Upload train_sft_qwen.py with huggingface_hub

train_sft_qwen.py

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+#!/usr/bin/env python3
+# /// script
+# dependencies = [
+#     "trl>=0.12.0",
+#     "peft>=0.7.0",
+#     "transformers>=4.36.0",
+#     "accelerate>=0.24.0",
+#     "trackio",
+# ]
+# ///
+
+"""
+SFT Training Script for Qwen/Qwen2.5-0.5B
+
+This script fine-tunes Qwen/Qwen2.5-0.5B using Supervised Fine-Tuning (SFT)
+with LoRA for efficient training on the Capybara dataset.
+
+Features:
+- Trackio integration for real-time monitoring
+- LoRA/PEFT for memory-efficient training
+- Automatic Hub saving with checkpoints
+- Train/eval split for progress monitoring
+"""
+
+import trackio
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+
+# Load dataset
+print("📦 Loading dataset...")
+dataset = load_dataset("trl-lib/Capybara", split="train")
+print(f"✅ Dataset loaded: {len(dataset)} examples")
+
+# Create train/eval split
+print("🔀 Creating train/eval split...")
+dataset_split = dataset.train_test_split(test_size=0.1, seed=42)
+train_dataset = dataset_split["train"]
+eval_dataset = dataset_split["test"]
+print(f"   Train: {len(train_dataset)} examples")
+print(f"   Eval: {len(eval_dataset)} examples")
+
+# Training configuration
+config = SFTConfig(
+    # CRITICAL: Hub settings
+    output_dir="qwen-capybara-sft",
+    push_to_hub=True,
+    hub_model_id="vgtomahawk/qwen-capybara-sft",
+    hub_strategy="every_save",  # Push checkpoints
+
+    # Training parameters
+    num_train_epochs=3,
+    per_device_train_batch_size=4,
+    gradient_accumulation_steps=4,
+    learning_rate=2e-5,
+
+    # Logging & checkpointing
+    logging_steps=10,
+    save_strategy="steps",
+    save_steps=100,
+    save_total_limit=2,
+
+    # Evaluation
+    eval_strategy="steps",
+    eval_steps=100,
+
+    # Optimization
+    warmup_ratio=0.1,
+    lr_scheduler_type="cosine",
+
+    # Monitoring with Trackio
+    report_to="trackio",
+    project="qwen-sft-training",
+    run_name="qwen-0.5b-capybara-baseline",
+)
+
+# LoRA configuration for efficient training
+peft_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM",
+    target_modules=["q_proj", "v_proj"],
+)
+
+# Initialize trainer
+print("🎯 Initializing trainer...")
+trainer = SFTTrainer(
+    model="Qwen/Qwen2.5-0.5B",
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    args=config,
+    peft_config=peft_config,
+)
+
+print("🚀 Starting training...")
+trainer.train()
+
+print("💾 Pushing final model to Hub...")
+trainer.push_to_hub()
+
+print("✅ Training complete!")
+print(f"📦 Model: https://huggingface.co/vgtomahawk/qwen-capybara-sft")
+print(f"📊 Metrics: https://huggingface.co/spaces/vgtomahawk/trackio")