week-4/supervised_finetuning.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Fine-tuning a Large Language Model\n",
    "\n",
    "In this lecture we will be looking at how to fine-tune an existing pre-trained language model.\n",
    "\n",
    "## Learning outcomes\n",
    "* You will learn how to download a pre-trained model and a training dataset from Hugging Face.\n",
    "* You will learn how to fine-tune the downloaded model with the dataset using Hugging Face trl library and the supervised fine-tuning (SFT) method.\n",
    "* You will learn how to use the fine-tuned model to generate text based on user input / prompts.\n",
    "* You will learn how to upload the fine-tuned model to your own Hugging Face repository so that it can be used later or shared with other users.\n",
    "\n",
    "## Prerequistes\n",
    "* You will need the following free accounts: Google, Hugging Face and Weights & Biases. You may use your existing accounts or create new accounts for the purposes of this course.\n",
    "* We will use the [Hugging Face](https://huggingface.co/) libraries: transformers (for models), datasets (for datasets), trl (for training). We will also store the fine-tuned models in a Hugging Face repository.\n",
    "* Training is done using [Google Colab](https://colab.research.google.com/), which provides free access to Jupyter notebooks backed with a GPU compute required for fine-tuning.\n",
    "* For monitoring the training run we will use [Weights & Biases](https://wandb.ai/)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine-tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first install some pre-requisites using Python's package manager pip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lioAFETZWlKi",
    "outputId": "32dc1efe-d08c-435c-bd13-eb5cb362cf30"
   },
   "outputs": [],
   "source": [
    "!pip install transformers peft accelerate\n",
    "!pip install -U datasets\n",
    "!pip install -q trl xformers wandb einops sentencepiece bitsandbytes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we need to import the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "NdjM7XroWqEx"
   },
   "outputs": [],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TrainingArguments, TextStreamer\n",
    "from peft import LoraConfig, PeftModel, prepare_model_for_kbit_training\n",
    "import torch, wandb\n",
    "from datasets import load_dataset\n",
    "from trl import SFTTrainer\n",
    "from huggingface_hub import notebook_login"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will download a pre-trained large language model from Hugging Face and a dataset to train the model with. Below we assign these to variables we will use later. We will also set the name of the repository and model for the fine-tuned model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jTq15hgUW8qB"
   },
   "outputs": [],
   "source": [
    "# Pre trained model\n",
    "model_name = \"mistralai/Mistral-7B-v0.3\"\n",
    "\n",
    "# Dataset name\n",
    "dataset_name = \"vicgalle/alpaca-gpt4\"\n",
    "\n",
    "# Hugging face repository link to save fine-tuned model(Create new repository in huggingface,copy and paste here)\n",
    "new_model = \"YOUR-USER-NAME/mistral-7b-hf-finetune\" "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To access your Hugging Face account, you need to log in. First go to your Hugging Face account, click *Settings* and select *Access Tokens*. Create a new token and copy the token. Then execute the below login command and when asked paste an access token.  "
   ]
  },
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   "source": [
    "notebook_login()"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Let's then download a subset of the dataset we want to use. Below we limit the dataset to the first 10,000 examples in order to save time. In real life you would probably use the full dataset."
   ]
  },
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   "source": [
    "dataset = load_dataset(dataset_name, split=\"train[0:100]\")\n",
    "dataset[\"text\"][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's then download the model. We first create a config object for quantization of the model using bitsandbytes. Bitsandbytes enables accessible large language models via k-bit quantization for PyTorch.\n",
    "\n",
    "We also need to download the tokenizer."
