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8546 lines
259 KiB
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8546 lines
259 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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||
"metadata": {
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"id": "CV_gQs58bsvM"
|
||
},
|
||
"source": [
|
||
"# Fine-tuning [Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) Dataset using QDora (quantized Lora w/ use_dora=True)."
|
||
]
|
||
},
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||
{
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||
"cell_type": "code",
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"execution_count": 2,
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||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
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||
},
|
||
"id": "FuXIFTFapAMI",
|
||
"outputId": "b95d8260-65bd-405f-f1e2-8d353aa46814"
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||
},
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||
"outputs": [
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||
{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.8/119.8 MB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m37.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
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||
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
|
||
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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" Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
|
||
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
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" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m309.4/309.4 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25h Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
|
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" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
|
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" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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" Building wheel for accelerate (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m547.8/547.8 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.8/40.8 MB\u001b[0m \u001b[31m28.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 kB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m27.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m20.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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||
"cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 16.1.0 which is incompatible.\n",
|
||
"google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
|
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"ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 16.1.0 which is incompatible.\u001b[0m\u001b[31m\n",
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"\u001b[0m"
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||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Install the libraries\n",
|
||
"!pip install -q -U bitsandbytes\n",
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"!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"
|
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]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"metadata": {
|
||
"colab": {
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||
"base_uri": "https://localhost:8080/",
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"height": 145,
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"referenced_widgets": [
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]
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},
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||
"id": "wAAPv5CRmg7e",
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||
"outputId": "687f979a-04c1-4160-d71c-4de8ecdb07d9"
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||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
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||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "8cc86330c2af436c9af314e8c04c8c2b",
|
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"version_major": 2,
|
||
"version_minor": 0
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||
},
|
||
"text/plain": [
|
||
"VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"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 it's tokenizer in quantized setup!\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {
|
||
"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 546,
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"referenced_widgets": [
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|
||
"id": "E0Nl5mWL0k2T",
|
||
"outputId": "a942d9b0-1f38-4a9b-ea20-e55bd7593920"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
|
||
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
|
||
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
|
||
"You will be able to reuse this secret in all of your notebooks.\n",
|
||
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "5924b266e95a42039634a334ff561a82",
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"version_major": 2,
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{
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"data": {
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "43d12a98d90a4bf7a96c033172c646e2",
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"version_major": 2,
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},
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"metadata": {},
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},
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{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
||
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
|
||
]
|
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},
|
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{
|
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "8800c351b6da450eace0c3890d36c8d7",
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"version_major": 2,
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{
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"data": {
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"model_id": "0aad2d9d1cba40cbb64308ede3242ed7",
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"version_major": 2,
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{
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"data": {
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"model_id": "a34b3fd5859a441f89cbe7f6e6df9da9",
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{
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"data": {
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"model_id": "cf6d1be81b6c4ffc81ce8fdabfc5ad28",
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"data": {
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||
{
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"data": {
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|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
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||
"model_id": "57f87d4780634d36ae8159d987c22993",
|
||
"version_major": 2,
|
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},
|
||
"text/plain": [
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|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "5cf4a57d21a545029b6448258a5ebd84",
|
||
"version_major": 2,
|
||
"version_minor": 0
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},
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|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# setting up the config for 4-bit quantization of Qlora\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": 5,
|
||
"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": 6,
|
||
"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 `LoraConfig`\n",
|
||
"### To use Dora we set the `use_dora=True`"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "Ybeyl20n3dYH",
