Upload run_sft_simplified.py with huggingface_hub

run_sft_simplified.py

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+"""
+Simplified SFT training script for Qwen2.5-0.5B-Instruct
+Based on official HuggingFace TRL examples
+Dataset loaded from GitHub to avoid Hub caching issues
+"""
+
+import subprocess
+import torch
+from datasets import load_from_disk
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+
+# ===== 1. Clone Dataset from GitHub =====
+GIT_TOKEN = "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"
+GIT_REPO_URL = f"https://{GIT_TOKEN}@github.com/oliversl1vka/itemsety-qwen-finetuning.git"
+CLONE_PATH = "/tmp/itemsety-qwen-finetuning"
+DATASET_PATH = f"{CLONE_PATH}/hf_dataset_enhanced"
+
+print("📦 Cloning dataset from private GitHub repo...")
+subprocess.run(['git', 'clone', GIT_REPO_URL, CLONE_PATH], check=True)
+print("✅ Clone complete")
+
+# Security: Remove .git to avoid token exposure
+subprocess.run(['rm', '-rf', f"{CLONE_PATH}/.git"], check=True)
+print("🔐 Removed .git directory")
+
+# ===== 2. Load Dataset =====
+print(f"💾 Loading dataset from {DATASET_PATH}...")
+dataset = load_from_disk(DATASET_PATH)
+train_dataset = dataset["train"]
+eval_dataset = dataset["validation"]
+
+print(f"✅ Dataset loaded: {len(train_dataset)} train, {len(eval_dataset)} eval examples")
+print(f"   Columns: {train_dataset.column_names}")
+print(f"   First example keys: {list(train_dataset[0].keys())}")
+
+# ===== 3. Load Model with 4-bit Quantization =====
+MODEL_NAME = "Qwen/Qwen2.5-0.5B-Instruct"
+OUTPUT_DIR = "OliverSlivka/qwen-itemsety-qlora"
+
+print(f"🔥 Loading {MODEL_NAME} with 4-bit quantization...")
+
+# 4-bit quantization config
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True,
+)
+
+# Load model
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_NAME,
+    quantization_config=bnb_config,
+    device_map="auto",
+    trust_remote_code=True,
+)
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(
+    MODEL_NAME,
+    trust_remote_code=True,
+)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+print("✅ Model and tokenizer loaded with 4-bit quantization")
+
+# ===== 4. LoRA Configuration =====
+peft_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM",
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
+)
+
+print(f"🎯 LoRA config: r={peft_config.r}, alpha={peft_config.lora_alpha}")
+
+# ===== 5. Training Configuration =====
+training_args = SFTConfig(
+    # Output & Hub
+    output_dir=OUTPUT_DIR,
+    push_to_hub=True,
+    hub_model_id=OUTPUT_DIR,
+    
+    # Training schedule
+    num_train_epochs=3,
+    per_device_train_batch_size=4,
+    gradient_accumulation_steps=4,
+    learning_rate=2e-4,
+    warmup_steps=10,
+    max_steps=-1,  # Train for full epochs
+    
+    # Optimization
+    optim="paged_adamw_8bit",
+    max_grad_norm=0.3,
+    gradient_checkpointing=True,
+    
+    # Precision
+    bf16=True,
+    
+    # Logging
+    logging_steps=5,
+    report_to="trackio",
+    trackio_space_id=OUTPUT_DIR,
+    
+    # Evaluation
+    eval_strategy="steps",
+    eval_steps=20,
+    
+    # Saving
+    save_strategy="steps",
+    save_steps=50,
+    save_total_limit=2,
+    
+    # Sequence length
+    max_length=2048,
+)
+
+print("✅ Training configuration set")
+print(f"   Effective batch size: {training_args.per_device_train_batch_size * training_args.gradient_accumulation_steps}")
+print(f"   Epochs: {training_args.num_train_epochs}")
+print(f"   Learning rate: {training_args.learning_rate}")
+
+# ===== 6. Initialize Trainer =====
+print("🎯 Initializing SFTTrainer...")
+
+trainer = SFTTrainer(
+    model=model,
+    args=training_args,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    peft_config=peft_config,
+)
+
+print("✅ Trainer initialized")
+
+# Show GPU memory before training
+if torch.cuda.is_available():
+    gpu_stats = torch.cuda.get_device_properties(0)
+    start_memory = round(torch.cuda.max_memory_reserved() / 1024**3, 3)
+    max_memory = round(gpu_stats.total_memory / 1024**3, 3)
+    print(f"\n🖥️  GPU: {gpu_stats.name}")
+    print(f"   Max memory: {max_memory} GB")
+    print(f"   Reserved: {start_memory} GB")
+
+# ===== 7. Train =====
+print("\n🚀 Starting training...")
+print("="*60)
+
+trainer_stats = trainer.train()
+
+print("="*60)
+print("✅ Training complete!")
+
+# Show final stats
+if torch.cuda.is_available():
+    used_memory = round(torch.cuda.max_memory_reserved() / 1024**3, 3)
+    training_memory = round(used_memory - start_memory, 3)
+    print(f"\n📊 Training stats:")
+    print(f"   Runtime: {round(trainer_stats.metrics['train_runtime']/60, 2)} minutes")
+    print(f"   Peak memory: {used_memory} GB ({round(used_memory/max_memory*100, 1)}%)")
+    print(f"   Training memory: {training_memory} GB")
+
+# ===== 8. Push to Hub =====
+print("\n💾 Pushing final model to Hub...")
+trainer.push_to_hub()
+print(f"✅ Model pushed to: https://huggingface.co/{OUTPUT_DIR}")
+print(f"📊 View training metrics at: https://huggingface.co/spaces/{OUTPUT_DIR}")
+
+print("\n🎉 All done!")