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RWKV_v4_RNN_Pile_Fine_Tuning.ipynb
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{
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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": "Vx7KFfeieD7z"
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},
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"source": [
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"# RWKV-v4-RNN-Pile Fine-Tuning\n",
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"\n",
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"[RWKV](https://github.com/BlinkDL/RWKV-LM) is an RNN with transformer-level performance\n",
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"\n",
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"\n",
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"This notebook aims to streamline fine-tuning RWKV-v4 models"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "7JFIiAsrfvJy"
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},
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"source": [
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"\n",
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"## Setup"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "g_qFjgYmtSfK"
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},
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"outputs": [],
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"source": [
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"#@title Google Drive Options { display-mode: \"form\" }\n",
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"save_models_to_drive = True #@param {type:\"boolean\"}\n",
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"drive_mount = '/content/drive' #@param {type:\"string\"}\n",
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"output_dir = 'rwkv-v4-rnn-pile-tuning' #@param {type:\"string\"}\n",
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"tuned_model_name = 'tuned' #@param {type:\"string\"}\n",
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"\n",
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"import os\n",
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"from google.colab import drive\n",
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"if save_models_to_drive:\n",
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" from google.colab import drive\n",
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" drive.mount(drive_mount)\n",
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" \n",
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"output_path = f\"{drive_mount}/MyDrive/{output_dir}\" if save_models_to_drive else f\"/content/{output_dir}\"\n",
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"os.makedirs(f\"{output_path}/{tuned_model_name}\", exist_ok=True)\n",
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"os.makedirs(f\"{output_path}/base_models/\", exist_ok=True)\n",
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"\n",
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"print(f\"Saving models to {output_path}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "eivKJ6FP1_9z",
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"outputId": "a687e3ad-8158-492a-da86-4f4ed8804699",
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"colab": {
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"base_uri": "https://localhost:8080/"
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}
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Fri Sep 2 16:11:37 2022 \n",
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"+-----------------------------------------------------------------------------+\n",
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"| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |\n",
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"|-------------------------------+----------------------+----------------------+\n",
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"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
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"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
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"| | | MIG M. |\n",
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"|===============================+======================+======================|\n",
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"| 0 Tesla P100-PCIE... Off | 00000000:00:04.0 Off | 0 |\n",
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"| N/A 35C P0 28W / 250W | 0MiB / 16280MiB | 0% Default |\n",
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"| | | N/A |\n",
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"+-------------------------------+----------------------+----------------------+\n",
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" \n",
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"+-----------------------------------------------------------------------------+\n",
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"| Processes: |\n",
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"| GPU GI CI PID Type Process name GPU Memory |\n",
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"| ID ID Usage |\n",
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"|=============================================================================|\n",
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"| No running processes found |\n",
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"+-----------------------------------------------------------------------------+\n"
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]
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}
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],
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"source": [
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"!nvidia-smi"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "R4lt0FTegJw9"
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},
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"outputs": [],
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"source": [
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"!git clone https://github.com/blinkdl/RWKV-LM\n",
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"repo_dir = \"/content/RWKV-LM/RWKV-v4\"\n",
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"%cd $repo_dir"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "RDavUrBsgKIV"
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},
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"outputs": [],
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"source": [
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"!pip install transformers pytorch-lightning==1.9 deepspeed wandb ninja"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Wt7y7vR6e6U3"
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},
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"source": [
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"## Load Base Model\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "KIgagN-Se3wi"
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},
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"outputs": [],
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"source": [
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"#@title Base Model Options\n",
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"#@markdown Using any of the listed options will download the checkpoint from huggingface\n",
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"\n",
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"base_model_name = \"RWKV-4-Pile-169M\" #@param [\"RWKV-4-Pile-1B5\", \"RWKV-4-Pile-430M\", \"RWKV-4-Pile-169M\"]\n",
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"base_model_url = f\"https://huggingface.co/BlinkDL/{base_model_name.lower()}\"\n",
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"\n",
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"# This may take a while\n",
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"!git lfs clone $base_model_url\n",
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"\n",
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"from glob import glob\n",
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149 |
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"base_model_path = glob(f\"{base_model_name.lower()}/{base_model_name}*.pth\")[0]\n",
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"\n",
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151 |
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"print(f\"Using {base_model_path} as base\")"
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152 |
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "hCOPnLelfJgP"
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},
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159 |
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"source": [
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160 |
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"## Generate Training Data"
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161 |
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]
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162 |
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "wW5OmlXmvaIU",
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"cellView": "form"
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},
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"outputs": [],
