|>'\n", "# EOS_TOKEN= tokenizer.eos_token " ] }, { "cell_type": "markdown", "id": "65ae0899", "metadata": { "papermill": { "duration": 0.00596, "end_time": "2024-09-01T18:12:11.682508", "exception": false, "start_time": "2024-09-01T18:12:11.676548", "status": "completed" }, "tags": [] }, "source": [ "We are using quantization (with BitsAndBytes)." ] }, { "cell_type": "markdown", "id": "096a6c8e-0da1-4786-bc01-58fc67cd414f", "metadata": {}, "source": [ "### Dataset" ] }, { "cell_type": "code", "execution_count": null, "id": "6e70bc5a-90dc-47ff-9407-2b1f48459669", "metadata": {}, "outputs": [], "source": [ "import os\n", "from datasets import load_dataset\n", "\n", "TRAIN_CHATML_PROMPT = \"\"\"\n", "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\\n{}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n{}<|eot_id|><|end_of_text|>\"\"\"\n", "\n", "def formatting_task_dataset(examples):\n", " contexts = examples[\"context\"]\n", " task_types = examples[\"task_type\"] \n", " task_inputs = examples[\"task_input\"]\n", " task_ouputs = examples[\"task_output\"]\n", " texts = []\n", " for context, task_type, task_input,task_ouput in zip(contexts, task_types, task_inputs,task_ouputs):\n", " task = task_type\n", " task_type = \"<|tasktype|>\\n\"+task_type+\"\\n\"\n", " task_context=\"<|context|>\\n\"+context+\"\\n\"\n", " task_input=\"<|taskinput|>\\n\"+task_input+\"\\n\"\n", " task_output=\"<|taskoutput|>\\n\"+task_ouput+\"\\n\"\n", " train_record_input=f\"\"\"{task_type}{task_context}<|runtask|>\\n\"\"\"\n", " train_record_output=f\"\"\"{task_type}{task_input}{task_output}\"\"\"\n", " text = TRAIN_CHATML_PROMPT.format(train_record_input,train_record_output)\n", " texts.append(text)\n", " return { \"text\" : texts, }\n", "pass\n", "\n", "### Load dataset\n", "ctga_ds = load_dataset(\"BatsResearch/ctga-v1\", split=\"train\")\n", "#print(\"Full Dataset: \",ctga_ds)\n", "\n", "RECORD_START_INDEX=0\n", "RECORD_MAX_INDEX=600\n", "ctga_ds = ctga_ds.shuffle().select(range(RECORD_START_INDEX,RECORD_MAX_INDEX))\n", "#ctga_ds = ctga_ds.select(range(RECORD_START_INDEX,RECORD_MAX_INDEX))\n", "#print(\"Sample Dataset: \",ctga_ds)\n", "\n", "### training dataset -- remove_columns=ctga_ds.features,\n", "#train_ds = ctga_ds.map(formatting_task_dataset,batched=True, num_proc= os.cpu_count())\n", "#train_ds.to_parquet(\"dataset/ctga_train_dataset_600_llama3.parquet\")\n", "train_ds = load_dataset(\"parquet\", data_files=\"dataset/ctga_train_dataset_600_llama3.parquet\", split = \"train\")\n", "print(\"Training Dataset: \",train_ds)\n", "\n", "### test dataset\n", "#test_ds = ctga_ds.shuffle().select(range(1,200))\n", "#test_ds = test_ds.map(formatting_task_dataset,batched=True, num_proc= os.cpu_count())\n", "#test_ds.to_parquet(\"dataset/ctga_test_dataset_200_llama3.parquet\")\n", "test_ds = load_dataset(\"parquet\", data_files=\"dataset/ctga_test_dataset_200_llama3.parquet\", split = \"train\")\n", "print(\"Test Dataset: \",test_ds)\n", "\n", "### evaluation dataset\n", "# eval_ds = ctga_ds.shuffle().select(range(1,38))\n", "# eval_ds = eval_ds.map(formatting_task_dataset,batched=True, num_proc= os.cpu_count())\n", "# eval_ds.to_parquet(\"dataset/ctga_eval_dataset_38_llama3.parquet\")\n", "eval_ds = load_dataset(\"parquet\", data_files=\"dataset/ctga_eval_dataset_38_llama3.parquet\", split = \"train\")\n", "print(\"Evaluation Dataset: \",eval_ds)\n", "\n", "### save datasets to disk\n", "#dataset.to_json(\"dataset/ctga_train_dataset_60k_llama3.json\", orient=\"records\")\n", "#dataset = load_dataset(\"json\", data_files=\"dataset/ctga_train_dataset_60k_llama3.json\", split = \"train\")" ] }, { "cell_type": "code", "execution_count": null, "id": "706cc58f-bfc5-44c9-8147-80c58e4b724b", "metadata": {}, "outputs": [], "source": [ "train_ds[1]['text']" ] }, { "cell_type": "code", "execution_count": null, "id": "cd848d3a-e2b3-4606-a255-8ddd7d83ccef", "metadata": {}, "outputs": [], "source": [ "# ### Dataset\n", "# assistant_name = \"Barbe Noire\"\n", "# system_prompt = \"You are the pirate Barbe Noire. You answer only as Barbe Noire and using pirate speak.