{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CV_gQs58bsvM"
   },
   "source": [
    "# Fine-tuning [Llama3-8b](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) Dataset using QRandLora (quantized RandLora) on T4 Free Colab GPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FuXIFTFapAMI",
    "outputId": "b95d8260-65bd-405f-f1e2-8d353aa46814"
   },
   "outputs": [],
   "source": [
    "# Install the libraries\n",
    "!pip install -q -U bitsandbytes\n",
    "!pip install -q -U git+https://github.com/huggingface/transformers.git\n",
    "!pip install -q -U git+https://github.com/huggingface/peft.git\n",
    "!pip install -q -U git+https://github.com/huggingface/accelerate.git\n",
    "!pip install -q datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145,
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      "53f287d4927541d08e2ae7d4d0b3c396",
      "afa442ab223b46cb82569438c0047823"
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    },
    "id": "wAAPv5CRmg7e",
    "outputId": "687f979a-04c1-4160-d71c-4de8ecdb07d9"
   },
   "outputs": [],
   "source": [
    "# Required when training models/data that are gated on HuggingFace, and required for pushing models to HuggingFace\n",
    "from huggingface_hub import notebook_login\n",
    "\n",
    "notebook_login()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MJ-5idQwzvg-"
   },
   "source": [
    "### Loading the model and its tokenizer in quantized setup!\n"
   ]
  },
  {
   "cell_type": "code",
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    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "E0Nl5mWL0k2T",
    "outputId": "a942d9b0-1f38-4a9b-ea20-e55bd7593920"
   },
   "outputs": [],
   "source": [
    "# setting up the config for 4-bit quantization of QRandLora\n",
    "import torch\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
    "\n",
    "model_id = \"meta-llama/Meta-Llama-3-8B\"\n",
    "bnb_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type=\"nf4\", bnb_4bit_compute_dtype=torch.bfloat16\n",
    ")\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
    "model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map={\"\": 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Xpx2Fq-icX56"
   },
   "outputs": [],
   "source": [
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Mp2gMi1ZzGET"
   },
   "source": [
    "#### Prepare model for PEFT fine-tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "a9EUEDAl0ss3"
   },
   "outputs": [],
   "source": [
    "from peft import prepare_model_for_kbit_training\n",
    "\n",
    "model.gradient_checkpointing_enable()\n",
    "model = prepare_model_for_kbit_training(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "gkIcwsSU01EB"
   },
   "outputs": [],
   "source": [
    "def print_trainable_parameters(model):\n",
    "    \"\"\"\n",
    "    Prints the number of trainable parameters in the model.\n",
    "    \"\"\"\n",
    "    trainable_params = 0\n",
    "    all_param = 0\n",
    "    for _, param in model.named_parameters():\n",
    "        all_param += param.numel()\n",
    "        if param.requires_grad:\n",
    "            trainable_params += param.numel()\n",
    "    print(\n",
    "        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HVTAJuKyM0gX"
   },
   "source": [
    "### Setup `RandLoraConfig`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ybeyl20n3dYH",
    "outputId": "ea35ec70-13e4-4f23-9481-1b33b1b06dec"
   },
   "outputs": [],
   "source": [
    "from peft import RandLoraConfig, get_peft_model\n",
    "\n",
    "config = RandLoraConfig(\n",
    "    r=32,\n",
    "    randlora_alpha=640,\n",
    "    target_modules=[\n",
    "        \"q_proj\",\n",
    "        \"k_proj\",\n",
    "        \"v_proj\",\n",
    "        \"o_proj\",\n",
    "        \"gate_proj\",\n",
    "        \"up_proj\",\n",
    "        \"down_proj\",\n",
    "    ],  # parameters specific to llama\n",
    "    randlora_dropout=0.05,\n",
    "    bias=\"none\",\n",
    "    task_type=\"CAUSAL_LM\",\n",
    ")\n",
    "\n",
    "model = get_peft_model(model, config)\n",