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"source": [
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"#@title Training Data Options\n",
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"#@markdown `input_file` should be the path to a single file that contains the text you want to fine-tune with.\n",
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174 |
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"#@markdown Either upload a file to this notebook instance or reference a file in your Google drive.\n",
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"\n",
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"import numpy as np\n",
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177 |
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"from transformers import PreTrainedTokenizerFast\n",
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"\n",
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"tokenizer = PreTrainedTokenizerFast(tokenizer_file=f'{repo_dir}/20B_tokenizer.json')\n",
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"\n",
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181 |
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"input_file = \"/content/drive/MyDrive/training.txt\" #@param {type:\"string\"}\n",
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182 |
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"output_file = 'train.npy'\n",
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"\n",
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184 |
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"print(f'Tokenizing {input_file} (VERY slow. please wait)')\n",
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"\n",
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"data_raw = open(input_file, encoding=\"utf-8\").read()\n",
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187 |
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"print(f'Raw length = {len(data_raw)}')\n",
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"\n",
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"data_code = tokenizer.encode(data_raw)\n",
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190 |
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"print(f'Tokenized length = {len(data_code)}')\n",
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"\n",
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192 |
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"out = np.array(data_code, dtype='uint16')\n",
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193 |
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"np.save(output_file, out, allow_pickle=False)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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199 |
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"id": "I4lz-3maeIwY"
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},
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"source": [
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202 |
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"## Training"
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203 |
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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209 |
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"id": "fuCw5_ASwMud"
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},
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"outputs": [],
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"source": [
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"#@title Training Options { display-mode: \"form\" }\n",
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"from shutil import copy\n",
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"import os\n",
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"\n",
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"def training_options():\n",
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" EXPRESS_PILE_MODE = True\n",
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" EXPRESS_PILE_MODEL_NAME = base_model_path.split(\".\")[0]\n",
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" EXPRESS_PILE_MODEL_TYPE = base_model_name\n",
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" n_epoch = 100 #@param {type:\"integer\"}\n",
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222 |
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" epoch_save_frequency = 25 #@param {type:\"integer\"}\n",
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223 |
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" batch_size = 11#@param {type:\"integer\"} \n",
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224 |
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" ctx_len = 384 #@param {type:\"integer\"}\n",
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225 |
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" epoch_save_path = f\"{output_path}/{tuned_model_name}\"\n",
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226 |
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" return locals()\n",
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"\n",
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"def model_options():\n",
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" T_MAX = 384 #@param {type:\"integer\"}\n",
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230 |
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" return locals()\n",
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"\n",
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"def env_vars():\n",
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" RWKV_FLOAT_MODE = 'fp16' #@param ['fp16', 'bf16', 'bf32'] {type:\"string\"}\n",
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234 |
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" RWKV_DEEPSPEED = '0' #@param ['0', '1'] {type:\"string\"}\n",
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235 |
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" return {f\"os.environ['{key}']\": value for key, value in locals().items()}\n",
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"\n",
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"def replace_lines(file_name, to_replace):\n",
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" with open(file_name, 'r') as f:\n",
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" lines = f.readlines()\n",
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" with open(f'{file_name}.tmp', 'w') as f:\n",
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" for line in lines:\n",
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" key = line.split(\" =\")[0]\n",
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" if key.strip() in to_replace:\n",
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" value = to_replace[key.strip()]\n",
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" if isinstance(value, str):\n",
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" f.write(f'{key} = \"{value}\"\\n')\n",
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" else:\n",
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248 |
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" f.write(f'{key} = {value}\\n')\n",
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" else:\n",
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" f.write(line)\n",
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" copy(f'{file_name}.tmp', file_name)\n",
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" os.remove(f'{file_name}.tmp')\n",
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"\n",
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"values = training_options()\n",
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"values.update(env_vars())\n",
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"replace_lines('train.py', values)\n",
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"replace_lines('src/model.py', model_options())"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"!python train.py "
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],
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"metadata": {
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266 |
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"id": "0ZSF8U-nzylI"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [],
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"metadata": {
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"id": "pcDci4O7xJiZ"
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},
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"execution_count": null,
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"outputs": []
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}
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],
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"metadata": {
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"accelerator": "GPU",
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"colab": {
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"name": "RWKV-v4-RNN-Pile Fine-Tuning",
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"provenance": [],
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"toc_visible": true
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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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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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