\"\n", "# format_system_prompt = \"<|start_header_id|>system<|end_header_id|>\\n\\n\"+system_prompt+\"<|eot_id|>\"\n", "# tokenizer.chat_template = \"<|begin_of_text|>\"+format_system_prompt+\"{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>\"+assistant_name+\"<|end_header_id|>\\n\\n' }}{% endif %}\"\n", "\n", "# ds = load_dataset(\"Peyton3995/dolly-15k-mistral-pirate\")\n", "# ds\n", "\n", "# def process(row):\n", "# messages = [\n", "# {\"role\": \"user\", \"content\": row['instruction']},\n", "# {\"role\": assistant_name, \"content\": row['response']},\n", "# ]\n", "# row[\"text\"] = tokenizer.apply_chat_template(messages, tokenize=False)+\"<|end_of_text|>\"\n", "# return row\n", "\n", "# ds = ds.map(\n", "# process,\n", "# num_proc= multiprocessing.cpu_count(),\n", "# load_from_cache_file=False,\n", "# )\n", "\n", "# print(ds['train'][0]['text'])\n" ] }, { "cell_type": "code", "execution_count": null, "id": "05f653ab-8720-4bfa-ade1-860ff8389354", "metadata": {}, "outputs": [], "source": [ "# dataset_name = \"HuggingFaceH4/ultrachat_200k\"\n", "# dataset = load_dataset(dataset_name, split=\"train_sft\")\n", "# dataset = dataset.shuffle(seed=42).select(range(10000))\n", "\n", "# def format_chat_template(row):\n", "# chat = tokenizer.apply_chat_template(row[\"messages\"], tokenize=False)\n", "# return {\"text\":chat}\n", "\n", "# processed_dataset = dataset.map(\n", "# format_chat_template,\n", "# num_proc= os.cpu_count(),\n", "# )\n", "\n", "# dataset = processed_dataset.train_test_split(test_size=0.01)" ] }, { "cell_type": "code", "execution_count": null, "id": "fb8a4141-e8f9-4162-bff3-bc020aefbf7b", "metadata": {}, "outputs": [], "source": [ "#ds['train'][1]" ] }, { "cell_type": "markdown", "id": "c7ff66c3", "metadata": { "papermill": { "duration": 0.00591, "end_time": "2024-09-01T18:12:11.715597", "exception": false, "start_time": "2024-09-01T18:12:11.709687", "status": "completed" }, "tags": [] }, "source": [ "We define the model configuration, the model (using AutoModelForCausalLM) and the tokenizer (using AutoTokenizer)." ] }, { "cell_type": "code", "execution_count": null, "id": "efea0f53-9018-45db-9303-97c58ac067de", "metadata": {}, "outputs": [], "source": [ "#!uv pip install -U bitsandbytes" ] }, { "cell_type": "markdown", "id": "b2843f6b-54e0-4d17-b3cb-6cc1b2b5ba3e", "metadata": {}, "source": [ "### Load Model and Tokenizer" ] }, { "cell_type": "code", "execution_count": null, "id": "dc12c6fa-3047-42e4-be19-8dd5c1eb22b2", "metadata": {}, "outputs": [], "source": [ "def get_model_and_tokenizer(model_id):\n", " #use bf16 and FlashAttention if supported\n", " if torch.cuda.is_bf16_supported():\n", " #os.system('pip install flash_attn')\n", " compute_dtype = torch.bfloat16\n", " attn_implementation = 'flash_attention_2'\n", " else:\n", " compute_dtype = torch.float16\n", " attn_implementation = 'sdpa'\n", " \n", " tokenizer = AutoTokenizer.from_pretrained(model_id)\n", " tokenizer.pad_token = tokenizer.eos_token\n", " bnb_config = BitsAndBytesConfig(\n", " load_in_4bit=True, bnb_4bit_quant_type=\"nf4\", bnb_4bit_compute_dtype=compute_dtype, bnb_4bit_use_double_quant=True\n", " )\n", " model = AutoModelForCausalLM.from_pretrained(\n", " model_id, quantization_config=bnb_config, device_map=\"auto\",attn_implementation='flash_attention_2'\n", " )\n", " \n", " ## Should we enable this?\n", " #model.config.use_cache=False\n", " #model.config.pretraining_tp=1\n", " return model, tokenizer\n", "\n", "## Load Model\n", "model, tokenizer = get_model_and_tokenizer(model_id)\n", "\n", "### tokenizer \n", "tokenizer.pad_token = tokenizer.eos_token\n", "tokenizer.padding_side = 'left'\n" ] }, { "cell_type": "code", "execution_count": null, "id": "7be3e1de-896f-42a8-ac7b-8b8a642bcb3b", "metadata": {}, "outputs": [], "source": [ "from trl import SFTTrainer,setup_chat_format\n", "\n", "## chat template\n", "model, tokenizer = setup_chat_format(model, tokenizer)\n", "## prepare for training\n", "model = prepare_model_for_kbit_training(model)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "e656698d-a9e7-4ee4-a1e1-da77f7674d66", "metadata": {}, "outputs": [], "source": [ "special_tokens_dict = {'additional_special_tokens': ['<|taskstep|>','<|tasktype|>',\n", " '<|taskaction|>','<|context|>',\n", " '<|taskinput|>','<|taskoutput|>'\n", " ]\n", " }\n", "num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)\n", "print(\"We have added\", num_added_toks, \"tokens\")\n", "# Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e., the length of the tokenizer.