Add model details and set training parameters

train_llm.ipynb

@@ -0,0 +1,302 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from uuid import uuid4\n",
+    "import pandas as pd\n",
+    "\n",
+    "from datasets import load_dataset\n",
+    "import subprocess\n",
+    "from transformers import AutoTokenizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from dotenv import load_dotenv,find_dotenv\n",
+    "# load_dotenv(find_dotenv(),override=True)\n",
+    "\n",
+    "def max_token_len(dataset):\n",
+    "    max_seq_length = 0\n",
+    "    for row in dataset:\n",
+    "        tokens = len(tokenizer(row['text'])['input_ids'])\n",
+    "        if tokens > max_seq_length:\n",
+    "            max_seq_length = tokens\n",
+    "    return max_seq_length"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model Max Length: 1000000000000000019884624838656\n"
+     ]
+    }
+   ],
+   "source": [
+    "# model_name='TinyLlama/TinyLlama-1.1B-Chat-v0.1'\n",
+    "model_name = 'mistralai/Mistral-7B-v0.1'\n",
+    "# model_name = 'distilbert-base-uncased'\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+    "model_max_length = tokenizer.model_max_length\n",
+    "print(\"Model Max Length:\", model_max_length)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Max token length train: 1121\n",
+      "Max token length validation: 38\n",
+      "Block size: 2242\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load dataset\n",
+    "dataset_name = 'ai-aerospace/ams_data_train_generic_v0.1_100'\n",
+    "dataset=load_dataset(dataset_name)\n",
+    "\n",
+    "# Write dataset files into data directory\n",
+    "data_directory = './fine_tune_data/'\n",
+    "\n",
+    "# Create the data directory if it doesn't exist\n",
+    "os.makedirs(data_directory, exist_ok=True)\n",
+    "\n",
+    "# Write the train data to a CSV file\n",
+    "train_data='train_data'\n",
+    "train_filename = os.path.join(data_directory, train_data)\n",
+    "dataset['train'].to_pandas().to_csv(train_filename+'.csv', columns=['text'], index=False)\n",
+    "max_token_length_train=max_token_len(dataset['train'])\n",
+    "print('Max token length train: '+str(max_token_length_train))\n",
+    "\n",
+    "# Write the validation data to a CSV file\n",
+    "validation_data='validation_data'\n",
+    "validation_filename = os.path.join(data_directory, validation_data)\n",
+    "dataset['validation'].to_pandas().to_csv(validation_filename+'.csv', columns=['text'], index=False)\n",
+    "max_token_length_validation=max_token_len(dataset['validation'])\n",
+    "print('Max token length validation: '+str(max_token_length_validation))\n",
+    "      \n",
+    "max_token_length=max(max_token_length_train,max_token_length_validation)\n",
+    "if max_token_length > model_max_length:\n",
+    "    raise ValueError(\"Maximum token length exceeds model limits.\")\n",
+    "block_size=2*max_token_length\n",
+    "print('Block size: '+str(block_size))\n",
+    "\n",
+    "# Define project parameters\n",
+    "username='ai-aerospace'\n",
+    "project_name='./llms/'+'ams_data_train-100_'+str(uuid4())\n",
+    "repo_name='ams-data-train-100-'+str(uuid4())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'project_name': './llms/ams_data_train-100_6abb23dc-cb9d-428e-9079-e47deee0edd9', 'model_name': 'mistralai/Mistral-7B-v0.1', 'repo_id': 'ai-aerospace/ams-data-train-100-4601c8c8-0903-4f18-a6e8-1d2a40a697ce', 'train_data': 'train_data', 'validation_data': 'validation_data', 'data_directory': './fine_tune_data/', 'block_size': 2242, 'model_max_length': 1121, 'logging_steps': -1, 'evaluation_strategy': 'epoch', 'save_total_limit': 1, 'save_strategy': 'epoch', 'mixed_precision': 'fp16', 'lr': 3e-05, 'epochs': 3, 'batch_size': 2, 'warmup_ratio': 0.1, 'gradient_accumulation': 1, 'optimizer': 'adamw_torch', 'scheduler': 'linear', 'weight_decay': 0, 'max_grad_norm': 1, 'seed': 42, 'quantization': 'int4', 'lora_r': 16, 'lora_alpha': 32, 'lora_dropout': 0.05}\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "This set of parameters runs on a low memory gpu on hugging face spaces:\n",
+    "{\n",
+    "  \"block_size\": 1024,\n",
+    "  \"model_max_length\": 2048,\n",
+    "  x\"use_flash_attention_2\": false,\n",
+    "  x\"disable_gradient_checkpointing\": false,\n",
+    "  \"logging_steps\": -1,\n",
+    "  \"evaluation_strategy\": \"epoch\",\n",
+    "  \"save_total_limit\": 1,\n",
+    "  \"save_strategy\": \"epoch\",\n",