    "print_trainable_parameters(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ybeyl20n3dYH",
    "outputId": "ea35ec70-13e4-4f23-9481-1b33b1b06dec"
   },
   "outputs": [],
   "source": [
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FCc64bfnmd3j"
   },
   "source": [
    "## Step 2) Fine-tuning process 💥\n"
   ]
  },
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    },
    "id": "s6f4z8EYmcJ6",
    "outputId": "8ece7a1a-cf27-4602-c70b-ce4f3d7e11bf"
   },
   "outputs": [],
   "source": [
    "# Load the dataset from HF\n",
    "from datasets import load_dataset\n",
    "\n",
    "data = load_dataset(\"timdettmers/openassistant-guanaco\")\n",
    "data = data.map(lambda samples: tokenizer(samples[\"text\"]), batched=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_0MOtwf3zdZp"
   },
   "source": [
    "## Training\n",
    "\n",
    "For the sake of the demo, we just ran it for 10 steps just to showcase how to use this integration with existing tools on the HF ecosystem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 498
    },
    "id": "jq0nX33BmfaC",
    "outputId": "94e17005-065b-48ab-9192-e3ab55c0c292"
   },
   "outputs": [],
   "source": [
    "import transformers\n",
    "\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "\n",
    "trainer = transformers.Trainer(\n",
    "    model=model,\n",
    "    train_dataset=data[\"train\"],\n",
    "    args=transformers.TrainingArguments(\n",
    "        per_device_train_batch_size=1,\n",
    "        gradient_accumulation_steps=4,\n",
    "        warmup_steps=2,\n",
    "        max_steps=10,\n",
    "        learning_rate=2e-4,\n",
    "        fp16=True,\n",
    "        logging_steps=1,\n",
    "        output_dir=\"path/to/your/HF/repo\",  # change it to your desired repo!\n",
    "        optim=\"paged_adamw_8bit\",\n",
    "        label_names=[\"labels\"],\n",
    "    ),\n",
    "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
    ")\n",
    "model.config.use_cache = False  # silence the warnings. Please re-enable for inference!\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Mr3rLrHwqhf6"
   },
   "source": [
    "## Usage Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9mrOJ9l8SMHv"
   },
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   "source": [
    "model.config.use_cache = True\n",
    "model.eval();"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "AM6FNOFzqKfI",
    "outputId": "fdbe28b1-e440-45d3-bd6d-c15e744ad23d"
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   "source": [
    "from transformers import GenerationConfig\n",
    "\n",
    "max_new_tokens = 120\n",
    "top_p = 0.9\n",
    "temperature = 0.7\n",
    "user_question = \"What is the purpose of quantization in LLMs?\"\n",
    "\n",
    "\n",
    "prompt = (\n",
    "    \"A chat between a curious human and an artificial intelligence assistant. \"\n",
    "    \"The assistant gives helpful, detailed, and polite answers to the user's questions. \"\n",
    "    \"### Human: {user_question}\"\n",
    "    \"### Assistant: \"\n",
    ")\n",
    "\n",
    "\n",
    "def generate(model, user_question, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature):\n",
    "    device = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\n",
    "    inputs = tokenizer(prompt.format(user_question=user_question), return_tensors=\"pt\").to(device)\n",
    "\n",
    "    outputs = model.generate(\n",
    "        **inputs,\n",
    "        generation_config=GenerationConfig(\n",
    "            do_sample=True,\n",
    "            max_new_tokens=max_new_tokens,\n",
    "            top_p=top_p,\n",
    "            temperature=temperature,\n",
    "        ),\n",
    "    )\n",
    "\n",
    "    text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
    "    # print(text)\n",
    "    return text\n",
    "\n",
    "\n",
    "generate(model, user_question)"
   ]
  },
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   "execution_count": null,
   "metadata": {
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   "source": [
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