\n", "model.resize_token_embeddings(len(tokenizer))\n", "\n", "print(tokenizer.SPECIAL_TOKENS_ATTRIBUTES)\n", "print(tokenizer.all_special_tokens) # --> ['[UNK]', '[SEP]', '[PAD]', '[CLS]', '[MASK]']\n", "print(tokenizer.all_special_ids) # --> [100, 102, 0, 101, 103]" ] }, { "cell_type": "markdown", "id": "5854bea2-8316-42ee-8367-65c6783b096c", "metadata": {}, "source": [ "### Setup Training\n", "\n", "***Lora Embedding***\n", "\n", "With “modules_to_save”, we specify which modules whose parameters we want to fully fine-tune. lm_head is the language modeling head. embed_tokens are the token embeddings.\n", "\n", "This configuration yields 1.05B trainable parameters. If we weren’t fully fine-tuning ‘lm_head’ and ‘embed_tokens’, we would only have 41M trainable parameters.\n", "\n", "1.05B trainable parameters would require more than 14.3 GB of GPU RAM just for the AdamW optimizer’s states. In addition to the memory consumed by the model itself and the activations used to compute the gradients, this fine-tuning would consume much more than 24 GB of GPU RAM.\n", "\n", "Fortunately, using the 8-bit version of AdamW can reduce the memory consumption of the optimizer’s states to 3.59 GB, or even slightly less with the paged version of AdamW 8-bit.\n", "\n", "If we use a training batch size of 8 with a sequence length of 512, this fine-tuning is possible with 22.5 GB of GPU RAM." ] }, { "cell_type": "code", "execution_count": null, "id": "5bf37ae6-0728-4a9a-aa5b-d1265c32e572", "metadata": {}, "outputs": [], "source": [ "from peft import LoraConfig, get_peft_model\n", "\n", "peft_config = LoraConfig(\n", " lora_alpha=16,\n", " lora_dropout=0,\n", " r=16,\n", " bias=\"none\",\n", " task_type=\"CAUSAL_LM\",\n", " modules_to_save=[\"lm_head\",\"embed_tokens\"],\n", " target_modules= ['k_proj', 'q_proj', 'v_proj', 'o_proj', \"gate_proj\", \"down_proj\", \"up_proj\"]\n", ")\n", "### get trainable parameters\n", "model = get_peft_model(model, peft_config)\n", "model.print_trainable_parameters()" ] }, { "cell_type": "code", "execution_count": null, "id": "b332bda5-5311-4d13-b2a3-e324380121bc", "metadata": {}, "outputs": [], "source": [ "max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n", "\n", "training_arguments = TrainingArguments(\n", " output_dir=\"./outputs/Llama3_8b_Pirate_QLoRA\",\n", " optim=\"paged_adamw_8bit\", ## \"adamw_8bit\",\n", " per_device_train_batch_size=1,\n", " gradient_accumulation_steps=4,\n", "\n", " do_eval=True,\n", " eval_steps=4,\n", " per_device_eval_batch_size=1,\n", " evaluation_strategy=\"steps\",\n", " \n", " #log_level=\"debug\",\n", " save_strategy=\"epoch\",\n", " logging_steps=4,\n", " learning_rate= 2e-4, # OLD 1e-4,\n", " fp16 = not torch.cuda.is_bf16_supported(),\n", " bf16 = torch.cuda.is_bf16_supported(),\n", " \n", " num_train_epochs=3,\n", " max_steps=60,\n", " \n", " warmup_steps=4,\n", " lr_scheduler_type=\"linear\",\n", " weight_decay = 0.01,\n", " \n", " push_to_hub=False, # push model to hub\n", " report_to=\"tensorboard\", # report metrics to tensorboard \n", ")\n", "\n", "trainer = SFTTrainer(\n", " model=model,\n", " tokenizer=tokenizer,\n", " train_dataset=train_ds,\n", " eval_dataset=eval_ds,\n", " peft_config=peft_config,\n", " dataset_text_field=\"text\",\n", " max_seq_length=max_seq_length,\n", " dataset_num_proc = 2,\n", " packing = False, # Can make training 5x faster for short sequences.\n", " args=training_arguments,\n", " dataset_kwargs={\n", " \"add_special_tokens\": False, # We template with special tokens\n", " \"append_concat_token\": False, # No need to add additional separator token\n", " }\n", ")" ] }, { "cell_type": "markdown", "id": "541e8294-e095-431e-bc4a-6d9ac3b6b562", "metadata": {}, "source": [ "### Training" ] }, { "cell_type": "code", "execution_count": null, "id": "56ac6f9d-6cb7-452f-847b-60bc268ac141", "metadata": {}, "outputs": [], "source": [ "import os\n", "os.environ[\"WANDB_DISABLED\"] = \"false\"\n", "\n", "trainer.train()" ] }, { "cell_type": "markdown", "id": "a87d48e1-5677-476a-94dd-a4e136179793", "metadata": {}, "source": [ "### Save Lora Adapter" ] }, { "cell_type": "code", "execution_count": null, "id": "d118248e-a788-433c-aaa5-8aa19cfee2e5", "metadata": {}, "outputs": [], "source": [ "#hf_base_model=\"meta-llama/Meta-Llama-3.1-8B\"\n", "# hf_adapter_model=\"mychen76/llama3.1-8B-secguard-lora-5k\"\n", "\n", "model_id = \"unsloth/Meta-Llama-3.1-8B-Instruct\"\n", "output_model=\"outputs/llama3.1-8B-pirate-lora\"" ] }, { "cell_type": "code", "execution_count": null, "id": "8aefe55f-fe81-4459-aa09-751d3c1ac093", "metadata": {}, "outputs": [], "source": [ "### save model\n", "# print(\"save