+    "  x\"auto_find_batch_size\": false,\n",
+    "  \"mixed_precision\": \"fp16\",\n",
+    "  \"lr\": 0.00003,\n",
+    "  \"epochs\": 3,\n",
+    "  \"batch_size\": 2,\n",
+    "  \"warmup_ratio\": 0.1,\n",
+    "  \"gradient_accumulation\": 1,\n",
+    "  \"optimizer\": \"adamw_torch\",\n",
+    "  \"scheduler\": \"linear\",\n",
+    "  \"weight_decay\": 0,\n",
+    "  \"max_grad_norm\": 1,\n",
+    "  \"seed\": 42,\n",
+    "  \"apply_chat_template\": false,\n",
+    "  \"quantization\": \"int4\",\n",
+    "  \"target_modules\": \"\",\n",
+    "  x\"merge_adapter\": false,\n",
+    "  \"peft\": true,\n",
+    "  \"lora_r\": 16,\n",
+    "  \"lora_alpha\": 32,\n",
+    "  \"lora_dropout\": 0.05\n",
+    "}\n",
+    "\"\"\"\n",
+    "\n",
+    "model_params={\n",
+    "  \"project_name\": project_name,\n",
+    "  \"model_name\": model_name,\n",
+    "  \"repo_id\": username+'/'+repo_name,\n",
+    "  \"train_data\": train_data,\n",
+    "  \"validation_data\": validation_data,\n",
+    "  \"data_directory\": data_directory,\n",
+    "  \"block_size\": block_size,\n",
+    "  \"model_max_length\": max_token_length,\n",
+    "  \"logging_steps\": -1,\n",
+    "  \"evaluation_strategy\": \"epoch\",\n",
+    "  \"save_total_limit\": 1,\n",
+    "  \"save_strategy\": \"epoch\",\n",
+    "  \"mixed_precision\": \"fp16\",\n",
+    "  \"lr\": 0.00003,\n",
+    "  \"epochs\": 3,\n",
+    "  \"batch_size\": 2,\n",
+    "  \"warmup_ratio\": 0.1,\n",
+    "  \"gradient_accumulation\": 1,\n",
+    "  \"optimizer\": \"adamw_torch\",\n",
+    "  \"scheduler\": \"linear\",\n",
+    "  \"weight_decay\": 0,\n",
+    "  \"max_grad_norm\": 1,\n",
+    "  \"seed\": 42,\n",
+    "  \"quantization\": \"int4\",\n",
+    "  \"lora_r\": 16,\n",
+    "  \"lora_alpha\": 32,\n",
+    "  \"lora_dropout\": 0.05\n",
+    "}\n",
+    "for key, value in model_params.items():\n",
+    "  os.environ[key] = str(value)\n",
+    "\n",
+    "print(model_params)\n",
+    "\n",
+    "\n",
+    "# Save parameters to environment variables\n",
+    "# os.environ[\"project_name\"] = project_name\n",
+    "# os.environ[\"model_name\"] = model_name\n",
+    "# os.environ[\"repo_id\"] = username+'/'+repo_name\n",
+    "# os.environ[\"train_data\"] = train_data   \n",
+    "# os.environ[\"validation_data\"] = validation_data\n",
+    "# os.environ[\"data_directory\"] = data_directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "⚠️ WARNING | 2023-12-22 10:39:42 | autotrain.cli.run_dreambooth:<module>:14 - ❌ Some DreamBooth components are missing! Please run `autotrain setup` to install it. Ignore this warning if you are not using DreamBooth or running `autotrain setup` already.\n",
+      "usage: autotrain <command> [<args>]\n",
+      "AutoTrain advanced CLI: error: unrecognized arguments: --batch_size 2\n"
+     ]
+    },
+    {
+     "ename": "CalledProcessError",
+     "evalue": "Command '\nautotrain llm --train     --trainer sft     --project_name ./llms/ams_data_train-100_6abb23dc-cb9d-428e-9079-e47deee0edd9     --model mistralai/Mistral-7B-v0.1     --data_path ./fine_tune_data/     --train_split train_data     --valid_split validation_data     --repo_id ai-aerospace/ams-data-train-100-4601c8c8-0903-4f18-a6e8-1d2a40a697ce     --push_to_hub     --token HUGGINGFACE_TOKEN     --block_size 2242     --model_max_length 1121     --logging_steps -1     --evaluation_strategy epoch     --save_total_limit 1     --save_strategy epoch     --fp16     --lr 3e-05     --num_train_epochs 3     --batch_size 2     --warmup_ratio 0.1     --gradient_accumulation 1     --optimizer adamw_torch     --scheduler linear     --weight_decay 0     --max_grad_norm 1     --seed 42     --use_int4     --use-peft     --lora_r 16     --lora_alpha 32     --lora_dropout 0.05\n' returned non-zero exit status 2.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mCalledProcessError\u001b[0m                        Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[49], line 40\u001b[0m\n\u001b[1;32m      4\u001b[0m command\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;124mautotrain llm --train \u001b[39m\u001b[38;5;130;01m\\\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;124m    --trainer sft \u001b[39m\u001b[38;5;130;01m\\\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[38;5;124m    --lora_dropout \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel_params[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlora_dropout\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\n\u001b[1;32m     37\u001b[0m \u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;66;03m# Use subprocess.run() to execute the command\u001b[39;00m\n\u001b[0;32m---> 40\u001b[0m \u001b[43msubprocess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcommand\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/usr/lib/python3.11/subprocess.py:571\u001b[0m, in \u001b[0;36mrun\u001b[0;34m(input, capture_output, timeout, check, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m    569\u001b[0m     retcode \u001b[38;5;241m=\u001b[39m process\u001b[38;5;241m.