   ]
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    "id": "ecbQfx6mYpSU",
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   "outputs": [],
   "source": [
    "bnb_config = BitsAndBytesConfig(\n",
    "    load_in_4bit= True,\n",
    "    bnb_4bit_quant_type= \"nf4\",\n",
    "    bnb_4bit_compute_dtype= torch.float16,\n",
    "    bnb_4bit_use_double_quant= False,\n",
    ")\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    model_name,\n",
    "    quantization_config=bnb_config,\n",
    "    device_map={\"\": 0}\n",
    ")\n",
    "model = prepare_model_for_kbit_training(model)\n",
    "model.config.use_cache = False # silence the warnings. Please re-enable for inference!\n",
    "model.config.pretraining_tp = 1\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "tokenizer.add_eos_token = True\n",
    "tokenizer.add_bos_token, tokenizer.add_eos_token"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we set the access token to Waights & Biases. You should copy your access token from your account at [https://wandb.ai](https://wandb.ai)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 208
    },
    "id": "ow0n2oSOYz6V",
    "outputId": "737f4b26-2f94-42b3-d623-2f0961130547"
   },
   "outputs": [],
   "source": [
    "#monitering login\n",
    "wandb.login(key=\"\") # Add your WANDB key here\n",
    "run = wandb.init(project='Fine tuning mistral 7B', job_type=\"training\", anonymous=\"allow\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we'll create a configuration for the lo-rank adaptation method we will use. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "D409OdMjcc-n"
   },
   "outputs": [],
   "source": [
    "peft_config = LoraConfig(\n",
    "    lora_alpha=8,\n",
    "    lora_dropout=0.1,\n",
    "    r=16,\n",
    "    bias=\"none\",\n",
    "    task_type=\"CAUSAL_LM\",\n",
    "    target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to set the training arguments for the training run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "n9sAp50mc4LR"
   },
   "outputs": [],
   "source": [
    "training_arguments = TrainingArguments(\n",
    "    output_dir=\"./results\",\n",
    "    num_train_epochs=1,\n",
    "    per_device_train_batch_size=8,\n",
    "    gradient_accumulation_steps=2,\n",
    "    optim=\"paged_adamw_8bit\",\n",
    "    save_steps=1000,\n",
    "    logging_steps=30,\n",
    "    learning_rate=2e-4,\n",
    "    weight_decay=0.001,\n",
    "    fp16=False,\n",
    "    bf16=False,\n",
    "    max_grad_norm=0.3,\n",
    "    max_steps=-1,\n",
    "    warmup_ratio=0.3,\n",
    "    group_by_length=True,\n",
    "    lr_scheduler_type=\"linear\",\n",
    "    report_to=\"wandb\",\n",
    ")"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we create the trainer object that uses supervised fine-tuning (SFT) as the training method."
   ]
  },
  {
   "cell_type": "code",
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    "colab": {
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    "# Setting sft parameters\n",
    "trainer = SFTTrainer(\n",
    "    model=model,\n",
    "    train_dataset=dataset,\n",
    "    peft_config=peft_config,\n",
    "    max_seq_length= None,\n",
    "    dataset_text_field=\"text\",\n",
    "    tokenizer=tokenizer,\n",
    "    args=training_arguments,\n",
    "    packing=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we can execute the training run. This will approximately 8 hours using the T4 GPU available in Colab and the dataset of 10,000 samples we downloaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 214
    },
    "id": "pEiMWxk9dFLk",
    "outputId": "842c435d-8eee-41a6-edba-c6c88ba9617e"
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   "outputs": [],
   "source": [
    "# Train model\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "UZg7GbvHdOUc"
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   "outputs": [],
   "source": [
    "# Save the fine-tuned model\n",
    "trainer.model.save_pretrained(new_model)\n",
    "wandb.finish()\n",
    "model.config.use_cache = True\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stream(user_prompt):\n",
    "    runtimeFlag = \"cuda:0\"\n",
    "    system_prompt = 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n\\n'\n",
    "    B_INST, E_INST = \"### Instruction:\\n\", \"### Response:\\n\"\n",
    "    prompt = f\"{system_prompt}{B_INST}{user_prompt.strip()}\\n\\n{E_INST}\"\n",
    "    inputs = tokenizer([prompt], return_tensors=\"pt\").to(runtimeFlag)\n",
    "    streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n",
    "    _ = model.generate(**inputs, streamer=streamer, max_new_tokens=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stream(\"what is newtons 3rd law and its formula\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This will fail due to cuda out of memeory issue. Need to add quantization\n",
    "base_model = AutoModelForCausalLM.from_pretrained(\n",
    "    model_name, \n",
    "    low_cpu_mem_usage=True,\n",
    "    return_dict=True,\n",
    "    torch_dtype=torch.float16,\n",
    "    device_map= {\"\": 0})\n",
    "model = PeftModel.from_pretrained(base_model, new_model)\n",
    "model = model.merge_and_unload()\n",
    "\n",
    "# Reload tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "tokenizer.padding_side = \"right\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.push_to_hub(new_model)\n",
    "tokenizer.push_to_hub(new_model)"
   ]
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