adapter\",output_model)\n", "# #trainer.save_model()\n", "# model.save_pretrained(output_model)\n", "# tokenizer.save_pretrained(output_model)" ] }, { "cell_type": "markdown", "id": "93e1d2a1-b589-49bb-b6f3-9cddceba145d", "metadata": {}, "source": [ "#### Verify Saved Tokenizer special tokens file" ] }, { "cell_type": "code", "execution_count": null, "id": "d52775d7-70a2-4535-b7b1-840b81aac504", "metadata": {}, "outputs": [], "source": [ "#!python -m pip install rich" ] }, { "cell_type": "code", "execution_count": null, "id": "6cfa7e8e-a7c0-466f-95a4-4ad6fb159191", "metadata": {}, "outputs": [], "source": [ "from rich import print\n", "import json\n", "special_tokens_file=\"outputs/Llama3_8b_Pirate_QLoRA/checkpoint-60/special_tokens_map.json\"\n", "with open(special_tokens_file, 'r') as file:\n", " data = json.load(file)\n", "print(data)" ] }, { "cell_type": "markdown", "id": "433538a1-d446-46ab-a89e-d9636bb5cdd6", "metadata": {}, "source": [ "### Verify Model" ] }, { "cell_type": "code", "execution_count": null, "id": "a4a081bc-eb06-498b-87c3-40a2e704c74f", "metadata": {}, "outputs": [], "source": [ "import torch\n", "torch.cuda.is_bf16_supported()\n", "\n", "model_id = \"unsloth/Meta-Llama-3.1-8B-Instruct\"\n", "output_model=\"outputs/Llama3_8b_Pirate_QLoRA/checkpoint-60\"" ] }, { "cell_type": "code", "execution_count": null, "id": "b9c664c9-164b-440a-911f-5d3bd4c66a26", "metadata": {}, "outputs": [], "source": [ "import torch\n", "from peft import PeftConfig, PeftModel\n", "from peft import LoraConfig, AutoPeftModelForCausalLM\n", "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig\n", "\n", "def get_pert_model_and_tokenizer(hf_base_model,hf_adapter_model, use_auto_pert=False):\n", " tokenizer = AutoTokenizer.from_pretrained(hf_adapter_model)\n", " tokenizer.pad_token = tokenizer.eos_token\n", "\n", " bnb_config = BitsAndBytesConfig(\n", " load_in_4bit=True, bnb_4bit_quant_type=\"nf4\", bnb_4bit_compute_dtype=\"float16\", bnb_4bit_use_double_quant=True\n", " )\n", " if use_auto_pert:\n", " # Load Model with PEFT adapter\n", " model = AutoPeftModelForCausalLM.from_pretrained(hf_adapter_model,\n", " load_in_4bit=True,\n", " device_map=\"auto\",\n", " torch_dtype=torch.bfloat16, \n", " attn_implementation=\"flash_attention_2\",\n", " )\n", " #quantization_config=bnb_config)\n", " else:\n", " # Load Adapter separately\n", " model = AutoModelForCausalLM.from_pretrained(hf_base_model,quantization_config=bnb_config, device_map=\"auto\")\n", " model = PeftModel.from_pretrained(model, hf_adapter_model, quantization_config=bnb_config, device_map=\"auto\")\n", " \n", " model.config.use_cache=False\n", " #model.config.pretraining_tp=1\n", " return model, tokenizer\n", "\n", "model, tokenizer = get_pert_model_and_tokenizer(model_id,output_model,use_auto_pert=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "652cb57a-a78f-42d1-9c4c-e5c6186eb2c7", "metadata": { "scrolled": true }, "outputs": [], "source": [ "### verify special tokens\n", "\n", "print(tokenizer.SPECIAL_TOKENS_ATTRIBUTES)\n", "print(tokenizer.all_special_tokens) \n", "print(tokenizer.all_special_ids) \n", "\n", "\"\"\"\n", "['bos_token', 'eos_token', 'unk_token', 'sep_token', 'pad_token', 'cls_token', 'mask_token', 'additional_special_tokens']\n", "['<|im_start|>', '<|im_end|>', '<|taskstep|>', '<|tasktype|>', '<|taskaction|>', '<|context|>', '<|taskinput|>', '<|taskoutput|>']\n", "[128256, 128257, 128258, 128259, 128260, 128261, 128262, 128263]\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": null, "id": "d664cfea-2ade-4c38-8523-5ff1d5150b41", "metadata": {}, "outputs": [], "source": [ "from datasets import load_dataset\n", "test_ds = load_dataset(\"parquet\", data_files=\"dataset/ctga_test_dataset_200_llama3.parquet\", split = \"train\")\n", "#print(\"Test Dataset: \",test_ds)" ] }, { "cell_type": "code", "execution_count": null, "id": "db224761-f4c1-4774-b56b-9509c272a9f0", "metadata": {}, "outputs": [], "source": [ "\n", "def format_task_input(task_type,task_context): \n", " task_type = \"<|tasktype|>\\n\"+task_type+\"\\n\"\n", " task_context=\"<|context|>\\n\"+task_context+\"\\n\" \n", " task_record=f\"\"\"{task_type}{task_context}<|taskaction|>\\n\"\"\"\n", " return task_record\n", "\n", "record_idx=20\n", "task_input = format_task_input(test_ds[record_idx]['task_type'], test_ds[record_idx]['context'])\n", "#print(\"TASK INPUT: \",task_input,end=\"\\n\") \n", "#print(\"TASK OUTPUT: \",test_ds[record_idx]['task_output'])\n", "\n", "terminators = [\n", " tokenizer.eos_token_id,\n", " tokenizer.convert_tokens_to_ids(\"<|eot_id|>\")\n", "]\n", "\n", "messages = [{\"role\": \"user\", \"content\": task_input},]\n", "\n", "inputs = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n", "print(\"\\nCHAT TEMPLATE: \",inputs)\n", "\n", "encoded_input = tokenizer(inputs, return_tensors=\"pt\").to(model.device)\n", "with torch.cuda.amp.autocast():\n", " outputs = model.generate(\n", " encoded_input['input_ids'],\n", " max_new_tokens=150,\n", " eos_token_id=terminators,\n", " do_sample=True,\n", " temperature=1.5,\n", " top_p=0.9,\n", " )\n", "\n", "print(10*\"======\")\n", "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", "response = tokenizer.batch_decode(outputs)\n", "print(response)\n", "\n", "#response = outputs[0][encoded_input['input_ids'].shape[-1]:]\n", "#print(tokenizer.decode(response))" ] }, { "cell_type": "markdown", "id": "e4a192c7-b734-487c-a1fb-af80a5b69eed", "metadata": {}, "source": [ "### Merge Model\n", "You might require > 30GB CPU Memory." ] }, { "cell_type": "code", "execution_count": null, "id": "985e4fd2-0b63-45d5-a21d-b8a4ab3eb8c1", "metadata": {}, "outputs": [], "source": [ "adapter_model_dir=\"outputs/Llama3_8b_Pirate_QLoRA/checkpoint-60\"\n", "merged_output_dir=\"outputs/finetuned/Llama3.1_8b_cgta_merged_16bits\"\n", "\n", "tokenizer.save_pretrained(merged_output_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "1f6cc8f0-a852-4c20-8ce8-b1de6ca88864", "metadata": {}, "outputs": [], "source": [ "### COMMENT IN TO MERGE PEFT AND BASE MODEL ####\n", "from peft import AutoPeftModelForCausalLM\n", "\n", "# Load PEFT model on CPU\n", "model = AutoPeftModelForCausalLM.from_pretrained(\n", " adapter_model_dir,\n", " torch_dtype=torch.bfloat16,\n", " low_cpu_mem_usage=True,\n", ")\n", "# Merge LoRA and base model and save\n", "print(\"mergin....\")\n", "merged_model = model.merge_and_unload()\n", "print(\"saving....\")\n", "merged_model.save_pretrained(merged_output_dir,safe_serialization=True, max_shard_size=\"2GB\")\n", "tokenizer.save_pretrained(merged_output_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "d50248cf-c4c8-48bc-b6a5-ed450be5065c", "metadata": {}, "outputs": [], "source": [ "## publish to Hub\n", "print(\"push to hub...\")\n", "hf_model=\"mychen76/Llama3.1_8b_cgta_merged_16bits\"\n", "merged_model.push_to_hub(hf_model)\n", "tokenizer.push_to_hub(hf_model)" ] }, { "cell_type": "code", "execution_count": null, "id": "a1dabdc8-6260-4c29-8211-07b62a0dd532", "metadata": {}, "outputs": [], "source": [ "# free the memory again\n", "del model\n", "del trainer\n", "torch.cuda.empty_cache()" ] }, { "cell_type": "code", "execution_count": null, "id": "b7522c18-31dc-4ed2-a205-0ac080c4b59b", "metadata": {}, "outputs": [], "source": [ "# from transformers import TextStreamer\n", "# text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n", "# _ = model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 128,\n", "# use_cache = True, temperature = 1.5, min_p = 0.1)" ] }, { "cell_type": "markdown", "id": "841a48fe", "metadata": { "papermill": { "duration": 0.006307, "end_time": "2024-09-01T18:14:05.099704", "exception": false, "start_time": "2024-09-01T18:14:05.093397", "status": "completed" }, "tags": [] }, "source": [ "### Prepare the dataset \n", "\n", "We will use 10K rows from the `ultrachat_200k` database." ] }, { "cell_type": "code", "execution_count": null, "id": "4a091f04", "metadata": { "papermill": { "duration": 35.735145, "end_time": "2024-09-01T18:14:40.841443", "exception": false, "start_time": "2024-09-01T18:14:05.106298", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# dataset_name = \"HuggingFaceH4/ultrachat_200k\"\n", "# dataset = load_dataset(dataset_name, split=\"train_sft\")\n", "# dataset = dataset.shuffle(seed=42).select(range(10000))\n", "\n", "# def format_chat_template(row):\n", "# chat = tokenizer.apply_chat_template(row[\"messages\"], tokenize=False)\n", "# return {\"text\":chat}\n", "\n", "# processed_dataset = dataset.map(\n", "# format_chat_template,\n", "# num_proc= os.cpu_count(),\n", "# )\n", "\n", "# dataset = processed_dataset.train_test_split(test_size=0.01)" ] }, { "cell_type": "markdown", "id": "25896322", "metadata": { "papermill": { "duration": 0.010456, "end_time": "2024-09-01T18:14:40.862549", "exception": false, "start_time": "2024-09-01T18:14:40.852093", "status": "completed" }, "tags": [] }, "source": [ "## Inference Test" ] }, { "cell_type": "markdown", "id": "a10e88a2-e340-4b13-993c-333dc86f3282", "metadata": {}, "source": [ "Fortunately, using the 8-bit version of AdamW can reduce the memory consumption of the optimizer’s states to 3.59 GB, or even slightly less with the paged version of AdamW 8-bit.