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"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 🕷️ WebScrapeAgent — Fine-tune Qwen2.5-7B for Autonomous Web Scraping\n",
"\n",
"This notebook fine-tunes **Qwen2.5-7B-Instruct** with **Unsloth + QLoRA** to create an autonomous web scraping agent that:\n",
"\n",
"1. **Reads HTML** and understands page structure (tables, lists, forms, nested elements)\n",
"2. **Decides action sequences** to extract data (navigate, click, scroll, wait)\n",
"3. **Handles authentication** (cookie replay, form login, token injection, browser profiles)\n",
"4. **Recovers from failures** (403→headless browser, timeout→JS execution, rate limit→backoff)\n",
"\n",
"**Training recipe based on:**\n",
"- ScrapeGraphAI-100k (arXiv:2602.15189): QLoRA r=16, lr=1e-4, completion-only loss → Key F1=0.887\n",
"- BrowserAgent (arXiv:2510.10666): Qwen2.5-7B SFT → +20% over baselines\n",
"- A3-Annotators (arXiv:2604.07776): assistant-token-only loss → 41.5% on WebArena\n",
"\n",
"**Free GPU**: Works on Colab T4 (16GB), Kaggle P100/T4, or any 16GB+ GPU.\n",
"\n",
"**Training data**: 45K examples from [sukritvemula/webscrape-agent-training-data](https://huggingface.co/datasets/sukritvemula/webscrape-agent-training-data)\n",
"- 55% real-world HTML→JSON extraction (ScrapeGraphAI-100k)\n",
"- 44% multi-turn browser interaction sessions (BrowserAgent)\n",
"- 1% synthetic auth handling, error recovery, and diverse HTML structures"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Install Dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%capture\n",
"!pip install unsloth\n",
"!pip install --no-deps trl peft accelerate bitsandbytes xformers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Configuration\n",
"\n",
"Adjust these based on your GPU. Defaults are tuned for **free Colab T4 (16GB VRAM)**."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# === EDIT THESE ===\n",
"HF_USERNAME = \"sukritvemula\" # Your HuggingFace username\n",
"OUTPUT_MODEL = f\"{HF_USERNAME}/WebScrapeAgent-7B-v1\" # Where to push the trained model\n",
"\n",
"# === Training hyperparameters (from ScrapeGraphAI + BrowserAgent papers) ===\n",
"MAX_SEQ_LENGTH = 4096 # Covers 90%+ of examples; increase to 8192 if you have more VRAM\n",
"LORA_R = 32 # LoRA rank (higher = more capacity for structured output)\n",
"LORA_ALPHA = 32 # alpha = r (standard)\n",
"LEARNING_RATE = 1e-4 # QLoRA needs ~10x higher LR than full fine-tuning\n",
"NUM_EPOCHS = 2 # Both reference papers use 2 epochs\n",
"BATCH_SIZE = 1 # Per-device (T4-safe)\n",
"GRAD_ACCUM = 16 # Effective batch = 16\n",
"\n",
"# === Model ===\n",
"MODEL_NAME = \"unsloth/Qwen2.5-7B-Instruct-bnb-4bit\" # Pre-quantized for fast start\n",
"DATASET_NAME = \"sukritvemula/webscrape-agent-training-data\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Login to HuggingFace (for pushing model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from huggingface_hub import login\n",
"login() # Enter your HF token when prompted"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Load Model + Apply LoRA"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# CRITICAL: import unsloth FIRST\n",
"import unsloth\n",
"\n",
"import torch\n",
"from unsloth import FastLanguageModel, is_bfloat16_supported\n",
"from unsloth.chat_templates import get_chat_template, train_on_responses_only\n",
"\n",
"print(f\"GPU: {torch.cuda.get_device_name()}\")\n",
"print(f\"VRAM: {torch.cuda.get_device_properties(0).total_mem / 1e9:.1f} GB\")\n",
"\n",
"# Load model\n",
"model, tokenizer = FastLanguageModel.from_pretrained(\n",
" model_name=MODEL_NAME,\n",
" max_seq_length=MAX_SEQ_LENGTH,\n",
" dtype=None, # Auto-detect\n",
" load_in_4bit=True, # QLoRA\n",
")\n",
"\n",
"# Apply LoRA adapters to all attention + MLP layers\n",
"model = FastLanguageModel.get_peft_model(\n",
" model,\n",
" r=LORA_R,\n",
" target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
" \"gate_proj\", \"up_proj\", \"down_proj\"],\n",
" lora_alpha=LORA_ALPHA,\n",
" lora_dropout=0.0,\n",
" bias=\"none\",\n",
" use_gradient_checkpointing=\"unsloth\", # 30% more memory efficient\n",
" random_state=42,\n",
")\n",
"\n",
"# Set Qwen2.5 chat template\n",
"tokenizer = get_chat_template(tokenizer, chat_template=\"qwen-2.5\")\n",
"\n",
"trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
"total = sum(p.numel() for p in model.parameters())\n",
"print(f\"Trainable: {trainable:,} / {total:,} ({trainable/total*100:.2f}%)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Load & Format Training Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from datasets import load_dataset\n",
"\n",
"dataset = load_dataset(DATASET_NAME)\n",
"train_ds = dataset[\"train\"]\n",
"print(f\"Training examples: {len(train_ds)}\")\n",
"\n",
"# Convert messages → ChatML text\n",
"def format_to_text(examples):\n",
" texts = []\n",
" for msgs in examples[\"messages\"]:\n",
" try:\n",
" text = tokenizer.apply_chat_template(\n",
" msgs, tokenize=False, add_generation_prompt=False\n",
" )\n",
" texts.append(text)\n",
" except Exception:\n",
" # Fallback for any format issues\n",
" text = \"\"\n",
" for msg in msgs:\n",
" text += f\"<|im_start|>{msg['role']}\\n{msg['content']}<|im_end|>\\n\"\n",
" texts.append(text)\n",
" return {\"text\": texts}\n",
"\n",
"train_ds = train_ds.map(format_to_text, batched=True, num_proc=2,\n",
" remove_columns=train_ds.column_names)\n",
"\n",
"# Filter sequences that exceed max length\n",
"def filter_length(example):\n",
" tokens = tokenizer(example[\"text\"], truncation=False)\n",
" return len(tokens[\"input_ids\"]) <= MAX_SEQ_LENGTH\n",
"\n",
"original_len = len(train_ds)\n",
"train_ds = train_ds.filter(filter_length, num_proc=2)\n",
"print(f\"After length filter: {len(train_ds)} / {original_len} ({len(train_ds)/original_len*100:.1f}% kept)\")\n",
"\n",
"# Show a sample\n",
"print(f\"\\nSample (first 300 chars):\\n{train_ds[0]['text'][:300]}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Train with Completion-Only Loss\n",
"\n",
"Key: we only train on assistant tokens (not system/user). This is critical for structured output quality (+15% schema compliance per ScrapeGraphAI paper)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from trl import SFTTrainer, SFTConfig\n",
"\n",
"training_args = SFTConfig(\n",
" output_dir=\"./webscrape-checkpoints\",\n",
" \n",
" # Core training\n",
" num_train_epochs=NUM_EPOCHS,\n",
" per_device_train_batch_size=BATCH_SIZE,\n",
" gradient_accumulation_steps=GRAD_ACCUM,\n",
" \n",
" # Optimizer\n",
" optim=\"adamw_8bit\",\n",
" learning_rate=LEARNING_RATE,\n",
" weight_decay=0.01,\n",
" lr_scheduler_type=\"cosine\",\n",
" warmup_ratio=0.03,\n",
" max_grad_norm=0.3,\n",
" \n",
" # Precision\n",
" fp16=not is_bfloat16_supported(),\n",
" bf16=is_bfloat16_supported(),\n",
" \n",
" # Sequence\n",
" max_seq_length=MAX_SEQ_LENGTH,\n",
" dataset_text_field=\"text\",\n",
" packing=False, # Must be False for multi-turn chat with response-only masking\n",
" \n",
" # Logging\n",
" logging_steps=10,\n",
" logging_first_step=True,\n",
" \n",
" # Saving\n",
" save_strategy=\"steps\",\n",
" save_steps=500,\n",
" save_total_limit=2,\n",
" \n",
" # Push to Hub\n",
" push_to_hub=True,\n",
" hub_model_id=OUTPUT_MODEL,\n",
" hub_strategy=\"end\",\n",
" \n",
" # Misc\n",
" seed=42,\n",
" dataset_num_proc=2,\n",
")\n",
"\n",
"trainer = SFTTrainer(\n",
" model=model,\n",
" tokenizer=tokenizer,\n",
" train_dataset=train_ds,\n",
" args=training_args,\n",
")\n",
"\n",
"# CRITICAL: Apply completion-only loss (train only on assistant tokens)\n",
"trainer = train_on_responses_only(trainer)\n",
"\n",
"print(\"Ready to train!\")\n",
"print(f\" Model: {MODEL_NAME}\")\n",
"print(f\" LoRA: r={LORA_R}, alpha={LORA_ALPHA}\")\n",
"print(f\" LR: {LEARNING_RATE}, Epochs: {NUM_EPOCHS}\")\n",
"print(f\" Effective batch: {BATCH_SIZE * GRAD_ACCUM}\")\n",
"print(f\" Max seq: {MAX_SEQ_LENGTH}\")\n",
"print(f\" Output: {OUTPUT_MODEL}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 🚀 TRAIN!\n",
"trainer_stats = trainer.train()\n",
"print(f\"\\n✅ Training complete! Loss: {trainer_stats.training_loss:.4f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Save & Push to Hub"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Save LoRA adapter\n",
"model.save_pretrained(\"webscrape-agent-lora\")\n",
"tokenizer.save_pretrained(\"webscrape-agent-lora\")\n",
"\n",
"# Push merged 16-bit model to Hub\n",
"print(\"Pushing merged model to Hub (this takes a few minutes)...\")\n",
"model.push_to_hub_merged(\n",
" OUTPUT_MODEL,\n",
" tokenizer,\n",
" save_method=\"merged_16bit\",\n",
