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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", # For real-time monitoring
# ]
# ///
"""
Production-ready SFT training example with all best practices.
This script demonstrates:
- Trackio integration for real-time monitoring
- LoRA/PEFT for efficient training
- Proper Hub saving configuration
- Train/eval split for monitoring
- Checkpoint management
- Optimized training parameters
Usage with hf_jobs MCP tool:
hf_jobs("uv", {
"script": '''<paste this entire file>''',
"flavor": "a10g-large",
"timeout": "3h",
"secrets": {"HF_TOKEN": "$HF_TOKEN"},
})
Or submit the script content directly inline without saving to a file.
"""
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")
# Note: For memory-constrained demos, skip eval by using full dataset as train_dataset
# and removing eval_dataset, eval_strategy, and eval_steps from config below
# Training configuration
config = SFTConfig(
# CRITICAL: Hub settings
output_dir="qwen-capybara-sft",
push_to_hub=True,
hub_model_id="username/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,
# max_length=1024, # Default - only set if you need different sequence length
# Logging & checkpointing
logging_steps=10,
save_strategy="steps",
save_steps=100,
save_total_limit=2,
# Evaluation - IMPORTANT: Only enable if eval_dataset provided
eval_strategy="steps",
eval_steps=100,
# Optimization
warmup_ratio=0.1,
lr_scheduler_type="cosine",
# Monitoring
report_to="trackio", # Integrate with Trackio
project="meaningful_project_name", # project name for the training name (trackio)
run_name="baseline-run", #Descriptive name for this training run
# LoRA configuration
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 and train
print("🎯 Initializing trainer...")
trainer = SFTTrainer(
model="Qwen/Qwen2.5-0.5B",
train_dataset=train_dataset,
eval_dataset=eval_dataset, # CRITICAL: Must provide eval_dataset when eval_strategy is enabled
args=config,
peft_config=peft_config,
)
print("🚀 Starting training...")
trainer.train()
print("💾 Pushing to Hub...")
trainer.push_to_hub()
# Finish Trackio tracking
print("✅ Complete! Model at: https://huggingface.co/username/qwen-capybara-sft")
print("📊 View metrics at: https://huggingface.co/spaces/username/trackio") |