tinyllama_fine-tuning_Taylor_Swift.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "## Fine Tuning TinyLlama\n",
    "\n",
    "## [Checkout the Youtube Video for in-depth explanation of this notebook](https://www.youtube.com/watch?v=3SlpXBvIqNw&list=PLxqBkZuBynVTzqUQCQFgetR97y1X_1uCI&index=37&ab_channel=Rohan-Paul-AI)\n",
    "\n",
    "Data - https://github.com/shaynak/taylor-swift-lyrics/blob/main/songs.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "import torch\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "from peft import get_peft_model, PeftConfig, PeftModel, LoraConfig, prepare_model_for_kbit_training\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, TrainingArguments, GenerationConfig\n",
    "from trl import SFTTrainer\n",
    "from datasets import Dataset\n",
    "import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load all .csv files from a directory which contains the Taylor Swift Lyrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PX26zB2dCR6g",
    "outputId": "061250cf-a572-4195-c3d2-11e60713bb6b"
   },
   "outputs": [],
   "source": [
    "import logging\n",
    "\n",
    "def find_csv_files(path, file_extension=\"*.csv\"):\n",
    "    try:\n",
    "        files = glob.glob(f\"{path}/{file_extension}\")\n",
    "        if not files:\n",
    "            logging.warning(f\"No files found in {path} with extension {file_extension}\")\n",
    "        return files\n",
    "    except Exception as e:\n",
    "        logging.error(f\"Error finding files in {path}: {e}\")\n",
    "        return []\n",
    "\n",
    "def read_csv_files(file_paths, column_name='Lyrics'):\n",
    "    df_list = []\n",
    "    for file in file_paths:\n",
    "        try:\n",
    "            df = pd.read_csv(file)\n",
    "            if column_name in df.columns:\n",
    "                df_list.append(df)\n",
    "            else:\n",
    "                logging.warning(f\"Column {column_name} not found in {file}\")\n",
    "        except Exception as e:\n",
    "            logging.error(f\"Error reading {file}: {e}\")\n",
    "    return pd.concat(df_list, ignore_index=True) if df_list else pd.DataFrame()\n",
    "\n",
    "def concatenate_lyrics(df, column_name='Lyrics'):\n",
    "    if column_name in df.columns:\n",
    "        try:\n",
    "            return '\\n'.join(str(lyric) for lyric in df[column_name])\n",
    "        except Exception as e:\n",
    "            logging.error(f\"Failed to concatenate lyrics: {e}\")\n",
    "            return \"\"\n",
    "    else:\n",
    "        logging.warning(f\"Column {column_name} not found in DataFrame\")\n",
    "        return \"\"\n",
    "\n",
    "def load_and_concatenate_lyrics(path, file_extension=\"*.csv\", column_name='Lyrics'):\n",
    "    files = find_csv_files(path, file_extension)\n",
    "    if not files:\n",
    "        return \"\"\n",
    "    df = read_csv_files(files, column_name)\n",
    "    if df.empty:\n",
    "        return \"\"\n",
    "    return concatenate_lyrics(df, column_name)\n",
    "path = '../data_input/'\n",
    "lyrics = load_and_concatenate_lyrics(path)\n",
    "print(lyrics[:200])"
   ]
  },
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   "outputs": [],
   "source": [
    "print(' '.join(sorted(set(lyrics))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
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   "source": [
    "import re\n",
    "\n",
    "def replace_characters(text, replacement_dict):\n",
    "    return text.translate(str.maketrans(replacement_dict))\n",
    "\n",
    "def remove_patterns(text, pattern_list):\n",
    "    for pattern in pattern_list:\n",
    "        text = re.sub(pattern, '', text)\n",
    "    return text\n",
    "\n",
    "def clean_lyrics(lyrics):\n",
    "    replace_with_space = ['\\u2005', '\\u200b', '\\u205f', '\\xa0', '-']\n",
    "    replace_letters = {'í':'i', 'é':'e', 'ï':'i', 'ó':'o', ';':',', '‘':'\\'', '’':'\\'', ':':',', 'е':'e'}\n",
    "    remove_list = ['\\)', '\\(', '–','\"','”', '\"', '\\[.*\\]', '.*\\|.*', '—']\n",
    "    lyrics = replace_characters(lyrics, replace_letters)\n",
    "    for string in replace_with_space:\n",
    "        lyrics = lyrics.replace(string, ' ')\n",
    "    lyrics = remove_patterns(lyrics, remove_list)\n",
    "    return lyrics\n",
    "cleaned_lyrics = clean_lyrics(lyrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CxQ-jIS-CR6i",
    "outputId": "41d39c18-2c0c-40f9-f280-f4ba87e16b23"
   },
   "outputs": [],
   "source": [
    "print(''.join(sorted(set(cleaned_lyrics))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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     "height": 174
    },
    "id": "K3e69_oiCR6j",
    "outputId": "3f922ad1-dd71-4a32-de5b-5708925b8262"
   },
   "outputs": [],
   "source": [
    "cleaned_lyrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