\u001b[39mpoll()\n\u001b[1;32m    570\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m check \u001b[38;5;129;01mand\u001b[39;00m retcode:\n\u001b[0;32m--> 571\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m CalledProcessError(retcode, process\u001b[38;5;241m.\u001b[39margs,\n\u001b[1;32m    572\u001b[0m                                  output\u001b[38;5;241m=\u001b[39mstdout, stderr\u001b[38;5;241m=\u001b[39mstderr)\n\u001b[1;32m    573\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m CompletedProcess(process\u001b[38;5;241m.\u001b[39margs, retcode, stdout, stderr)\n",
+      "\u001b[0;31mCalledProcessError\u001b[0m: Command '\nautotrain llm --train     --trainer sft     --project_name ./llms/ams_data_train-100_6abb23dc-cb9d-428e-9079-e47deee0edd9     --model mistralai/Mistral-7B-v0.1     --data_path ./fine_tune_data/     --train_split train_data     --valid_split validation_data     --repo_id ai-aerospace/ams-data-train-100-4601c8c8-0903-4f18-a6e8-1d2a40a697ce     --push_to_hub     --token HUGGINGFACE_TOKEN     --block_size 2242     --model_max_length 1121     --logging_steps -1     --evaluation_strategy epoch     --save_total_limit 1     --save_strategy epoch     --fp16     --lr 3e-05     --num_train_epochs 3     --batch_size 2     --warmup_ratio 0.1     --gradient_accumulation 1     --optimizer adamw_torch     --scheduler linear     --weight_decay 0     --max_grad_norm 1     --seed 42     --use_int4     --use-peft     --lora_r 16     --lora_alpha 32     --lora_dropout 0.05\n' returned non-zero exit status 2."
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Set .venv and execute the autotrain script\n",
+    "# To see all parameters: autotrain llm --help\n",
+    "# !autotrain llm --train --project_name my-llm --model TinyLlama/TinyLlama-1.1B-Chat-v0.1 --data_path . --use-peft --use_int4 --learning_rate 2e-4 --train_batch_size 6 --num_train_epochs 3 --trainer sft\n",
+    "command=f\"\"\"\n",
+    "autotrain llm --train \\\n",
+    "    --trainer sft \\\n",
+    "    --project_name {model_params['project_name']} \\\n",
+    "    --model {model_params['model_name']} \\\n",
+    "    --data_path {model_params['data_directory']} \\\n",
+    "    --train_split {model_params['train_data']} \\\n",
+    "    --valid_split {model_params['validation_data']} \\\n",
+    "    --repo_id {model_params['repo_id']} \\\n",
+    "    --push_to_hub \\\n",
+    "    --token HUGGINGFACE_TOKEN \\\n",
+    "    --block_size {model_params['block_size']} \\\n",
+    "    --model_max_length {model_params['model_max_length']} \\\n",
+    "    --logging_steps {model_params['logging_steps']} \\\n",
+    "    --evaluation_strategy {model_params['evaluation_strategy']} \\\n",
+    "    --save_total_limit {model_params['save_total_limit']} \\\n",
+    "    --save_strategy {model_params['save_strategy']} \\\n",
+    "    --fp16 \\\n",
+    "    --lr {model_params['lr']} \\\n",
+    "    --num_train_epochs {model_params['epochs']} \\\n",
+    "    --train_batch_size {model_params['batch_size']} \\\n",
+    "    --warmup_ratio {model_params['warmup_ratio']} \\\n",
+    "    --gradient_accumulation {model_params['gradient_accumulation']} \\\n",
+    "    --optimizer {model_params['optimizer']} \\\n",
+    "    --scheduler linear \\\n",
+    "    --weight_decay {model_params['weight_decay']} \\\n",
+    "    --max_grad_norm {model_params['max_grad_norm']} \\\n",
+    "    --seed {model_params['seed']} \\\n",
+    "    --use_int4 \\\n",
+    "    --use-peft \\\n",
+    "    --lora_r {model_params['lora_r']} \\\n",
+    "    --lora_alpha {model_params['lora_alpha']} \\\n",
+    "    --lora_dropout {model_params['lora_dropout']}\n",
+    "\"\"\"\n",
+    "\n",
+    "# Use subprocess.run() to execute the command\n",
+    "subprocess.run(command, shell=True, check=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

