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# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "trackio", "transformers>=4.36.0", "datasets>=2.16.0"]
# ///
from datasets import load_dataset
from peft import LoraConfig
from trl import SFTTrainer, SFTConfig
import trackio
# Load dataset
dataset = load_dataset("machina-sports/sportingbot-classification", split="train")
# Create train/eval split (10% eval)
dataset_split = dataset.train_test_split(test_size=0.1, seed=42)
print(f"✅ Dataset loaded: {len(dataset_split['train'])} train, {len(dataset_split['test'])} eval")
# Configure LoRA
peft_config = LoraConfig(
r=32,
lora_alpha=64,
target_modules="all-linear",
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM"
)
# Configure training
training_args = SFTConfig(
output_dir="sportingbot-gemma-classifier",
# Hub settings (CRITICAL - saves results)
push_to_hub=True,
hub_model_id="fernando-machina/sportingbot-gemma-classifier",
hub_strategy="every_save",
hub_private_repo=False,
# Training hyperparameters (from user's config)
num_train_epochs=5,
per_device_train_batch_size=2,
gradient_accumulation_steps=4,
learning_rate=0.0001,
# Optimization (bf16 for Gemma)
bf16=True,
gradient_checkpointing=True,
# Evaluation
eval_strategy="steps",
eval_steps=10,
# Checkpointing
save_strategy="steps",
save_steps=50,
save_total_limit=3,
# Logging
logging_steps=5,
report_to="trackio",
# Trackio monitoring
project="sportingbot-classification",
run_name="gemma-2-2b-it-v1",
# Sequence length
max_length=512,
)
print("🚀 Starting training with Gemma 2-2B-it...")
# Create trainer
trainer = SFTTrainer(
model="google/gemma-2-2b-it",
train_dataset=dataset_split["train"],
eval_dataset=dataset_split["test"],
peft_config=peft_config,
args=training_args,
)
# Train
trainer.train()
print("✅ Training complete! Pushing to Hub...")
# Push final model
trainer.push_to_hub()
print(f"🎉 Model saved to: https://huggingface.co/fernando-machina/sportingbot-gemma-classifier")
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