|
||
"outputId": "ea35ec70-13e4-4f23-9481-1b33b1b06dec"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"trainable params: 22347776 || all params: 4562948096 || trainable%: 0.48976616717579247\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from peft import LoraConfig, get_peft_model\n",
|
||
"\n",
|
||
"config = LoraConfig(\n",
|
||
" use_dora=True,\n",
|
||
" r=8,\n",
|
||
" lora_alpha=32,\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",
|
||
" lora_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": "markdown",
|
||
"metadata": {
|
||
"id": "FCc64bfnmd3j"
|
||
},
|
||
"source": [
|
||
"## Step 2) Fine-tuning process 💥\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 277,
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{
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"name": "stderr",
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"text": [
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"Repo card metadata block was not found. Setting CardData to empty.\n",
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"WARNING:huggingface_hub.repocard:Repo card metadata block was not found. Setting CardData to empty.\n"
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{
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"data": {
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"version_minor": 0
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|
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]
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},
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"metadata": {},
|
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"output_type": "display_data"
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},
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{
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"data": {
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"metadata": {},
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"output_type": "display_data"
|
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "9cfaf17064bc49a5aded0fc53dd7cd7f",
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"version_major": 2,
|
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"version_minor": 0
|
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},
|
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"text/plain": [
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|
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]
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},
|
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"metadata": {},
|
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"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
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"model_id": "4a7c8dfd88db4bc893da2bced0560d47",
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"version_major": 2,
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"version_minor": 0
|
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},
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"text/plain": [
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|
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]
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},
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"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"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": 10,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 498
|
||
},
|
||
"id": "jq0nX33BmfaC",
|
||
"outputId": "94e17005-065b-48ab-9192-e3ab55c0c292"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"max_steps is given, it will override any value given in num_train_epochs\n",
|
||
"/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
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"text/html": [
|
||
"\n",
|
||
" <div>\n",
|
||
" \n",
|
||
" <progress value='10' max='10' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
|
||
" [10/10 03:56, Epoch 0/1]\n",
|
||
" </div>\n",
|
||
" <table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: left;\">\n",
|
||
" <th>Step</th>\n",
|
||
" <th>Training Loss</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1.276300</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>2</td>\n",
|
||
" <td>1.877700</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>3</td>\n",
|
||
" <td>1.983800</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>4</td>\n",
|
||
" <td>2.011400</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>5</td>\n",
|
||
" <td>1.997800</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>6</td>\n",
|
||
" <td>1.648100</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>7</td>\n",
|
||
" <td>1.576000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>8</td>\n",
|
||
" <td>0.916400</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>9</td>\n",
|
||
" <td>1.523100</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>10</td>\n",
|
||
" <td>1.814500</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table><p>"
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.HTML object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"TrainOutput(global_step=10, training_loss=1.662518608570099, metrics={'train_runtime': 269.3407, 'train_samples_per_second': 0.149, 'train_steps_per_second': 0.037, 'total_flos': 530537216679936.0, 'train_loss': 1.662518608570099, 'epoch': 0.004062563477554337})"
|
||
]
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"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",
|
||
" ),\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": 11,
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"id": "9mrOJ9l8SMHv"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"model.config.use_cache = True\n",
|
||
"model.eval();"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 122
|
||
},
|
||
"id": "AM6FNOFzqKfI",
|
||
"outputId": "fdbe28b1-e440-45d3-bd6d-c15e744ad23d"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.google.colaboratory.intrinsic+json": {
|
||
"type": "string"
|
||
},
|
||
"text/plain": [
|
||
"\"A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. ### Human: What is the purpose of quantization in LLMs?### Assistant: Quantization is a technique used to reduce the size of a model without significantly impacting its performance. In the context of language models, quantization is the process of converting floating-point numbers (which are used to represent the weights and activations of a model) to smaller, fixed-point numbers. This can be done by grouping the weights into small chunks and assigning each chunk a single, fixed-point number. Quantization can significantly reduce the size of a model, making it more efficient to train and deploy. In addition, quantization can improve the performance of a model on low-power devices, such as mobile phones\""
|
||
]
|
||
},
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"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",
|
||
"device = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\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",
|
||
" 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)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "T5t_gl2_f5OO"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# trainer.push_to_hub()"
|
||
]
|
||
}
|
||
],
|
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"metadata": {
|
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"accelerator": "GPU",
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"colab": {
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"gpuType": "T4",
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"provenance": []
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},
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"gpuClass": "standard",
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"kernelspec": {
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"display_name": "Python 3",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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