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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PromptWizard Qwen Fine-tuning on HuggingFace\n",
"\n",
"This notebook fine-tunes Qwen models using GSM8K dataset with PromptWizard methodology.\n",
"Run this on HuggingFace or Google Colab with GPU support."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Install required packages\n",
"!pip install -q transformers==4.36.2 datasets==2.16.1 peft==0.7.1 accelerate==0.25.0 bitsandbytes==0.41.3"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import json\n",
"from datasets import Dataset, load_dataset\n",
"from transformers import (\n",
" AutoModelForCausalLM,\n",
" AutoTokenizer,\n",
" TrainingArguments,\n",
" Trainer,\n",
" DataCollatorForLanguageModeling\n",
")\n",
"from peft import LoraConfig, get_peft_model, TaskType\n",
"\n",
"# Check GPU availability\n",
"if torch.cuda.is_available():\n",
" print(f\"✅ GPU Available: {torch.cuda.get_device_name(0)}\")\n",
" print(f\" Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB\")\n",
"else:\n",
" print(\"⚠️ No GPU detected. Training will be slow.\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load and prepare GSM8K dataset\n",
"print(\"Loading GSM8K dataset...\")\n",
"dataset = load_dataset(\"openai/gsm8k\", \"main\")\n",
"\n",
"def format_prompt(item):\n",
" \"\"\"Format GSM8K item for training\"\"\"\n",
" prompt = f\"\"\"<|system|>\n",
"You are a mathematics expert. Solve grade school math problems step by step.\n",
"<|user|>\n",
"{item['question']}\n",
"<|assistant|>\n",
"Let me solve this step by step.\n",
"\n",
"{item['answer']}\"\"\"\n",
" return {\"text\": prompt}\n",
"\n",
"# Process datasets (using smaller subset for demo)\n",
"train_data = dataset['train'].select(range(min(1000, len(dataset['train']))))\n",
"eval_data = dataset['test'].select(range(min(100, len(dataset['test']))))\n",
"\n",
"train_dataset = train_data.map(format_prompt)\n",
"eval_dataset = eval_data.map(format_prompt)\n",
"\n",
"print(f\"Train samples: {len(train_dataset)}\")\n",
"print(f\"Eval samples: {len(eval_dataset)}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load model and tokenizer\n",
"MODEL_NAME = \"Qwen/Qwen2.5-1.5B\" # Using smaller model for faster training\n",
"\n",
"print(f\"Loading {MODEL_NAME}...\")\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(\n",
" MODEL_NAME,\n",
" trust_remote_code=True,\n",
" padding_side=\"left\"\n",
")\n",
"\n",
"if tokenizer.pad_token is None:\n",
" tokenizer.pad_token = tokenizer.eos_token\n",
"\n",
"model = AutoModelForCausalLM.from_pretrained(\n",
" MODEL_NAME,\n",
" trust_remote_code=True,\n",
" torch_dtype=torch.float16,\n",
" device_map=\"auto\",\n",
" load_in_8bit=True\n",
")\n",
"\n",
"# Configure LoRA\n",
"lora_config = LoraConfig(\n",
" task_type=TaskType.CAUSAL_LM,\n",
" r=8, # Lower rank for faster training\n",
" lora_alpha=16,\n",
" lora_dropout=0.1,\n",
" target_modules=[\"q_proj\", \"v_proj\"],\n",
" bias=\"none\"\n",
")\n",
"\n",
"model = get_peft_model(model, lora_config)\n",
"model.print_trainable_parameters()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Tokenize datasets\n",
"def tokenize_function(examples):\n",
" return tokenizer(\n",
" examples[\"text\"],\n",
" padding=\"max_length\",\n",
" truncation=True,\n",
" max_length=512\n",
" )\n",
"\n",
"print(\"Tokenizing datasets...\")\n",
"train_dataset = train_dataset.map(tokenize_function, batched=True)\n",
"eval_dataset = eval_dataset.map(tokenize_function, batched=True)\n",
"\n",
"# Data collator\n",
"data_collator = DataCollatorForLanguageModeling(\n",
" tokenizer=tokenizer,\n",
" mlm=False,\n",
" pad_to_multiple_of=8\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Training arguments\n",
"training_args = TrainingArguments(\n",
" output_dir=\"./qwen-promptwizard\",\n",
" num_train_epochs=1, # Quick training for demo\n",
" per_device_train_batch_size=4,\n",
" per_device_eval_batch_size=4,\n",
" gradient_accumulation_steps=4,\n",
" warmup_steps=100,\n",
" logging_steps=10,\n",
" save_steps=100,\n",
" evaluation_strategy=\"steps\",\n",
" eval_steps=50,\n",
" save_total_limit=2,\n",
" load_best_model_at_end=True,\n",
" fp16=True,\n",
" push_to_hub=False, # Set to True to push to HF Hub\n",
" gradient_checkpointing=True,\n",
" optim=\"adamw_torch\",\n",
" learning_rate=2e-4,\n",
" lr_scheduler_type=\"cosine\",\n",
")\n",
"\n",
"# Initialize trainer\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=train_dataset,\n",
" eval_dataset=eval_dataset,\n",
" data_collator=data_collator,\n",
" tokenizer=tokenizer,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Start training\n",
"print(\"Starting training...\")\n",
"print(f\"Using {torch.cuda.device_count()} GPU(s)\")\n",
"\n",
"trainer.train()\n",
"\n",
"print(\"\\n✅ Training complete!\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Save model\n",
"print(\"Saving model...\")\n",
"trainer.save_model(\"./qwen-promptwizard-final\")\n",
"tokenizer.save_pretrained(\"./qwen-promptwizard-final\")\n",
"\n",
"print(\"Model saved to ./qwen-promptwizard-final\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Test the fine-tuned model\n",
"from transformers import pipeline\n",
"\n",
"# Load the fine-tuned model\n",
"generator = pipeline(\n",
" \"text-generation\",\n",
" model=\"./qwen-promptwizard-final\",\n",
" tokenizer=tokenizer,\n",
" device_map=\"auto\"\n",
")\n",
"\n",
"# Test prompt\n",
"test_prompt = \"\"\"<|system|>\n",
"You are a mathematics expert. Solve grade school math problems step by step.\n",
"<|user|>\n",
"Janet has 3 apples. She buys 5 more apples from the store. How many apples does she have now?\n",
"<|assistant|>\"\"\"\n",
"\n",
"# Generate response\n",
"response = generator(\n",
" test_prompt,\n",
" max_new_tokens=200,\n",
" temperature=0.7,\n",
" do_sample=True\n",
")\n",
"\n",
"print(\"Test Response:\")\n",
"print(response[0]['generated_text'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Next Steps\n",
"\n",
"1. **Push to HuggingFace Hub**: Set `push_to_hub=True` in training arguments\n",
"2. **Increase Training**: Use more epochs and larger dataset for better results\n",
"3. **Use Larger Model**: Try Qwen2.5-7B for better performance (needs more GPU memory)\n",
"4. **Fine-tune Hyperparameters**: Adjust learning rate, LoRA rank, etc.\n",
"\n",
"The trained model can now be used with PromptWizard for enhanced prompt optimization!"
]
}
],
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