Upload train_qwen3_8b_hf.py with huggingface_hub

train_qwen3_8b_hf.py

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+# /// script
+# dependencies = [
+#     "trl>=0.12.0",
+#     "peft>=0.7.0",
+#     "transformers>=4.36.0",
+#     "accelerate>=0.24.0",
+#     "trackio",
+#     "bitsandbytes",
+#     "datasets"
+# ]
+# ///
+
+"""
+Fine-tune Qwen3-8B on Vyvo Life CoPilot conversations dataset.
+"""
+
+import json
+from datasets import load_dataset, Dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+
+print("📦 Loading dataset from Hub...")
+raw_dataset = load_dataset("Codyfederer/vyvo-text-conversations", split="train")
+print(f"✅ Loaded {len(raw_dataset)} conversations")
+
+# Convert to messages format
+print("🔄 Converting to messages format...")
+conversations = []
+for item in raw_dataset:
+    messages = []
+    for turn in item['turns']:
+        messages.append({
+            'role': turn['role'],
+            'content': turn['content']
+        })
+    conversations.append({'messages': messages})
+
+dataset = Dataset.from_list(conversations)
+print(f"✅ Converted {len(dataset)} conversations")
+
+# Create train/eval split
+print("🔀 Creating train/eval split...")
+dataset_split = dataset.train_test_split(test_size=0.05, 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(
+    # Hub settings
+    output_dir="qwen3-8b-vyvo-copilot",
+    push_to_hub=True,
+    hub_model_id="Codyfederer/qwen3-8b-vyvo-copilot",
+    hub_strategy="every_save",
+    hub_private_repo=False,
+
+    # Training parameters - optimized for 8B model with LoRA
+    num_train_epochs=3,
+    per_device_train_batch_size=2,
+    gradient_accumulation_steps=8,  # Effective batch size = 16
+    learning_rate=2e-4,
+    max_length=2048,  # Good context for multi-turn conversations
+
+    # Memory optimization
+    gradient_checkpointing=True,
+    bf16=True,
+
+    # Logging & checkpointing
+    logging_steps=10,
+    save_strategy="steps",
+    save_steps=200,
+    save_total_limit=3,
+
+    # Evaluation
+    eval_strategy="steps",
+    eval_steps=200,
+
+    # Optimization
+    warmup_ratio=0.05,
+    lr_scheduler_type="cosine",
+    weight_decay=0.01,
+
+    # Monitoring
+    report_to="trackio",
+    project="vyvo-copilot-training",
+    run_name="qwen3-8b-sft-v1",
+)
+
+# LoRA configuration - optimized for Qwen3
+peft_config = LoraConfig(
+    r=32,
+    lora_alpha=64,
+    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"],
+)
+
+# Initialize and train
+print("🎯 Initializing trainer with Qwen/Qwen3-8B...")
+trainer = SFTTrainer(
+    model="Qwen/Qwen3-8B",
+    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("📦 Model saved to: https://huggingface.co/Codyfederer/qwen3-8b-vyvo-copilot")
+print("📊 View metrics at: https://huggingface.co/spaces/Codyfederer/trackio")