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[ag-mout]

smol-course/units/en/unit1/3.md

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	Ready to put theory into practice? Here's a preview of what you'll build in the exercises. You can use either Python or CLI approach:
	<frameworkcontent>
	<pythontab>
	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	from trl import SFTTrainer, SFTConfig
	from datasets import load_dataset
	import trackio as wandb
	# Initialize experiment tracking
	wandb.init(project="smollm3-sft", name="my-first-sft-run")
	# Load SmolLM3 base model
	model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM3-3B-Base")
	tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM3-3B-Base")
	# Load SmolTalk2 dataset
	dataset = load_dataset("HuggingFaceTB/smoltalk2", "SFT")
	# Configure training with Trackio integration
	config = SFTConfig(
	output_dir="./smollm3-finetuned",
	per_device_train_batch_size=4,
	learning_rate=5e-5,
	max_steps=1000,
	report_to="trackio", # Enable Trackio logging
	)
	# Train!
	trainer = SFTTrainer(
	model=model,
	tokenizer=tokenizer,
	train_dataset=dataset["train"],
	args=config,
	)
	trainer.train()
	```
	</pythontab>
	<clitab>
	```bash
	# Fine-tune SmolLM3 using TRL CLI with Trackio tracking
	trl sft \
	--model_name_or_path HuggingFaceTB/SmolLM3-3B-Base \
	--dataset_name HuggingFaceTB/smoltalk2 \
	--dataset_config SFT \
	--output_dir ./smollm3-sft-model \
	--per_device_train_batch_size 4 \
	--learning_rate 5e-5 \
	--max_steps 1000 \
	--logging_steps 50 \
	--save_steps 200 \
	--report_to trackio \
	--push_to_hub \
	--hub_model_id your-username/smollm3-custom
	```
	</clitab>
	</frameworkcontent>
	## Severless Training Options

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