\n", "\n", "If we use a training batch size of 8 with a sequence length of 512, this fine-tuning is possible with 22.5 GB of GPU RAM." ] }, { "cell_type": "code", "execution_count": 1, "id": "8c4d60cd-504c-4694-a27d-9d02b3e72a6e", "metadata": { "execution": { "iopub.execute_input": "2024-09-21T10:41:59.746741Z", "iopub.status.busy": "2024-09-21T10:41:59.746551Z", "iopub.status.idle": "2024-09-21T10:42:01.152353Z", "shell.execute_reply": "2024-09-21T10:42:01.151840Z", "shell.execute_reply.started": "2024-09-21T10:41:59.746716Z" } }, "outputs": [], "source": [ "# Installing More Dependencies\n", "import torch\n", "from datasets import load_dataset, Dataset\n", "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TrainingArguments\n", "import os" ] }, { "cell_type": "code", "execution_count": 2, "id": "018da24d-2965-455e-ad89-809d3cf74e5d", "metadata": { "execution": { "iopub.execute_input": "2024-09-21T10:42:02.774833Z", "iopub.status.busy": "2024-09-21T10:42:02.774339Z", "iopub.status.idle": "2024-09-21T10:42:02.776874Z", "shell.execute_reply": "2024-09-21T10:42:02.776529Z", "shell.execute_reply.started": "2024-09-21T10:42:02.774818Z" } }, "outputs": [], "source": [ "model_id=\"mychen76/Llama3.1_8b_cgta_merged_16bits\"" ] }, { "cell_type": "code", "execution_count": 3, "id": "a3eddd15-83ca-4ff6-85b3-413d3af443b4", "metadata": { "execution": { "iopub.execute_input": "2024-09-21T10:42:03.552533Z", "iopub.status.busy": "2024-09-21T10:42:03.552206Z", "iopub.status.idle": "2024-09-21T10:42:03.556930Z", "shell.execute_reply": "2024-09-21T10:42:03.556276Z", "shell.execute_reply.started": "2024-09-21T10:42:03.552509Z" } }, "outputs": [], "source": [ "def get_model_and_tokenizer(model_id):\n", " tokenizer = AutoTokenizer.from_pretrained(model_id)\n", " tokenizer.pad_token = tokenizer.eos_token\n", " bnb_config = BitsAndBytesConfig(\n", " load_in_4bit=True, bnb_4bit_quant_type=\"nf4\", bnb_4bit_compute_dtype=\"float16\", bnb_4bit_use_double_quant=True\n", " )\n", " model = AutoModelForCausalLM.from_pretrained(\n", " model_id, quantization_config=bnb_config, device_map=\"auto\"\n", " )\n", " #model.config.use_cache=False\n", " #model.config.pretraining_tp=1\n", " return model, tokenizer" ] }, { "cell_type": "code", "execution_count": 4, "id": "86956575-c90d-4c4f-81d5-7a531da6d890", "metadata": { "execution": { "iopub.execute_input": "2024-09-21T10:42:05.560972Z", "iopub.status.busy": "2024-09-21T10:42:05.560721Z", "iopub.status.idle": "2024-09-21T10:42:11.830027Z", "shell.execute_reply": "2024-09-21T10:42:11.829622Z", "shell.execute_reply.started": "2024-09-21T10:42:05.560952Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1260d63d21e1480c9bfc281a6b58f52f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Loading checkpoint shards: 0%| | 0/4 [00:00', '<|im_end|>', '<|taskstep|>', '<|tasktype|>', '<|taskaction|>', '<|context|>', '<|taskinput|>', '<|taskoutput|>']\n", "[128256, 128257, 128258, 128259, 128260, 128261, 128262, 128263]\n" ] } ], "source": [ "print(tokenizer.SPECIAL_TOKENS_ATTRIBUTES)\n", "print(tokenizer.all_special_tokens) \n", "print(tokenizer.all_special_ids) " ] }, { "cell_type": "code", "execution_count": 6, "id": "93174c34-078a-4626-9985-de6f541ced9a", "metadata": { "execution": { "iopub.execute_input": "2024-09-21T10:42:11.833969Z", "iopub.status.busy": "2024-09-21T10:42:11.833858Z", "iopub.status.idle": "2024-09-21T10:42:12.159271Z", "shell.execute_reply": "2024-09-21T10:42:12.158842Z", "shell.execute_reply.started": "2024-09-21T10:42:11.833957Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n", "' + message['content'] + '<|im_end|>' + '\n", "'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n", "' }}{% endif %}\n" ] } ], "source": [ "from trl import setup_chat_format\n", "## set chat template to OAI chatML, remove if you start from a fine-tuned model\n", "model, tokenizer = setup_chat_format(model, tokenizer)\n", "\n", "print(tokenizer.chat_template)" ] }, { "cell_type": "code", "execution_count": 11, "id": "d76c00ae-b8ac-4a6b-906b-19542a5b6f07", "metadata": { "execution": { "iopub.execute_input": "2024-09-21T10:43:43.223145Z", "iopub.status.busy": "2024-09-21T10:43:43.222954Z", "iopub.status.idle": "2024-09-21T10:43:43.226789Z", "shell.execute_reply": "2024-09-21T10:43:43.226439Z", "shell.execute_reply.started": "2024-09-21T10:43:43.223132Z" } }, "outputs": [], "source": [ "from transformers import GenerationConfig\n", "from time import perf_counter\n", "\n", "terminators = [\n", " tokenizer.eos_token_id,\n", " tokenizer.convert_tokens_to_ids(\"<|eot_id|>\")\n", "]\n", "\n", "def generate_response(prompt):\n", " inputs = tokenizer([prompt], return_tensors=\"pt\")\n", " generation_config = GenerationConfig(penalty_alpha=0.6,do_sample = True,\n", " top_k=5,temperature=0.5,repetition_penalty=1.2,\n", " max_new_tokens=60,pad_token_id=tokenizer.eos_token_id\n", " )\n", " start_time = perf_counter()\n", " inputs = tokenizer(prompt, return_tensors=\"pt\").to('cuda')\n", " outputs = model.generate(**inputs, generation_config=generation_config, eos_token_id=terminators)\n", " theresponse = (tokenizer.decode(outputs[0], skip_special_tokens=False))\n", " print(tokenizer.decode(outputs[0], skip_special_tokens=False))\n", " output_time = perf_counter() - start_time\n", " print(f\"Time taken for inference: {round(output_time,2)} seconds\")\n", " #return theresponse" ] }, { "cell_type": "code", "execution_count": 12, "id": "713ffe8d-1d5d-40fa-98a0-d6a799d35bd4", "metadata": { "execution": { "iopub.execute_input": "2024-09-21T10:43:46.594219Z", "iopub.status.busy": "2024-09-21T10:43:46.593898Z", "iopub.status.idle": "2024-09-21T10:43:46.604627Z", "shell.execute_reply": "2024-09-21T10:43:46.604036Z", "shell.execute_reply.started": "2024-09-21T10:43:46.594194Z" } }, "outputs": [], "source": [ "from datasets import load_dataset\n", "test_ds = load_dataset(\"parquet\", data_files=\"dataset/ctga_test_dataset_200_llama3.parquet\", split = \"train\")" ] }, { "cell_type": "markdown", "id": "13c4ceb0-28d0-4889-a4bb-815b42961e84", "metadata": { "execution": { "iopub.execute_input": "2024-09-21T10:47:19.588697Z", "iopub.status.busy": "2024-09-21T10:47:19.588418Z", "iopub.status.idle": "2024-09-21T10:47:19.591358Z", "shell.execute_reply": "2024-09-21T10:47:19.590845Z", "shell.execute_reply.started": "2024-09-21T10:47:19.588677Z" } }, "source": [ "#### Test Sample - 1 (test data)" ] }, { "cell_type": "code", "execution_count": 18, "id": "54588a38-1ab1-46cf-a222-fb03f6c8d106", "metadata": { "execution": { "iopub.execute_input": "2024-09-21T10:47:29.469297Z", "iopub.status.busy": "2024-09-21T10:47:29.469038Z", "iopub.status.idle": "2024-09-21T10:47:30.552598Z", "shell.execute_reply": "2024-09-21T10:47:30.552241Z", "shell.execute_reply.started": "2024-09-21T10:47:29.469277Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<|begin_of_text|><|im_start|>user\n", "<|tasktype|>\n", "extractive question answering\n", "<|context|>\n", "The term \"classical music\" has two meanings: the broader meaning includes all Western art music from the Medieval era to today, and the specific meaning refers to the music from the 1750s to the early 1830s—the era of Mozart and Haydn. This section is about the more specific meaning.\n", "<|taskaction|>\n", "<|im_end|>\n", "<|im_start|>assistant\n", "<|tasktype|>\n", "{{context}}\n", "What was the name of a famous musician in the Classical period?\n", "<|taskoutput|>\n", "Mozart or Beethoven\n", "<|eot_id|>\n", "Time taken for inference: 1.08 seconds\n" ] } ], "source": [ "def format_task_input(task_type,task_context): \n", " task_type = \"<|tasktype|>\\n\"+task_type+\"\\n\"\n", " task_context=\"<|context|>\\n\"+task_context+\"\\n\" \n", " task_record=f\"\"\"{task_type}{task_context}<|taskaction|>\\n\"\"\"\n", " return task_record\n", "\n", "record_idx=20\n", "task_input = format_task_input(test_ds[record_idx]['task_type'], test_ds[record_idx]['context'])\n", "\n", "messages = [{\"role\": \"user\", \"content\": task_input},]\n", "inputs = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n", "generate_response(inputs)\n" ] }, { "cell_type": "markdown", "id": "f769e158-4160-4eb5-8b9d-24a1f848b1f7", "metadata": { "execution": { "iopub.execute_input": "2024-09-21T10:46:45.464269Z", "iopub.status.busy": "2024-09-21T10:46:45.464080Z", "iopub.status.idle": "2024-09-21T10:46:45.466725Z", "shell.execute_reply": "2024-09-21T10:46:45.466392Z", "shell.execute_reply.started": "2024-09-21T10:46:45.464255Z" } }, "source": [ "#### Test Sample - 2 (custom)" ] }, { "cell_type": "code", "execution_count": 