SIA/tools/train/train/qwq_train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unsloth should be imported before transformers to ensure all optimizations are applied.\n",
    "from unsloth import FastLanguageModel, is_bfloat16_supported"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "from pathlib import Path\n",
    "from transformers import TrainingArguments\n",
    "from trl import SFTTrainer, DataCollatorForCompletionOnlyLM\n",
    "from typing import Optional, List\n",
    "import argparse\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from train import qwq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "args = qwq.Args([\"--output-dir\", \"/root/models/notebook\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = qwq.Dataset(args.config_path)\n",
    "dataset.validate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_seq_length = 2048 # Can increase for longer reasoning traces\n",
    "lora_rank = 64 # Larger rank = smarter, but slower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model, tokenizer = FastLanguageModel.from_pretrained(\n",
    "    model_name = args.base_model,\n",
    "    max_seq_length = max_seq_length,\n",
    "    load_in_4bit = True, # False for LoRA 16bit\n",
    "    fast_inference = True, # Enable vLLM fast inference\n",
    "    max_lora_rank = lora_rank,\n",
    "    gpu_memory_utilization = 0.5, # Reduce if out of memory\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = FastLanguageModel.get_peft_model(\n",
    "    model,\n",
    "    r = lora_rank, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n",
    "    target_modules = [\n",
    "        \"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
    "        \"gate_proj\", \"up_proj\", \"down_proj\",\n",
    "    ], # Remove QKVO if out of memory\n",
    "    lora_alpha = lora_rank,\n",
    "    lora_dropout = 0, # Supports any, but = 0 is optimized\n",
    "    bias = \"none\",    # Supports any, but = \"none\" is optimized\n",
    "    use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n",
    "    random_state = 3407,\n",
    "    use_rslora = False,  # We support rank stabilized LoRA\n",
    "    loftq_config = None, # And LoftQ\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_args = TrainingArguments(\n",
    "    output_dir=str(args.output_dir),\n",
    "    num_train_epochs=3,\n",
    "    per_device_train_batch_size=1,\n",
    "    gradient_accumulation_steps=16,\n",
    "    gradient_checkpointing=True,\n",
    "    learning_rate=2e-5,\n",
    "    lr_scheduler_type=\"cosine\",\n",
    "    warmup_ratio=0.05,\n",
    "    weight_decay=0.01,\n",
    "    fp16=not is_bfloat16_supported(),\n",
    "    bf16=is_bfloat16_supported(),\n",
    "    logging_steps=10,\n",
    "    save_steps=200,\n",
    "    save_total_limit=3,\n",
    "    report_to=\"none\",\n",
    "    optim=\"adamw_8bit\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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",
    "    max_seq_length=max_seq_length,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_pretrained_merged(\n",
    "    str(args.output_dir), \n",
    "    tokenizer=tokenizer,\n",
    "    save_method=\"merged_16bit\"\n",
    ")"
   ]
  }
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