train_llm.py

@@ -4,10 +4,30 @@ import pandas as pd
 
 from datasets import load_dataset
 import subprocess
+from transformers import AutoTokenizer
 
+### Read environment variables
 # from dotenv import load_dotenv,find_dotenv
 # load_dotenv(find_dotenv(),override=True)
 
+### Functions
+def max_token_len(dataset):
+    max_seq_length = 0
+    for row in dataset:
+        tokens = len(tokenizer(row['text'])['input_ids'])
+        if tokens > max_seq_length:
+            max_seq_length = tokens
+    return max_seq_length
+
+### Model details
+# model_name='TinyLlama/TinyLlama-1.1B-Chat-v0.1'
+model_name = 'mistralai/Mistral-7B-v0.1'
+# model_name = 'distilbert-base-uncased'
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model_max_length = tokenizer.model_max_length
+print("Model Max Length:", model_max_length)
+
+### Repo name, dataset initialization, and data directory
 # Load dataset
 dataset_name = 'ai-aerospace/ams_data_train_generic_v0.1_100'
 dataset=load_dataset(dataset_name)
@@ -21,54 +41,99 @@ os.makedirs(data_directory, exist_ok=True)
 # Write the train data to a CSV file
 train_data='train_data'
 train_filename = os.path.join(data_directory, train_data)
-dataset['train'].to_pandas().to_csv(train_filename, columns=['text'], index=False)
+dataset['train'].to_pandas().to_csv(train_filename+'.csv', columns=['text'], index=False)
+max_token_length_train=max_token_len(dataset['train'])
+print('Max token length train: '+str(max_token_length_train))
 
 # Write the validation data to a CSV file
 validation_data='validation_data'
 validation_filename = os.path.join(data_directory, validation_data)
-dataset['validation'].to_pandas().to_csv(validation_filename, columns=['text'], index=False)
+dataset['validation'].to_pandas().to_csv(validation_filename+'.csv', columns=['text'], index=False)
+max_token_length_validation=max_token_len(dataset['validation'])
+print('Max token length validation: '+str(max_token_length_validation))
+      
+max_token_length=max(max_token_length_train,max_token_length_validation)
+if max_token_length > model_max_length:
+    raise ValueError("Maximum token length exceeds model limits.")
+block_size=2*max_token_length
 
 # Define project parameters
 username='ai-aerospace'
 project_name='./llms/'+'ams_data_train-100_'+str(uuid4())
 repo_name='ams-data-train-100-'+str(uuid4())
 
