LLM Fine-Tuning Guide

Master the art of fine-tuning large language models to create specialized models optimized for your specific use cases, domains, and performance requirements.

Overview

Fine-tuning adapts pre-trained LLMs to specific tasks, domains, or styles by training them on curated datasets. This improves accuracy, reduces hallucinations, and optimizes costs.

When to Fine-Tune

Domain Specialization: Legal documents, medical records, financial reports
Task-Specific Performance: Better results on specific tasks than base model
Cost Optimization: Smaller fine-tuned model replaces expensive large model
Style Adaptation: Match specific writing styles or tones
Compliance Requirements: Keep sensitive data within your infrastructure
Latency Requirements: Smaller models deploy faster

When NOT to Fine-Tune

One-off queries (use prompting instead)
Rapidly changing information (use RAG instead)
Limited training data (< 100 examples typically insufficient)
General knowledge questions (base model sufficient)

Quick Start

Full Fine-Tuning:

python examples/full_fine_tuning.py

LoRA (Recommended for most cases):

python examples/lora_fine_tuning.py

QLoRA (Single GPU):

python examples/qlora_fine_tuning.py

Data Preparation:

python scripts/data_preparation.py

Fine-Tuning Approaches

1. Full Fine-Tuning

Update all model parameters during training.

Pros:

Maximum performance improvement
Can completely rewrite model behavior
Best for significant domain shifts

Cons:

High computational cost
Requires large dataset (1000+ examples)
Risk of catastrophic forgetting
Long training time

from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer

model_id = "meta-llama/Llama-2-7b"
model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)

training_args = TrainingArguments(
    output_dir="./fine-tuned-llama",
    num_train_epochs=3,
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    learning_rate=2e-5,
    weight_decay=0.01,
    logging_steps=10,
    save_steps=100,
    eval_strategy="steps",
    eval_steps=50,
    load_best_model_at_end=True,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
)

trainer.train()

2. Parameter-Efficient Fine-Tuning (PEFT)

Train only a small fraction of parameters.

LoRA (Low-Rank Adaptation)

Adds trainable low-rank matrices to existing weights.

Pros:

99% fewer trainable parameters
Maintains base model knowledge
Fast training (10-20x faster)
Easy to switch between adapters

Cons:

Slightly lower performance than full fine-tuning
Requires base model at inference

from peft import get_peft_model, LoraConfig, TaskType
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model_id = "meta-llama/Llama-2-7b"
model = AutoModelForCausalLM.from_pretrained(base_model_id)
tokenizer = AutoTokenizer.from_pretrained(base_model_id)

# Configure LoRA
lora_config = LoraConfig(
    r=8,  # Rank of low-rank matrices
    lora_alpha=16,  # Scaling factor
    target_modules=["q_proj", "v_proj"],  # Which layers to adapt
    lora_dropout=0.05,
    bias="none",
    task_type=TaskType.CAUSAL_LM
)

# Wrap model with LoRA
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()
# Output: trainable params: 4,194,304 || all params: 6,738,415,616 || trainable%: 0.06

# Train as normal
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
)
trainer.train()

# Save only LoRA weights
model.save_pretrained("./llama-lora-adapter")

QLoRA (Quantized LoRA)

Combines LoRA with quantization for extreme efficiency.

from peft import prepare_model_for_kbit_training, get_peft_model, LoraConfig
from transformers import AutoModelForCausalLM, BitsAndBytesConfig

# Quantization config
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="float16",
    bnb_4bit_use_double_quant=True
)

# Load quantized model
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-7b",
    quantization_config=bnb_config,
    device_map="auto"
)

# Prepare for training
model = prepare_model_for_kbit_training(model)

# Apply LoRA
lora_config = LoraConfig(
    r=8,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type=TaskType.CAUSAL_LM
)

model = get_peft_model(model, lora_config)

# Train on single GPU
trainer = Trainer(
    model=model,
    args=TrainingArguments(
        output_dir="./qlora-output",
        per_device_train_batch_size=1,
        gradient_accumulation_steps=4,
        learning_rate=5e-4,
        num_train_epochs=3,
    ),
    train_dataset=train_dataset,
)
trainer.train()

Prefix Tuning

Prepends trainable tokens to input.

from peft import get_peft_model, PrefixTuningConfig

config = PrefixTuningConfig(
    num_virtual_tokens=20,
    task_type=TaskType.CAUSAL_LM,
)

model = get_peft_model(model, config)
# Only 20 * embedding_dim parameters trained

3. Instruction Fine-Tuning

Train model to follow instructions with examples.