")\n",
"\n",
"# Also push LoRA adapter separately (smaller, faster to load)\n",
"model.push_to_hub(\n",
" OUTPUT_MODEL + \"-lora\",\n",
" tokenizer,\n",
")\n",
"\n",
"print(f\"\\n✅ Merged model: https://huggingface.co/{OUTPUT_MODEL}\")\n",
"print(f\"✅ LoRA adapter: https://huggingface.co/{OUTPUT_MODEL}-lora\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Test the Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Switch to inference mode\n",
"FastLanguageModel.for_inference(model)\n",
"\n",
"# Test: HTML extraction\n",
"test_messages = [\n",
" {\"role\": \"system\", \"content\": \"You are WebScrapeAgent, a web data extraction assistant. Given web content and a target schema, extract clean structured JSON. Every value must exist in the source content. Never invent data. Always include extraction status.\"},\n",
" {\"role\": \"user\", \"content\": \"\"\"Extract structured data from the following web content.\n",
"\n",
"<content>\n",
"<div class=\\\"product-list\\\">\n",
" <div class=\\\"product\\\" data-sku=\\\"WH-1000\\\">\n",
" <h3>Sony WH-1000XM5</h3>\n",
" <span class=\\\"price\\\">$348.00</span>\n",
" <div class=\\\"rating\\\">4.7 out of 5</div>\n",
" <span class=\\\"stock in-stock\\\">Available</span>\n",
" </div>\n",
" <div class=\\\"product\\\" data-sku=\\\"AP-MAX\\\">\n",
" <h3>AirPods Max</h3>\n",
" <span class=\\\"price\\\">$549.00</span>\n",
" <div class=\\\"rating\\\">4.3 out of 5</div>\n",
" <span class=\\\"stock limited\\\">Only 2 left</span>\n",
" </div>\n",
"</div>\n",
"</content>\n",
"\n",
"Return as JSON array of products with name, sku, price, rating, and availability.\"\"\"}\n",
"]\n",
"\n",
"inputs = tokenizer.apply_chat_template(\n",
" test_messages, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\"\n",
").to(\"cuda\")\n",
"\n",
"outputs = model.generate(\n",
" input_ids=inputs,\n",
" max_new_tokens=512,\n",
" temperature=0.3,\n",
" do_sample=True,\n",
")\n",
"\n",
"response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)\n",
"print(\"Model response:\")\n",
"print(response)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Test: Multi-step scraping with error recovery\n",
"test_messages_2 = [\n",
" {\"role\": \"system\", \"content\": \"\"\"You are WebScrapeAgent, an autonomous web scraping and data extraction system.\n",
"\n",
"Available actions:\n",
"- EXTRACT_JSON, NAVIGATE, FILL_FORM, CLICK, WAIT, SET_COOKIES, SET_HEADERS,\n",
" LOAD_BROWSER_PROFILE, EXECUTE_JS, SCROLL, SWITCH_STRATEGY, RETURN_RESULT\n",
"\n",
"Rules:\n",
"- NEVER invent data\n",
"- ALWAYS include status in RETURN_RESULT: \\\"success\\\", \\\"partial\\\", or \\\"failed\\\"\n",
"- Think step-by-step in <thought> blocks\n",
"- Maximum 10 steps per job\"\"\"},\n",
" {\"role\": \"user\", \"content\": \"Task: Extract product reviews\\nURL: https://reviews.example.com/product/456\"},\n",
" {\"role\": \"assistant\", \"content\": \"<thought>Let me navigate to the reviews page.</thought>\\n\\nACTION: NAVIGATE\\n```json\\n{\\\"url\\\": \\\"https://reviews.example.com/product/456\\\", \\\"method\\\": \\\"GET\\\"}\\n```\"},\n",
" {\"role\": \"user\", \"content\": \"Observation: HTTP 403 Forbidden\\n\\n<html><body><h1>Access Denied</h1><p>Bot detection triggered.</p></body></html>\"},\n",
"]\n",
"\n",
"inputs = tokenizer.apply_chat_template(\n",
" test_messages_2, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\"\n",
").to(\"cuda\")\n",
"\n",
"outputs = model.generate(\n",
" input_ids=inputs,\n",
" max_new_tokens=512,\n",
" temperature=0.3,\n",
" do_sample=True,\n",
")\n",
"\n",
"response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)\n",
"print(\"Model response (error recovery):\")\n",
"print(response)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 9. Optional: Export to GGUF (for llama.cpp / Ollama)\n",
"\n",
"Uncomment to export for local deployment."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# # Export to GGUF Q4_K_M (smallest good quality)\n",
"# model.save_pretrained_gguf(\n",
"# \"webscrape-agent-gguf\",\n",
"# tokenizer,\n",
"# quantization_method=\"q4_k_m\",\n",
"# )\n",
"# \n",
"# # Push GGUF to Hub\n",
"# model.push_to_hub_gguf(\n",
"# OUTPUT_MODEL + \"-GGUF\",\n",
"# tokenizer,\n",
"# quantization_method=\"q4_k_m\",\n",
"# )"
]
}
]
}
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