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    },
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   "outputs": [],
   "source": [
    "from datasets import Dataset\n",
    "\n",
    "def create_train_test_datasets(cleaned_lyrics, train_ratio=0.95, segment_length=500):\n",
    "    split_point = int(len(cleaned_lyrics) * train_ratio)\n",
    "    train_data = cleaned_lyrics[:split_point]\n",
    "    test_data = cleaned_lyrics[split_point:]\n",
    "    train_data_segments = [train_data[i:i + segment_length]\n",
    "                           for i in range(0, len(train_data), segment_length)]\n",
    "    train_dataset = Dataset.from_dict({'text': train_data_segments})\n",
    "    return train_dataset, test_data\n",
    "train_dataset, test_data = create_train_test_datasets(cleaned_lyrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AB8pDCdMCR6k",
    "outputId": "c7e527ef-e37c-4490-ba27-828cdd943422"
   },
   "outputs": [],
   "source": [
    "print(len(train_dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "colab": {
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   "outputs": [],
   "source": [
    "train_dataset\n",
    "\n",
    "# Check the dataset structure\n",
    "# Should output below\n",
    "\"\"\"\n",
    "Dataset({\n",
    "    features: ['text'],\n",
    "    num_rows: 557\n",
    "})\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 504
    },
    "id": "z4TaWEu7CR6l",
    "outputId": "70adf089-fc6b-4f0b-83d7-c40e66bc568f"
   },
   "outputs": [],
   "source": [
    "from pprint import pprint\n",
    "pprint(train_dataset[0])"
   ]
  },
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   "source": [
    "def load_quantized_model(model_identifier: str, compute_dtype: torch.dtype) -> AutoModelForCausalLM:\n",
    "    quantization_config = BitsAndBytesConfig(\n",
    "        load_in_4bit=True,\n",
    "        bnb_4bit_quant_type=\"nf4\",\n",
    "        bnb_4bit_use_double_quant=True,\n",
    "        bnb_4bit_compute_dtype=compute_dtype,\n",
    "    )\n",
    "    model =  AutoModelForCausalLM.from_pretrained(\n",
    "        model_identifier,\n",
    "        quantization_config=quantization_config,\n",
    "        device_map=\"auto\",\n",
    "        trust_remote_code=True,\n",
    "    )\n",
    "    return model\n",
    "model_name = \"TinyLlama/TinyLlama-1.1B-Chat-v1.0\"\n",
    "\n",
    "model = load_quantized_model(model_name, torch.bfloat16)"
   ]
  },
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   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
    "\n",
    "tokenizer.pad_token = tokenizer.eos_token"
   ]
  },
  {
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   "metadata": {
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   "outputs": [],
   "source": [
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "\n",
    "def generate_lyrics(query, model):\n",
    "    encoding = tokenizer(query, return_tensors=\"pt\").to(device)\n",
    "    generation_config = GenerationConfig(max_new_tokens=250, pad_token_id = tokenizer.eos_token_id,repetition_penalty=1.3, eos_token_id = tokenizer.eos_token_id)\n",
    "\n",
    "    outputs = model.generate(input_ids=encoding.input_ids, generation_config=generation_config)\n",
    "\n",
    "    text_output = tokenizer.decode(outputs[0],skip_special_tokens=True)\n",
    "    print('INPUT\\n', query, '\\n\\nOUTPUT\\n', text_output[len(query):])\n",
    "\n",
    "generate_lyrics(test_data[200:700], model)"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "model = prepare_model_for_kbit_training(model)\n",
    "lora_alpha = 32\n",
    "lora_dropout = 0.05\n",
    "lora_rank = 32\n",
    "lora_config = LoraConfig(\n",
    "    lora_alpha=lora_alpha,\n",
    "    lora_dropout=lora_dropout,\n",
    "    r=lora_rank,\n",
    "    bias=\"none\",\n",
    "    task_type=\"CAUSAL_LM\")\n",
    "peft_model = get_peft_model(model, lora_config)"
   ]
  },
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   "outputs": [],
   "source": [
    "output_dir = \"hf-username/tinylama_taylor_swift\"\n",
    "per_device_train_batch_size = 3\n",
    "gradient_accumulation_steps = 2\n",
    "optim = \"paged_adamw_32bit\"\n",
    "save_strategy=\"steps\"\n",
    "save_steps = 10\n",
    "logging_steps = 10\n",
    "learning_rate = 2e-3\n",
    "max_grad_norm = 0.3\n",
    "max_steps = 200\n",
    "warmup_ratio = 0.03\n",
    "lr_scheduler_type = \"cosine\"\n",
    "\n",
    "training_arguments = TrainingArguments(\n",
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    "    per_device_train_batch_size=per_device_train_batch_size,\n",
    "    gradient_accumulation_steps=gradient_accumulation_steps,\n",
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    "    save_steps=save_steps,\n",
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    "    max_seq_length=500,\n",
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