File size: 22,053 Bytes
ee54ebc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 | {
"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": [
"# 🤖 MCP-Agent-1.7B — Training Notebook\n",
"\n",
"**What we're building:** The first open-source small language model that natively speaks the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/). It plans and executes multi-step tool chains with DAG dependencies.\n",
"\n",
"**Base model:** [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) (2B params, Apache 2.0)\n",
"\n",
"**Method:** LoRA SFT (rank=16, all linear layers)\n",
"\n",
"**Cost:** $0 (Google Colab free T4 GPU)\n",
"\n",
"**Time:** ~2 hours\n",
"\n",
"---\n",
"\n",
"## 🎓 ML Concepts You'll Learn\n",
"1. **LoRA** — How to fine-tune a 2B model by only training 2% of parameters\n",
"2. **SFT** — Supervised Fine-Tuning: teaching a model with input→output examples\n",
"3. **bf16** — Half-precision training to cut memory usage in half\n",
"4. **Gradient Checkpointing** — Trading compute for memory\n",
"5. **Cosine LR Schedule** — Why we slow down learning over time\n",
"\n",
"---\n",
"\n",
"⚡ **Before you start:** Go to `Runtime → Change runtime type → T4 GPU`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 0: Verify GPU & Install Dependencies\n",
"\n",
"🎓 **What's happening:** We check that Colab gave us a GPU, then install the ML libraries.\n",
"- `transformers` — HuggingFace's core library for loading/using AI models\n",
"- `trl` — Training library specifically for fine-tuning language models (SFT, RLHF, DPO)\n",
"- `peft` — Parameter-Efficient Fine-Tuning (LoRA lives here)\n",
"- `datasets` — For loading our training data from HuggingFace Hub\n",
"- `accelerate` — Makes training work on any hardware (CPU, GPU, multi-GPU)\n",
"- `bitsandbytes` — Memory-efficient optimizers and quantization"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Check GPU — this MUST show \"Tesla T4\" or similar\n",
"!nvidia-smi\n",
"\n",
"import torch\n",
"print(f\"\\n✅ PyTorch version: {torch.__version__}\")\n",
"print(f\"✅ CUDA available: {torch.cuda.is_available()}\")\n",
"if torch.cuda.is_available():\n",
" print(f\"✅ GPU: {torch.cuda.get_device_name(0)}\")\n",
" print(f\"✅ VRAM: {torch.cuda.get_device_properties(0).total_mem / 1e9:.1f} GB\")\n",
"else:\n",
" raise RuntimeError(\"❌ No GPU! Go to Runtime → Change runtime type → T4 GPU\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Install all dependencies (takes ~2-3 minutes)\n",
"!pip install -q transformers trl peft datasets accelerate bitsandbytes huggingface_hub\n",
"print(\"\\n✅ All packages installed!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 1: Login to HuggingFace\n",
"\n",
"🎓 **Why?** We need to:\n",
"1. Download Qwen3-1.7B from HuggingFace Hub\n",
"2. **Push our trained model** back to your HuggingFace account\n",
"\n",
"Get your token at: https://huggingface.co/settings/tokens (needs **Write** permission)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from huggingface_hub import notebook_login\n",
"notebook_login() # Paste your HF token when prompted"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 2: Load Dataset\n",
"\n",
"🎓 **What's our data?** 16,520 conversations teaching the model to:\n",
"- Call tools using MCP protocol (JSON-RPC format)\n",
"- Plan multi-step tool chains with dependencies\n",
"- Ask clarifying questions when info is missing\n",
"- Refuse dangerous requests\n",
"\n",
"Each example is a conversation: `[{role: system, content: ...}, {role: user, content: ...}, {role: assistant, content: ...}]`\n",
"\n",
"The SFTTrainer automatically detects this `messages` format and applies the model's chat template."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from datasets import load_dataset\n",
"\n",
"dataset = load_dataset(\"muhammadtlha944/mcp-agent-training-data\")\n",
"\n",
"print(f\"📊 Train examples: {len(dataset['train']):,}\")\n",
"print(f\"📊 Validation examples: {len(dataset['validation']):,}\")\n",
"print(f\"📊 Columns: {dataset['train'].column_names}\")\n",
"\n",
"# Let's peek at one example\n",
"print(f\"\\n📝 Sample conversation (first 2 messages):\")\n",
"sample = dataset['train'][0]['messages']\n",