-# model_name='TinyLlama/TinyLlama-1.1B-Chat-v0.1'
-model_name='mistralai/Mistral-7B-v0.1'
-
-# Save parameters to environment variables
-os.environ["project_name"] = project_name
-os.environ["model_name"] = model_name
-os.environ["repo_id"] = username+'/'+repo_name
-os.environ["train_data"] = train_data   
-os.environ["validation_data"] = validation_data
-os.environ["data_directory"] = data_directory
-
-print("project_name:", project_name)
-print("model_name:", model_name)
-print("repo_id:", username+'/'+repo_name)
-print("train_data:", train_data)
-print("validation_data:", validation_data)
-print("data_directory:", data_directory)
+### Set training params
+model_params={
+  "project_name": project_name,
+  "model_name": model_name,
+  "repo_id": username+'/'+repo_name,
+  "train_data": train_data,
+  "validation_data": validation_data,
+  "data_directory": data_directory,
+  "block_size": block_size,
+  "model_max_length": max_token_length,
+  "logging_steps": -1,
+  "evaluation_strategy": "epoch",
+  "save_total_limit": 1,
+  "save_strategy": "epoch",
+  "mixed_precision": "fp16",
+  "lr": 0.00003,
+  "epochs": 3,
+  "batch_size": 2,
+  "warmup_ratio": 0.1,
+  "gradient_accumulation": 1,
+  "optimizer": "adamw_torch",
+  "scheduler": "linear",
+  "weight_decay": 0,
+  "max_grad_norm": 1,
+  "seed": 42,
+  "quantization": "int4",
+  "target_modules": "",
+  "lora_r": 16,
+  "lora_alpha": 32,
+  "lora_dropout": 0.05
+}
+for key, value in model_params.items():
+  os.environ[key] = str(value)
 
+### Feed into and run autotrain command
 # Set .venv and execute the autotrain script
 # To see all parameters: autotrain llm --help
 # !autotrain llm --train --project_name my-llm --model TinyLlama/TinyLlama-1.1B-Chat-v0.1 --data_path . --use-peft --use_int4 --learning_rate 2e-4 --train_batch_size 6 --num_train_epochs 3 --trainer sft
-command="""
+command=f"""
 autotrain llm --train \
-    --project_name ${project_name} \
-    --model ${model_name} \
-    --data_path ${data_directory} \
-    --train_split ${train_data} \
-    --valid_split ${validation_data} \
-    --use-peft \
-    --learning_rate 2e-4 \
-    --train_batch_size 6 \
-    --num_train_epochs 3 \
     --trainer sft \
+    --project_name {model_params['project_name']} \
+    --model {model_params['model_name']} \
+    --data_path {model_params['data_directory']} \
+    --train_split {model_params['train_data']} \
+    --valid_split {model_params['validation_data']} \
+    --repo_id {model_params['repo_id']} \
     --push_to_hub \
-    --repo_id ${repo_id} \
-    --token $HUGGINGFACE_TOKEN
+    --token HUGGINGFACE_TOKEN
+    --block_size {model_params['block_size']} \
+    --model_max_length {model_params['model_max_length']} \
+    --logging_steps {model_params['logging_steps']} \
+    --evaluation_strategy {model_params['evaluation_strategy']} \
+    --save_total_limit {model_params['save_total_limit']} \
+    --save_strategy {model_params['save_strategy']} \
+    --fp16 \
+    --lr {model_params['lr']} \
+    --num_train_epochs {model_params['lr']} \
+    --batch_size {model_params['batch_size']} \
+    --warmup_ratio {model_params['warmup_ratio']} \
+    --gradient_accumulation {model_params['gradient_accumulation']} \
+    --optimizer {model_params['gradient_accumulation']} \
+    --scheduler linear \
+    --weight_decay {model_params['weight_decay']} \
+    --max_grad_norm {model_params['max_grad_norm']} \
+    --seed {model_params['seed']} \
+    --use_int4 \
+    --target_modules {model_params['target_modules']} \
+    --use-peft \
+    --lora_r {model_params['lora_r']} \
+    --lora_alpha {model_params['lora_alpha']} \
+    --lora_dropout {model_params['lora_dropout']}
 """
 
 # Use subprocess.run() to execute the command