Upload train_ministral.py with huggingface_hub

train_ministral.py

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+# /// script
+# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "trackio", "torch>=2.0.0", "transformers>=4.40.0"]
+# ///
+
+"""Fine-tune Ministral-3-3B-Instruct on NATO doctrine dataset."""
+
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+import trackio
+
+# Load dataset from HF Hub
+print("Loading NATO doctrine dataset...")
+dataset = load_dataset("AndreasThinks/nato-doctrine-sft", split="train")
+dataset_test = load_dataset("AndreasThinks/nato-doctrine-sft", split="test")
+
+print(f"✓ Train set: {len(dataset)} examples")
+print(f"✓ Test set: {len(dataset_test)} examples")
+
+# Configure LoRA for efficient fine-tuning
+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"]
+)
+
+# Training configuration
+training_args = SFTConfig(
+    output_dir="nato-ministral-3b",
+
+    # Model saving
+    push_to_hub=True,
+    hub_model_id="AndreasThinks/ministral-3b-nato-doctrine",
+    hub_strategy="every_save",
+    hub_private_repo=False,
+
+    # Training parameters
+    num_train_epochs=3,
+    per_device_train_batch_size=2,
+    per_device_eval_batch_size=2,
+    gradient_accumulation_steps=8,  # Effective batch size = 16
+    gradient_checkpointing=True,
+
+    # Learning rate
+    learning_rate=2e-4,
+    lr_scheduler_type="cosine",
+    warmup_ratio=0.1,
+
+    # Optimization
+    optim="adamw_torch",
+    weight_decay=0.01,
+    max_grad_norm=1.0,
+
+    # Evaluation
+    eval_strategy="steps",
+    eval_steps=50,
+
+    # Logging and saving
+    logging_steps=10,
+    save_strategy="steps",
+    save_steps=100,
+    save_total_limit=3,
+
+    # Monitoring with Trackio
+    report_to="trackio",
+    run_name="nato-ministral-3b-v1",
+    project="nato-doctrine-training",
+
+    # Other
+    bf16=True,  # Use bfloat16 for better stability
+    seed=42,
+)
+
+# Initialize trainer
+print("\n✓ Initializing SFT trainer...")
+trainer = SFTTrainer(
+    model="mistralai/Ministral-3-3B-Instruct-2512",
+    train_dataset=dataset,
+    eval_dataset=dataset_test,
+    peft_config=peft_config,
+    args=training_args,
+)
+
+# Start training
+print("\n✓ Starting training...")
+print(f"  Model: mistralai/Ministral-3-3B-Instruct-2512")
+print(f"  Training examples: {len(dataset)}")
+print(f"  Test examples: {len(dataset_test)}")
+print(f"  Epochs: 3")
+print(f"  LoRA rank: 16")
+print(f"  Output: AndreasThinks/ministral-3b-nato-doctrine\n")
+
+trainer.train()
+
+# Save final model
+print("\n✓ Training complete! Saving final model...")
+trainer.push_to_hub()
+
+print("\n✅ Fine-tuning complete!")
+print(f"  Model: https://huggingface.co/AndreasThinks/ministral-3b-nato-doctrine")
+print(f"  Trackio: Check your dashboard for metrics")