"for msg in sample[:2]:\n",
" role = msg['role']\n",
" content = msg['content'][:200] + '...' if len(msg['content']) > 200 else msg['content']\n",
" print(f\" [{role}]: {content}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 3: Configure LoRA\n",
"\n",
"🎓 **LoRA (Low-Rank Adaptation) — The Key Idea:**\n",
"\n",
"Instead of updating all 2 billion parameters (which would need ~16GB+ VRAM just for optimizer states), we add tiny trainable matrices to each layer.\n",
"\n",
"Think of it like this:\n",
"- **Full fine-tuning** = Rewriting an entire textbook (expensive, slow)\n",
"- **LoRA** = Adding sticky notes to key pages (cheap, fast, nearly as effective)\n",
"\n",
"**Parameters explained:**\n",
"- `r=16` — Rank of the adapter matrices. Like resolution: higher = more detail but more memory. 16 is the sweet spot for 16K examples.\n",
"- `lora_alpha=32` — Scaling factor (rule of thumb: 2× rank). Controls how strongly LoRA affects output.\n",
"- `target_modules=\"all-linear\"` — Apply LoRA to ALL linear layers, not just attention. Research paper \"LoRA Without Regret\" proved this matches full fine-tuning quality.\n",
"- `lora_dropout=0.05` — 5% dropout prevents overfitting (randomly zeros out some adapter weights during training)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from peft import LoraConfig\n",
"\n",
"peft_config = LoraConfig(\n",
" r=16, # Rank — 16 dimensions per adapter\n",
" lora_alpha=32, # Scaling factor — 2x rank\n",
" lora_dropout=0.05, # 5% dropout for regularization\n",
" bias=\"none\", # No bias terms — saves memory, no quality loss\n",
" task_type=\"CAUSAL_LM\", # This is a language model (predicts next token)\n",
" target_modules=\"all-linear\", # Apply to ALL linear layers\n",
")\n",
"\n",
"print(\"✅ LoRA config ready!\")\n",
"print(f\" Rank: {peft_config.r}\")\n",
"print(f\" Alpha: {peft_config.lora_alpha}\")\n",
"print(f\" Targets: {peft_config.target_modules}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 4: Configure Training\n",
"\n",
"🎓 **Hyperparameters — The Recipe:**\n",
"\n",
"Training a model is like cooking. The hyperparameters are your recipe:\n",
"\n",
"| Parameter | Value | Why |\n",
"|-----------|-------|-----|\n",
"| **Learning rate** | 2e-4 | 10× higher than full fine-tuning because LoRA updates fewer params — each update needs more impact |\n",
"| **Batch size** | 4 × 4 = 16 effective | Process 4 examples at once, accumulate 4 times before updating weights |\n",
"| **Epochs** | 3 | See the data 3 times. 1 = underfitting, 10 = overfitting, 3 = sweet spot |\n",
"| **Warmup** | 10% of steps | Start with tiny learning rate, ramp up gradually. Prevents early instability |\n",
"| **LR schedule** | Cosine | Learning rate follows a cosine curve: high in middle, low at end. Helps convergence |\n",
"| **Max seq length** | 2048 tokens | Covers our examples while fitting in T4's 16GB VRAM |"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from trl import SFTConfig\n",
"\n",
"training_args = SFTConfig(\n",
" # === Output ===\n",
" output_dir=\"./mcp-agent-checkpoints\",\n",
"\n",
" # === Core hyperparameters ===\n",
" num_train_epochs=3,\n",
" per_device_train_batch_size=4, # 4 examples per GPU step\n",
" gradient_accumulation_steps=4, # Accumulate 4 steps → effective batch = 16\n",
" learning_rate=2e-4, # 10x base LR for LoRA\n",
" weight_decay=0.01, # L2 regularization\n",
" lr_scheduler_type=\"cosine\", # Cosine decay\n",
" warmup_ratio=0.1, # 10% warmup\n",
" max_grad_norm=1.0, # Gradient clipping\n",
" max_seq_length=2048, # Max tokens per example\n",
"\n",
" # === Memory optimization (critical for T4 16GB!) ===\n",
" bf16=False, # T4 doesn't support bf16 well\n",
" fp16=True, # Use fp16 instead — T4 is great at this\n",
" gradient_checkpointing=True, # Trade compute for memory\n",
" gradient_checkpointing_kwargs={\"use_reentrant\": False},\n",
"\n",
" # === Logging ===\n",
" logging_steps=10,\n",
" logging_first_step=True,\n",
" logging_strategy=\"steps\",\n",
"\n",
" # === Evaluation ===\n",
" eval_strategy=\"steps\",\n",
" eval_steps=200,\n",
" per_device_eval_batch_size=4,\n",
"\n",
" # === Checkpointing ===\n",
