662 lines
21 KiB
Plaintext
662 lines
21 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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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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"🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
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"Unsloth: Failed to patch Gemma3ForConditionalGeneration.\n",
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"🦥 Unsloth Zoo will now patch everything to make training faster!\n",
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"INFO 04-23 16:23:47 [__init__.py:239] Automatically detected platform cuda.\n"
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]
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}
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],
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"source": [
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"# Unsloth should be imported before transformers to ensure all optimizations are applied.\n",
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"from unsloth import FastLanguageModel, is_bfloat16_supported"
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from dataclasses import dataclass\n",
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"from pathlib import Path\n",
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"from transformers import AutoTokenizer, TrainingArguments\n",
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"from trl import SFTTrainer, DataCollatorForCompletionOnlyLM\n",
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"from typing import Optional, List\n",
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"import argparse\n",
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"import json\n",
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"import os"
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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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"from train import qwq"
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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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"args = qwq.Args([\"--output-dir\", \"/root/models/notebook\", \"--base-model\", \"unsloth/QwQ-32B-bnb-4bit\"])"
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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": 5,
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"metadata": {},
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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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"Validating 20 XML files...\n",
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"file: /root/sia/training/clean_start/iteration_20250116_134549_655.xml\n",
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"WARNING: System prompt hash mismatch in /root/sia/training/clean_start/iteration_20250116_134549_655.xml\n",
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"file: /root/sia/training/clean_start/iteration_20250116_134555_680.xml\n",
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"WARNING: System prompt hash mismatch in /root/sia/training/clean_start/iteration_20250116_134555_680.xml\n",
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"file: /root/sia/training/delete_indicated_entries/iteration_20250116_141241_092.xml\n",
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"WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141241_092.xml\n",
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"file: /root/sia/training/delete_indicated_entries/iteration_20250116_141252_317.xml\n",
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"WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141252_317.xml\n",
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"file: /root/sia/training/delete_indicated_entries/iteration_20250116_141302_940.xml\n",
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"WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141302_940.xml\n",
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"file: /root/sia/training/delete_indicated_entries/iteration_20250116_141329_886.xml\n",
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"WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141329_886.xml\n",
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"file: /root/sia/training/delete_indicated_entries/iteration_20250116_141343_416.xml\n",
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"WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141343_416.xml\n",
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"file: /root/sia/training/delete_indicated_entries/iteration_20250116_141357_412.xml\n",
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"WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141357_412.xml\n",
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"file: /root/sia/training/delete_indicated_entries/iteration_20250116_141410_965.xml\n",
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"WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141410_965.xml\n",
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"file: /root/sia/training/delete_indicated_entries/iteration_20250116_141428_204.xml\n",
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"WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141428_204.xml\n",
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"file: /root/sia/training/delete_indicated_entries/iteration_20250116_141441_443.xml\n",
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"WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141441_443.xml\n",
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"file: /root/sia/training/delete_indicated_entries/iteration_20250116_141447_231.xml\n",
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"WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141447_231.xml\n",
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"file: /root/sia/training/delete_indicated_entries/iteration_20250116_141454_509.xml\n",
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"WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141454_509.xml\n",
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"file: /root/sia/training/delete_indicated_entries/iteration_20250116_141458_495.xml\n",
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"WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141458_495.xml\n",
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"file: /root/sia/training/delete_indicated_entries/iteration_20250116_141503_889.xml\n",
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"WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141503_889.xml\n",
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"file: /root/sia/training/delete_indicated_entries/iteration_20250116_141516_718.xml\n",
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"WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141516_718.xml\n",
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"file: /root/sia/training/delete_indicated_entries/iteration_20250116_141533_231.xml\n",
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"WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141533_231.xml\n",
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"file: /root/sia/training/delete_indicated_entries/iteration_20250116_141603_549.xml\n",