# Training data format
training_data = [
    {
        "instruction": "Translate to French",
        "input": "Hello, how are you?",
        "output": "Bonjour, comment allez-vous?"
    },
    {
        "instruction": "Summarize this text",
        "input": "Long document...",
        "output": "Summary..."
    }
]

# Template for training
template = """Below is an instruction that describes a task, paired with an input that provides further context.

### Instruction:
{instruction}

### Input:
{input}

### Response:
{output}"""

# Create formatted dataset
formatted_data = [
    template.format(**example) for example in training_data
]

4. Domain-Specific Fine-Tuning

Tailor models for specific industries or fields.

Legal Domain Example

legal_training_data = [
    {
        "prompt": "What are the key clauses in an NDA?",
        "completion": """Key clauses typically include:
1. Definition of Confidential Information
2. Non-Disclosure Obligations
3. Permitted Disclosures
4. Term and Termination
5. Return of Information
6. Remedies"""
    },
    # ... more legal examples
]

# Train on legal domain
model = fine_tune_on_domain(
    base_model="gpt-3.5-turbo",
    training_data=legal_training_data,
    epochs=3,
    learning_rate=0.0002,
)

Data Preparation

1. Dataset Quality

class DatasetValidator:
    def validate_dataset(self, data):
        issues = {
            "empty_samples": 0,
            "duplicates": 0,
            "outliers": 0,
            "imbalance": {}
        }

        # Check for empty samples
        for sample in data:
            if not sample.get("text"):
                issues["empty_samples"] += 1

        # Check for duplicates
        texts = [s.get("text") for s in data]
        issues["duplicates"] = len(texts) - len(set(texts))

        # Check for length outliers
        lengths = [len(t.split()) for t in texts]
        mean_length = sum(lengths) / len(lengths)
        issues["outliers"] = sum(1 for l in lengths if l > mean_length * 3)

        return issues

# Validate before training
validator = DatasetValidator()
issues = validator.validate_dataset(training_data)
print(f"Dataset Issues: {issues}")

2. Data Augmentation

from nlpaug.augmenter.word import SynonymAug, RandomWordAug
import nlpaug.flow as naf

# Create augmentation pipeline
text = "The quick brown fox jumps over the lazy dog"

# Synonym replacement
aug_syn = SynonymAug(aug_p=0.3)
augmented_syn = aug_syn.augment(text)

# Random word insertion
aug_insert = RandomWordAug(action="insert", aug_p=0.3)
augmented_insert = aug_insert.augment(text)

# Combine augmentations
flow = naf.Sequential([
    SynonymAug(aug_p=0.2),
    RandomWordAug(action="swap", aug_p=0.2)
])
augmented = flow.augment(text)

3. Train/Validation Split

from sklearn.model_selection import train_test_split

# Create splits
train_data, eval_data = train_test_split(
    data,
    test_size=0.2,
    random_state=42
)

eval_data, test_data = train_test_split(
    eval_data,
    test_size=0.5,
    random_state=42
)

print(f"Train: {len(train_data)}, Eval: {len(eval_data)}, Test: {len(test_data)}")

Training Techniques

1. Learning Rate Scheduling

from torch.optim.lr_scheduler import CosineAnnealingLR, LinearLR

# Linear warmup + cosine annealing
def get_scheduler(optimizer, num_steps):
    lr_scheduler = get_linear_schedule_with_warmup(
        optimizer,
        num_warmup_steps=500,
        num_training_steps=num_steps
    )
    return lr_scheduler

training_args = TrainingArguments(
    learning_rate=1e-4,
    lr_scheduler_type="cosine",
    warmup_steps=500,
    warmup_ratio=0.1,
)