" save_strategy=\"steps\",\n",
" save_steps=200,\n",
" save_total_limit=2, # Keep 2 checkpoints (save disk space)\n",
" load_best_model_at_end=True,\n",
" metric_for_best_model=\"eval_loss\",\n",
"\n",
" # === Push to HuggingFace Hub ===\n",
" push_to_hub=True,\n",
" hub_model_id=\"muhammadtlha944/MCP-Agent-1.7B\",\n",
" hub_strategy=\"end\",\n",
"\n",
" # === Misc ===\n",
" seed=42,\n",
" dataloader_num_workers=2,\n",
" optim=\"adamw_torch\",\n",
")\n",
"\n",
"# Print training stats\n",
"steps_per_epoch = len(dataset['train']) // (4 * 4) # train_size // effective_batch\n",
"total_steps = steps_per_epoch * 3\n",
"print(f\"✅ Training config ready!\")\n",
"print(f\" Effective batch size: 16\")\n",
"print(f\" Steps per epoch: {steps_per_epoch}\")\n",
"print(f\" Total steps: {total_steps}\")\n",
"print(f\" Warmup steps: {int(total_steps * 0.1)}\")\n",
"print(f\" Estimated time: ~2 hours on T4\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 5: Load Tokenizer\n",
"\n",
"🎓 **Tokenizer — Translating Words to Numbers:**\n",
"\n",
"AI models don't understand text — they work with numbers. The tokenizer converts:\n",
"- `\"Hello world\"` → `[9707, 1879]` (encoding)\n",
"- `[9707, 1879]` → `\"Hello world\"` (decoding)\n",
"\n",
"Qwen3 uses a **chat template** that wraps conversations in special tokens like `<|im_start|>user` and `<|im_end|>`. The SFTTrainer applies this automatically to our `messages` data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from transformers import AutoTokenizer\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(\n",
" \"Qwen/Qwen3-1.7B\",\n",
" trust_remote_code=True,\n",
")\n",
"\n",
"print(f\"✅ Tokenizer loaded!\")\n",
"print(f\" Vocab size: {tokenizer.vocab_size:,}\")\n",
"\n",
"# Demo: see how tokenization works\n",
"demo_text = \"Call the GitHub search tool\"\n",
"tokens = tokenizer.encode(demo_text)\n",
"print(f\"\\n📝 Demo: '{demo_text}'\")\n",
"print(f\" → Token IDs: {tokens}\")\n",
"print(f\" → Tokens: {[tokenizer.decode([t]) for t in tokens]}\")\n",
"print(f\" → {len(tokens)} tokens\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 6: Create Trainer & Start Training! 🚀\n",
"\n",
"🎓 **SFTTrainer does everything:**\n",
"1. Loads the 2B parameter model onto the GPU\n",
"2. Injects LoRA adapters into all linear layers (~40M trainable params out of 2B)\n",
"3. Tokenizes all conversations using the chat template\n",
"4. Runs the training loop for 3 epochs\n",
"5. Evaluates on validation set every 200 steps\n",
"6. Saves checkpoints and picks the best one\n",
"7. Pushes the final model to HuggingFace Hub\n",
"\n",
"**What to watch:** The `loss` value should go DOWN over time. This means the model is learning. If loss goes up after going down, that's overfitting (the model is memorizing instead of learning).\n",
"\n",
"⏱️ **This cell takes ~2 hours. Don't close the tab!**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from trl import SFTTrainer\n",
"\n",
"print(\"🔧 Loading model and applying LoRA adapters...\")\n",
"print(\" (This takes 2-3 minutes — downloading 2B parameters)\\n\")\n",
"\n",
"trainer = SFTTrainer(\n",
" model=\"Qwen/Qwen3-1.7B\",\n",
" args=training_args,\n",
" train_dataset=dataset[\"train\"],\n",
" eval_dataset=dataset[\"validation\"],\n",
" peft_config=peft_config,\n",
" processing_class=tokenizer,\n",
")\n",
"\n",
"# Print parameter stats\n",
"trainable = sum(p.numel() for p in trainer.model.parameters() if p.requires_grad)\n",
"total = sum(p.numel() for p in trainer.model.parameters())\n",
"print(f\"\\n📊 Model loaded!\")\n",
"print(f\" Total parameters: {total:,}\")\n",
"print(f\" Trainable (LoRA): {trainable:,}\")\n",
"print(f\" Trainable %: {100 * trainable / total:.2f}%\")\n",
"print(f\" GPU memory used: {torch.cuda.memory_allocated() / 1e9:.1f} GB\")\n",
"print(f\"\\n🚀 Starting training...\\n\")\n",
"\n",
"# TRAIN!\n",
"train_result = trainer.train()\n",
"\n",
"print(f\"\\n✅ Training complete!\")\n",
"print(f\" Final loss: {train_result.metrics.get('train_loss', 'N/A')}\")\n",
"print(f\" Runtime: {train_result.metrics.get('train_runtime', 0)/3600:.1f} hours\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 7: Evaluate & Push to Hub\n",
