{
"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,
"referenced_widgets": [
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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",
"execution_count": null,
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"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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 277,
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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"
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"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()"
]
},
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"metadata": {
"id": "Mr3rLrHwqhf6"
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"source": [
"## Usage Example"
]
},
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"metadata": {
"id": "9mrOJ9l8SMHv"
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]
},
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"metadata": {
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"height": 122
},
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"from transformers import GenerationConfig\n",
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"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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