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"WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141603_549.xml\n",
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"file: /root/sia/training/delete_indicated_entries/iteration_20250116_141633_083.xml\n",
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"WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141633_083.xml\n",
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"file: /root/sia/training/list_entries_to_delete/iteration_20250116_141227_271.xml\n",
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"WARNING: System prompt hash mismatch in /root/sia/training/list_entries_to_delete/iteration_20250116_141227_271.xml\n",
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"Validation complete. Found 20 valid files.\n"
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]
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}
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],
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"source": [
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"dataset = qwq.Dataset(args.config_path)\n",
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"dataset.validate()"
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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": 6,
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"metadata": {},
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"outputs": [
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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": "b72535c55d214b9da158b90ab0d3e65a",
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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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"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": "5b09cbc50ad94b81a25cf790b5841d75",
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"version_major": 2,
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},
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"text/plain": [
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"metadata": {},
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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": "d561672fb7e34306aada26df664e08cd",
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"version_major": 2,
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"metadata": {},
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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": "b2fa48006e3047689ce3951347ee8d87",
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"version_major": 2,
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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": "e4bb1c113f5b46dc95b90924de24aaaf",
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"version_major": 2,
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"version_minor": 0
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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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"application/vnd.jupyter.widget-view+json": {
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"model_id": "200953e14bad47a38757dd643c7bb176",
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"version_major": 2,
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"version_minor": 0
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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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"source": [
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"with open('/root/sia/qwq_tokenizer_config.json', 'r') as f:\n",
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" tokenizer_config = json.load(f)\n",
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"\n",
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"tokenizer = AutoTokenizer.from_pretrained(\n",
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" args.base_model,\n",
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" **tokenizer_config,\n",
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")"
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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": 7,
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"metadata": {},
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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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"==((====))== Unsloth 2025.3.19: Fast Qwen2 patching. Transformers: 4.51.3. vLLM: 0.8.2.\n",
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" \\\\ /| NVIDIA RTX 6000 Ada Generation. Num GPUs = 1. Max memory: 47.5 GB. Platform: Linux.\n",
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"O^O/ \\_/ \\ Torch: 2.6.0+cu124. CUDA: 8.9. CUDA Toolkit: 12.4. Triton: 3.2.0\n",
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"\\ / Bfloat16 = TRUE. FA [Xformers = 0.0.29.post2. FA2 = False]\n",
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" \"-____-\" Free license: http://github.com/unslothai/unsloth\n",
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"Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
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]
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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": "78af66cb4ea84a4e8dea6cb5db0a7ca2",
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"version_major": 2,
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"version_minor": 0
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},
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]
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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": "8d4904d5f68941369fef4e0b67f21397",
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"version_major": 2,
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"model_id": "728274cc4ab84d64ab152b22a842e1bc",
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"model_id": "4273150dc40846008cc36d19b87e595e",
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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": "0d4ad6c3535741a2aeb454c98d04a395",
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"version_major": 2,
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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": [
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"Sliding Window Attention is enabled but not implemented for `eager`; unexpected results may be encountered.\n"
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]
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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": "73f1c658797c4b73937e26c2012e2019",
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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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"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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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "7dea89c178f34e2185d7ebbac5b9f84e",
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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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"source": [
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"model, _returned_tokenizer = FastLanguageModel.from_pretrained(\n",
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" model_name = args.base_model,\n",
