SFT Training Guide

Complete guide to Supervised Fine-Tuning (SFT) with TRL for instruction tuning and task-specific fine-tuning.

Overview

SFT trains models on input-output pairs to minimize cross-entropy loss. Use for:

Instruction following
Task-specific fine-tuning
Chatbot training
Domain adaptation

Dataset Formats

Format 1: Prompt-Completion

[
  {
    "prompt": "What is the capital of France?",
    "completion": "The capital of France is Paris."
  }
]

Format 2: Conversational (ChatML)

[
  {
    "messages": [
      {"role": "user", "content": "What is Python?"},
      {"role": "assistant", "content": "Python is a programming language."}
    ]
  }
]

Format 3: Text-only

[
  {"text": "User: Hello\nAssistant: Hi! How can I help?"}
]

Basic Training

from trl import SFTTrainer, SFTConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
from datasets import load_dataset

# Load model
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B")

# Load dataset
dataset = load_dataset("trl-lib/Capybara", split="train")

# Configure
config = SFTConfig(
    output_dir="Qwen2.5-SFT",
    per_device_train_batch_size=4,
    num_train_epochs=1,
    learning_rate=2e-5,
    save_strategy="epoch"
)

# Train
trainer = SFTTrainer(
    model=model,
    args=config,
    train_dataset=dataset,
    tokenizer=tokenizer
)
trainer.train()

Chat Templates

Apply chat templates automatically:

trainer = SFTTrainer(
    model=model,
    args=config,
    train_dataset=dataset,  # Messages format
    tokenizer=tokenizer
    # Chat template applied automatically
)

Or manually:

def format_chat(example):
    messages = example["messages"]
    text = tokenizer.apply_chat_template(messages, tokenize=False)
    return {"text": text}

dataset = dataset.map(format_chat)

Packing for Efficiency

Pack multiple sequences into one to maximize GPU utilization:

config = SFTConfig(
    packing=True,  # Enable packing
    max_seq_length=2048,
    dataset_text_field="text"
)

Benefits: 2-3× faster training Trade-off: Slightly more complex batching

Multi-GPU Training

accelerate launch --num_processes 4 train_sft.py

Or with config:

config = SFTConfig(
    output_dir="model-sft",
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    num_train_epochs=1
)

LoRA Fine-Tuning

from peft import LoraConfig

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules="all-linear",
    lora_dropout=0.05,
    task_type="CAUSAL_LM"
)

trainer = SFTTrainer(
    model=model,
    args=config,
    train_dataset=dataset,
    peft_config=lora_config  # Add LoRA
)

Hyperparameters

Model Size	Learning Rate	Batch Size	Epochs
<1B	5e-5	8-16	1-3
1-7B	2e-5	4-8	1-2
7-13B	1e-5	2-4	1
13B+	5e-6	1-2	1

References

TRL docs: https://huggingface.co/docs/trl/sft_trainer
Examples: https://github.com/huggingface/trl/tree/main/examples/scripts

3.2 KiB Raw Blame History Unescape Escape