"\n",
"🎓 **Evaluation:** We run the model on the validation set (826 examples it has NEVER seen during training) to measure real performance. If eval loss is close to train loss = good generalization. If eval loss >> train loss = overfitting."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Final evaluation\n",
"print(\"📊 Running final evaluation...\")\n",
"eval_metrics = trainer.evaluate()\n",
"print(f\" Eval loss: {eval_metrics['eval_loss']:.4f}\")\n",
"\n",
"# Save metrics\n",
"trainer.log_metrics(\"train\", train_result.metrics)\n",
"trainer.save_metrics(\"train\", train_result.metrics)\n",
"trainer.log_metrics(\"eval\", eval_metrics)\n",
"trainer.save_metrics(\"eval\", eval_metrics)\n",
"\n",
"# Push to HuggingFace Hub\n",
"print(\"\\n🚀 Pushing model to HuggingFace Hub...\")\n",
"trainer.push_to_hub(\n",
" commit_message=\"MCP-Agent-1.7B: LoRA fine-tuned Qwen3-1.7B for MCP tool calling\",\n",
" tags=[\"mcp\", \"tool-calling\", \"function-calling\", \"agent\", \"qwen3\", \"lora\"],\n",
")\n",
"\n",
"print(f\"\\n\" + \"=\"*60)\n",
"print(f\"🎉 MCP-Agent-1.7B is LIVE!\")\n",
"print(f\"=\"*60)\n",
"print(f\"📦 Model: https://huggingface.co/muhammadtlha944/MCP-Agent-1.7B\")\n",
"print(f\"📊 Train loss: {train_result.metrics.get('train_loss', 'N/A'):.4f}\")\n",
"print(f\"📊 Eval loss: {eval_metrics['eval_loss']:.4f}\")\n",
"print(f\"⏱️ Training time: {train_result.metrics.get('train_runtime', 0)/3600:.1f} hours\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 8: Test Your Model! 🧪\n",
"\n",
"Let's see MCP-Agent-1.7B in action — give it a request and watch it plan tool calls!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Quick test — see the model generate MCP tool calls\n",
"from transformers import pipeline\n",
"\n",
"print(\"🧪 Testing MCP-Agent-1.7B...\\n\")\n",
"\n",
"pipe = pipeline(\n",
" \"text-generation\",\n",
" model=trainer.model,\n",
" tokenizer=tokenizer,\n",
" max_new_tokens=512,\n",
" do_sample=True,\n",
" temperature=0.7,\n",
")\n",
"\n",
"test_prompts = [\n",
" # Test 1: Simple tool call\n",
" {\n",
" \"messages\": [\n",
" {\"role\": \"system\", \"content\": \"You are an MCP agent with access to tools: github_search, read_file, shell_exec. Use JSON-RPC format for tool calls.\"},\n",
" {\"role\": \"user\", \"content\": \"Find all Python files in the src/ directory that import pandas\"}\n",
" ]\n",
" },\n",
" # Test 2: Multi-step planning\n",
" {\n",
" \"messages\": [\n",
" {\"role\": \"system\", \"content\": \"You are an MCP agent with access to tools: github_search, read_file, shell_exec, sqlite_query. Plan multi-step tool chains when needed.\"},\n",
" {\"role\": \"user\", \"content\": \"Clone the repo https://github.com/example/app, find all TODO comments, and create a summary report\"}\n",
" ]\n",
" },\n",
" # Test 3: Clarification (should ask for missing info)\n",
" {\n",
" \"messages\": [\n",
" {\"role\": \"system\", \"content\": \"You are an MCP agent. Ask for clarification when the request is ambiguous or missing critical information.\"},\n",
" {\"role\": \"user\", \"content\": \"Delete the database\"}\n",
" ]\n",
" },\n",
"]\n",
"\n",
"for i, prompt in enumerate(test_prompts, 1):\n",
" print(f\"{'='*60}\")\n",
" print(f\"TEST {i}: {prompt['messages'][-1]['content']}\")\n",
" print(f\"{'='*60}\")\n",
" result = pipe(prompt['messages'])\n",
" assistant_msg = result[0]['generated_text'][-1]['content']\n",
" print(f\"\\n🤖 MCP-Agent Response:\\n{assistant_msg}\\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 🎉 Congratulations!\n",
"\n",
"You just trained an AI model! Here's what you accomplished:\n",
"\n",
"- ✅ Fine-tuned a 2 billion parameter model using LoRA\n",
"- ✅ Trained on 16,520 MCP tool-calling examples\n",
"- ✅ Published your model to HuggingFace Hub\n",
"- ✅ Tested it on real MCP scenarios\n",
"\n",
"**Your model:** [muhammadtlha944/MCP-Agent-1.7B](https://huggingface.co/muhammadtlha944/MCP-Agent-1.7B)\n",
"\n",
"**Next steps:**\n",
"1. Try more test prompts above\n",
"2. Share on X/Twitter with #MCP-Agent\n",
"3. Build a Gradio demo for interactive testing\n",
"\n",
"---\n",
"*Built by Muhammad Talha — Learning ML by building real projects*"
]
}
]
} |