Upload train_genomics_lora.py with huggingface_hub

train_genomics_lora.py

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+# /// script
+# dependencies = [
+#     "trl>=0.12.0",
+#     "peft>=0.7.0",
+#     "trackio",
+#     "torch",
+#     "transformers>=4.44.0",
+#     "datasets",
+#     "accelerate",
+# ]
+# ///
+
+"""
+LoRA Fine-tuning for Qwen2.5-72B on Consumer Genomics Data
+Trains model to interpret SNP data and provide health insights
+"""
+
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+import trackio
+import torch
+
+print("="*80)
+print("Qwen2.5-72B LoRA Fine-tuning for Genomics Interpretation")
+print("="*80)
+
+# Load dataset from Hub
+print("\n[1/4] Loading dataset...")
+dataset = load_dataset("mattPearce/genellm-genomics-finetune", split="train")
+print(f"✓ Loaded {len(dataset)} training examples")
+
+# Create train/eval split for monitoring training progress
+print("\n[2/4] Creating train/eval split...")
+dataset_split = dataset.train_test_split(test_size=0.05, seed=42)  # 5% for eval (25 examples)
+print(f"✓ Train: {len(dataset_split['train'])} examples")
+print(f"✓ Eval: {len(dataset_split['test'])} examples")
+
+# LoRA configuration optimized for 72B model
+print("\n[3/4] Configuring LoRA...")
+peft_config = LoraConfig(
+    r=32,                          # Rank - higher for better quality on large models
+    lora_alpha=64,                 # Scaling factor (typically 2x rank)
+    target_modules=[               # Apply LoRA to all attention layers
+        "q_proj",
+        "k_proj",
+        "v_proj",
+        "o_proj",
+        "gate_proj",
+        "up_proj",
+        "down_proj"
+    ],
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM"
+)
+print(f"✓ LoRA config: r={peft_config.r}, alpha={peft_config.lora_alpha}")
+print(f"✓ Target modules: {len(peft_config.target_modules)} layer types")
+
+# Training configuration
+print("\n[4/4] Setting up training...")
+training_args = SFTConfig(
+    # Model output
+    output_dir="qwen2.5-72b-genomics-lora",
+
+    # Hub configuration - CRITICAL for saving results
+    push_to_hub=True,
+    hub_model_id="mattPearce/qwen2.5-72b-genomics-lora",
+    hub_strategy="every_save",     # Push checkpoints to Hub
+    hub_private_repo=False,
+
+    # Training hyperparameters
+    num_train_epochs=3,
+    per_device_train_batch_size=1,   # Small batch for 72B model
+    gradient_accumulation_steps=16,  # Effective batch size = 16
+    learning_rate=2e-4,              # Standard for LoRA
+    lr_scheduler_type="cosine",
+    warmup_steps=50,
+
+    # Optimization for memory efficiency
+    gradient_checkpointing=True,
+    bf16=True,                       # Use bfloat16 for better stability
+
+    # Evaluation strategy
+    eval_strategy="steps",
+    eval_steps=25,                   # Evaluate every 25 steps
+
+    # Checkpointing
+    save_strategy="steps",
+    save_steps=50,                   # Save every 50 steps
+    save_total_limit=3,              # Keep only 3 most recent checkpoints
+
+    # Logging and monitoring
+    logging_steps=5,
+    report_to="trackio",
+    run_name="qwen2.5-72b-genomics-v1",
+
+    # Misc
+    seed=42,
+    remove_unused_columns=True,
+)
+
+print(f"✓ Training config:")
+print(f"  Epochs: {training_args.num_train_epochs}")
+print(f"  Effective batch size: {training_args.per_device_train_batch_size * training_args.gradient_accumulation_steps}")
+print(f"  Learning rate: {training_args.learning_rate}")
+print(f"  Hub model ID: {training_args.hub_model_id}")
+
+# Initialize trainer
+print("\n[Starting Training]")
+print("Model: Qwen/Qwen2.5-72B")
+print("Method: LoRA fine-tuning")
+print("Trackio monitoring: https://huggingface.co/spaces/mattPearce/trackio")
+print("="*80)
+
+trainer = SFTTrainer(
+    model="Qwen/Qwen2.5-72B",
+    train_dataset=dataset_split["train"],
+    eval_dataset=dataset_split["test"],
+    peft_config=peft_config,
+    args=training_args,
+)
+
+# Train the model
+trainer.train()
+
+# Save final model to Hub
+print("\n[Finalizing]")
+print("Pushing final model to Hub...")
+trainer.push_to_hub()
+
+print("\n" + "="*80)
+print("✓ Training completed successfully!")
+print(f"✓ Model saved to: https://huggingface.co/mattPearce/qwen2.5-72b-genomics-lora")
+print("="*80)