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" gpu_memory_utilization = 0.5, # Reduce if out of memory\n",
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")"
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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": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Unsloth 2025.3.19 patched 64 layers with 64 QKV layers, 64 O layers and 64 MLP layers.\n"
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]
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}
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],
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"source": [
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"model = FastLanguageModel.get_peft_model(\n",
|
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" model,\n",
|
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" target_modules = [\n",
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" \"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
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" \"gate_proj\", \"up_proj\", \"down_proj\",\n",
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" ],\n",
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" lora_dropout = 0, # Supports any, but = 0 is optimized\n",
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" bias = \"none\", # Supports any, but = \"none\" is optimized\n",
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" use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n",
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" random_state = 3407,\n",
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")"
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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": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"training_args = TrainingArguments(\n",
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" output_dir=str(args.output_dir) + \"_train\",\n",
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" num_train_epochs=3,\n",
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" per_device_train_batch_size=1,\n",
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" gradient_accumulation_steps=16,\n",
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" gradient_checkpointing=True,\n",
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" learning_rate=2e-5,\n",
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" lr_scheduler_type=\"cosine\",\n",
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" warmup_ratio=0.05,\n",
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" weight_decay=0.01,\n",
|
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" fp16=not is_bfloat16_supported(),\n",
|
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" bf16=is_bfloat16_supported(),\n",
|
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" logging_steps=10,\n",
|
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" save_steps=200,\n",
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" save_total_limit=3,\n",
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" report_to=\"none\",\n",
|
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" optim=\"adamw_8bit\",\n",
|
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")"
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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": 10,
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"metadata": {},
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"outputs": [
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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": "0bf6b9b1d6de48688ff82a8077790f7a",
|
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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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"Generating train split: 0 examples [00:00, ? examples/s]"
|
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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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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
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"num_proc must be <= 20. Reducing num_proc to 20 for dataset of size 20.\n"
|
|
]
|
|
},
|
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{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
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"model_id": "74e69db167224b35b381b91827ac0c30",
|
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"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
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"Unsloth: Tokenizing [\"messages\"] (num_proc=20): 0%| | 0/20 [00:00<?, ? examples/s]"
|
|
]
|
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},
|
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"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"trainer = SFTTrainer(\n",
|
|
" model=model,\n",
|
|
" tokenizer=tokenizer,\n",
|
|
" args=training_args,\n",
|
|
" train_dataset=dataset.to_transformers_dataset(tokenizer),\n",
|
|
" dataset_text_field=\"messages\",\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1\n",
|
|
" \\\\ /| Num examples = 20 | Num Epochs = 3 | Total steps = 3\n",
|
|
"O^O/ \\_/ \\ Batch size per device = 1 | Gradient accumulation steps = 16\n",
|
|
"\\ / Data Parallel GPUs = 1 | Total batch size (1 x 16 x 1) = 16\n",
|
|
" \"-____-\" Trainable parameters = 134,217,728/32,000,000,000 (0.42% trained)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Unsloth: Will smartly offload gradients to save VRAM!\n"
|
|
]
|
|
},
|
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{
|
|
"data": {
|
|
"text/html": [
|
|
"\n",
|
|
" <div>\n",
|
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" \n",
|
|
" <progress value='3' max='3' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
|
|
" [3/3 01:48, Epoch 1/3]\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",
|
|
" </tbody>\n",
|
|
"</table><p>"
|
|
],
|
|
"text/plain": [
|
|
"<IPython.core.display.HTML object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"TrainOutput(global_step=3, training_loss=1.7701025009155273, metrics={'train_runtime': 187.628, 'train_samples_per_second': 0.32, 'train_steps_per_second': 0.016, 'total_flos': 1.41437049721344e+16, 'train_loss': 1.7701025009155273})"
|
|
]
|
|
},
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"trainer.train()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Unsloth: Merging 4bit and LoRA weights to 4bit...\n",
|
|
"This might take 5 minutes...\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/root/venvs/train/lib/python3.10/site-packages/peft/tuners/lora/bnb.py:351: UserWarning: Merge lora module to 4-bit linear may get different generations due to rounding errors.\n",
|
|
" warnings.warn(\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Done.\n",
|
|
"Unsloth: Saving tokenizer... Done.\n",
|
|
"Unsloth: Saving model... This might take 10 minutes for Llama-7b... Done.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"model.save_pretrained_merged(\n",
|
|
" str(args.output_dir) + \"_merged_4bit\", \n",
|
|
" tokenizer=tokenizer,\n",
|
|
" save_method=\"merged_4bit_forced\"\n",
|
|
")"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "train",
|
|
"language": "python",
|
|
"name": "train"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.12"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|