Fine-Tuning

Adapt LLMs to specific tasks and domains efficiently.

Quick Start

LoRA Fine-Tuning with PEFT

from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments
from peft import LoraConfig, get_peft_model, TaskType
from datasets import load_dataset
from trl import SFTTrainer

# Load base model
model_name = "meta-llama/Llama-2-7b-hf"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_token

# Configure LoRA
lora_config = LoraConfig(
    r=16,                          # Rank
    lora_alpha=32,                 # Alpha scaling
    target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type=TaskType.CAUSAL_LM
)

# Apply LoRA
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()
# trainable params: 4,194,304 || all params: 6,742,609,920 || trainable%: 0.06%

# Training arguments
training_args = TrainingArguments(
    output_dir="./output",
    num_train_epochs=3,
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    learning_rate=2e-4,
    warmup_ratio=0.03,
    logging_steps=10,
    save_strategy="epoch"
)

# Train
trainer = SFTTrainer(
    model=model,
    args=training_args,
    train_dataset=dataset,
    tokenizer=tokenizer,
    max_seq_length=512
)
trainer.train()

QLoRA (4-bit Quantized LoRA)

from transformers import BitsAndBytesConfig
import torch

# Quantization config
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True
)

# Load quantized model
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    device_map="auto"
)

# Apply LoRA on top of quantized model
model = get_peft_model(model, lora_config)

Dataset Preparation

Instruction Dataset Format

# Alpaca format
instruction_format = {
    "instruction": "Summarize the following text.",
    "input": "The quick brown fox jumps over the lazy dog...",
    "output": "A fox jumps over a dog."
}

# ChatML format
chat_format = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Summarize this text: ..."},
    {"role": "assistant", "content": "Summary: ..."}
]

# Formatting function
def format_instruction(sample):
    return f"""### Instruction:
{sample['instruction']}

### Input:
{sample['input']}

### Response:
{sample['output']}"""

Data Preparation Pipeline

from datasets import Dataset
import json

class DatasetPreparer:
    def __init__(self, tokenizer, max_length=512):
        self.tokenizer = tokenizer
        self.max_length = max_length

    def prepare(self, data_path: str) -> Dataset:
        # Load raw data
        with open(data_path) as f:
            raw_data = json.load(f)

        # Format samples
        formatted = [self._format_sample(s) for s in raw_data]

        # Create dataset
        dataset = Dataset.from_dict({"text": formatted})

        # Tokenize
        return dataset.map(
            self._tokenize,
            batched=True,
            remove_columns=["text"]
        )

    def _format_sample(self, sample):
        return f"""<s>[INST] {sample['instruction']}

{sample['input']} [/INST] {sample['output']}</s>"""

    def _tokenize(self, examples):
        return self.tokenizer(
            examples["text"],
            truncation=True,
            max_length=self.max_length,
            padding="max_length"
        )

Fine-Tuning Methods Comparison

Method	VRAM	Speed	Quality	Use Case
Full Fine-Tune	60GB+	Slow	Best	Unlimited resources
LoRA	16GB	Fast	Very Good	Most applications
QLoRA	8GB	Medium	Good	Consumer GPUs
Prefix Tuning	8GB	Fast	Good	Fixed tasks
Prompt Tuning	4GB	Very Fast	Moderate	Simple adaptation

LoRA Hyperparameters

rank (r):
  small (4-8): Simple tasks, less capacity
  medium (16-32): General fine-tuning
  large (64-128): Complex domain adaptation

alpha:
  rule: Usually 2x rank
  effect: Higher = more influence from LoRA weights

target_modules:
  attention: [q_proj, k_proj, v_proj, o_proj]
  mlp: [gate_proj, up_proj, down_proj]
  all: Maximum adaptation, more VRAM

dropout:
  typical: 0.05-0.1
  effect: Regularization, prevents overfitting

Training Best Practices

Learning Rate Schedule

from transformers import get_cosine_schedule_with_warmup

optimizer = torch.optim.AdamW(model.parameters(), lr=2e-4)
scheduler = get_cosine_schedule_with_warmup(
    optimizer,
    num_warmup_steps=100,
    num_training_steps=total_steps
)

Gradient Checkpointing

# Save memory by recomputing activations
model.gradient_checkpointing_enable()

# Also enable for LoRA
model.enable_input_require_grads()

Evaluation During Training

def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    # Shift for causal LM
    predictions = predictions[:, :-1]
    labels = labels[:, 1:]

    # Calculate perplexity
    loss_fct = torch.nn.CrossEntropyLoss(reduction='mean')
    loss = loss_fct(predictions.view(-1, vocab_size), labels.view(-1))
    perplexity = torch.exp(loss)

    return {"perplexity": perplexity.item()}

Merging and Deploying

Merge LoRA Weights

# After training, merge LoRA into base model
merged_model = model.merge_and_unload()

# Save merged model
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

Multiple LoRA Adapters

from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained("base_model")

# Load and switch between adapters
model = PeftModel.from_pretrained(base_model, "adapter_1")
model.load_adapter("adapter_2", adapter_name="code")

# Switch adapters at runtime
model.set_adapter("code")  # Use code adapter
model.set_adapter("default")  # Use default adapter

Common Issues

Issue	Cause	Solution
Loss not decreasing	LR too low/high	Adjust learning rate
OOM errors	Batch too large	Reduce batch, use gradient accumulation
Overfitting	Too many epochs	Early stopping, more data
Catastrophic forgetting	Too aggressive LR	Lower LR, shorter training
Poor quality	Data issues	Clean and validate dataset

Error Handling & Recovery

from transformers import TrainerCallback

class CheckpointCallback(TrainerCallback):
    def on_save(self, args, state, control, **kwargs):
        # Always keep last 3 checkpoints
        pass

    def on_epoch_end(self, args, state, control, **kwargs):
        if state.best_metric is None:
            # Save checkpoint on each epoch
            control.should_save = True

Troubleshooting

Symptom	Cause	Solution
NaN loss	LR too high	Lower to 1e-5
No improvement	LR too low	Increase 10x
OOM mid-training	Batch too large	Enable gradient checkpointing

Unit Test Template

def test_lora_config():
    config = LoraConfig(r=16, lora_alpha=32)
    model = get_peft_model(base_model, config)
    assert model.print_trainable_parameters() < 1%