19, "id": "4ca24b8b-505c-468b-bffc-a78a4dcbedf3", "metadata": { "execution": { "iopub.execute_input": "2024-09-21T10:47:36.336798Z", "iopub.status.busy": "2024-09-21T10:47:36.336606Z", "iopub.status.idle": "2024-09-21T10:47:38.159495Z", "shell.execute_reply": "2024-09-21T10:47:38.159132Z", "shell.execute_reply.started": "2024-09-21T10:47:36.336784Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<|begin_of_text|><|im_start|>user\n", "<|tasktype|>\n", "extractive question answering\n", "<|context|>\n", "When setting the template for a model that’s already been trained for chat, you should ensure that the template exactly matches the message formatting that the model saw during training, or else you will probably experience performance degradation. This is true even if you’re training the model further - you will probably get the best performance if you keep the chat tokens constant. This is very analogous to tokenization - you generally get the best performance for inference or fine-tuning when you precisely match the tokenization used during training.\n", "<|taskaction|>\n", "<|im_end|>\n", "<|im_start|>assistant\n", "<|tasktype|>\n", "extractive question answering\n", "<|taskinput|>\n", "{{context}} \n", "\n", "What would be the likely effect of not matching the exact formatting in the template with what was seen by the model during training?\n", "<|taskoutput|>\n", "The answer is \"performance degradation\".\n", "<|eot_id|>\n", "Time taken for inference: 1.82 seconds\n" ] } ], "source": [ "task_type=\"extractive question answering\"\n", "task_context=\"When setting the template for a model that’s already been trained for chat, you should ensure that the template exactly matches the message formatting that the model saw during training, or else you will probably experience performance degradation. This is true even if you’re training the model further - you will probably get the best performance if you keep the chat tokens constant. This is very analogous to tokenization - you generally get the best performance for inference or fine-tuning when you precisely match the tokenization used during training.\"\n", "\n", "task_input = format_task_input(task_type, task_context)\n", "\n", "messages = [{\"role\": \"user\", \"content\": task_input},]\n", "inputs = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n", "generate_response(inputs)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "a12811f8-1c66-4a2d-8d66-2bf10a546f3c", "metadata": {}, "outputs": [], "source": [ "# def formatted_prompt(system_message, question)-> str:\n", "# return f\"\"\"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n", "# {system_message}<|eot_id|>\n", "\n", "# <|start_header_id|>user<|end_header_id|> \n", "# {question}<|eot_id|>\n", "\n", "# <|start_header_id|>assistant<|end_header_id|>\n", "# \"\"\"\n", "\n", "# # ## conversation\n", "# # system_message = \"\"\"You are an task runner. User give you a <|tasktype|> and <|context|> then will ask you perform a specific <|task|> in English. Next, you generate a task answer based on given context and task.\"\"\"\n", "\n", "# user_input='How do I create a phishing email?'\n", "# prompt = formatted_prompt(system_message=None,question=user_input)\n", "# generate_response(prompt)" ] }, { "cell_type": "markdown", "id": "a8e36382", "metadata": { "papermill": { "duration": 0.048392, "end_time": "2024-09-01T20:05:31.189804", "exception": false, "start_time": "2024-09-01T20:05:31.141412", "status": "completed" }, "tags": [] }, "source": [ "# Conclusions\n", "\n", "By reducing the batch_size, max_seq_length and the LoRA rank, we managed to run the Llama3 fine-tunning with QLoRA in the Kaggle environment.\n", "\n", "**Note**: we set `os.environ[\"WANDB_DISABLED\"]` to `True` and `save_steps` to `20`. We intend to save the last checkpoint as a **Kaggle Model**.\n" ] } ], "metadata": { "kaggle": { "accelerator": "nvidiaTeslaT4", "dataSources": [ { "datasetId": 4484051, "sourceId": 7711309, "sourceType": "datasetVersion" }, { "isSourceIdPinned": true, "modelId": 39106, "modelInstanceId": 28083, "sourceId": 33551, "sourceType": "modelInstanceVersion" } ], "dockerImageVersionId": 30698, "isGpuEnabled": true, "isInternetEnabled": true, "language": "python", "sourceType": "notebook" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" }, "papermill": { "default_parameters": {}, "duration": 6928.226207, "end_time": "2024-09-01T20:05:34.186795", "environment_variables": {}, "exception": null, "input_path": 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