2. Gradient Accumulation

training_args = TrainingArguments(
    gradient_accumulation_steps=4,  # Accumulate gradients over 4 steps
    per_device_train_batch_size=1,   # Effective batch size: 1 * 4 = 4
)

# Simulates larger batch on limited GPU memory

3. Mixed Precision Training

training_args = TrainingArguments(
    fp16=True,  # Use 16-bit floats
    bf16=False,
)

# Reduces memory usage by 50%, speeds up training

4. Multi-GPU Training

training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    gradient_accumulation_steps=4,
    dataloader_pin_memory=True,
    dataloader_num_workers=4,
)

# Automatically uses all available GPUs
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
)

Popular Models for Fine-Tuning

Open Source Models

Llama 3.2 (Meta)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-7b")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-7b")

# Fine-tune on custom data
# ... training code

Characteristics:

7B, 70B parameter versions
Strong instruction-following
Excellent for domain adaptation
Apache 2.0 license

Gemma 3 (Google)

model = AutoModelForCausalLM.from_pretrained("google/gemma-3-2b")
tokenizer = AutoTokenizer.from_pretrained("google/gemma-3-2b")

# Gemma 3 sizes: 2B, 7B, 27B
# Very efficient, great for fine-tuning

Characteristics:

Small, medium, large sizes
Efficient architecture
Good for edge deployment
Built on cutting-edge research

Mistral 7B

model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")

# Strong performance, efficient architecture

Characteristics:

Sliding window attention
Efficient inference
Strong performance-to-size ratio

Commercial Models

OpenAI Fine-Tuning API

import openai

# Prepare training data
training_file = openai.File.create(
    file=open("training_data.jsonl", "rb"),
    purpose="fine-tune"
)

# Create fine-tuning job
fine_tune_job = openai.FineTuningJob.create(
    training_file=training_file.id,
    model="gpt-3.5-turbo",
    hyperparameters={
        "n_epochs": 3,
        "learning_rate_multiplier": 0.1,
    }
)

# Wait for completion
fine_tuned_model = openai.FineTuningJob.retrieve(fine_tune_job.id)
print(f"Status: {fine_tuned_model.status}")

# Use fine-tuned model
response = openai.ChatCompletion.create(
    model=fine_tuned_model.fine_tuned_model,
    messages=[{"role": "user", "content": "Hello"}]
)

Evaluation and Metrics

1. Perplexity

import torch
from math import exp

def calculate_perplexity(model, eval_dataset):
    model.eval()
    total_loss = 0
    total_tokens = 0

    with torch.no_grad():
        for batch in eval_dataset:
            outputs = model(**batch)
            loss = outputs.loss
            total_loss += loss.item() * batch["input_ids"].shape[0]
            total_tokens += batch["input_ids"].shape[0]

    perplexity = exp(total_loss / total_tokens)
    return perplexity

perplexity = calculate_perplexity(model, eval_dataset)
print(f"Perplexity: {perplexity:.2f}")

2. Task-Specific Metrics

from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score

def evaluate_task(predictions, ground_truth):
    return {
        "accuracy": accuracy_score(ground_truth, predictions),
        "precision": precision_score(ground_truth, predictions, average='weighted'),
        "recall": recall_score(ground_truth, predictions, average='weighted'),
        "f1": f1_score(ground_truth, predictions, average='weighted'),
    }

# Evaluate on task
predictions = [model.predict(x) for x in test_data]
metrics = evaluate_task(predictions, test_labels)
print(f"Metrics: {metrics}")

3. Human Evaluation

class HumanEvaluator:
    def evaluate_response(self, prompt, response):
        criteria = {
            "relevance": self._score_relevance(prompt, response),
            "coherence": self._score_coherence(response),
            "factuality": self._score_factuality(response),
            "helpfulness": self._score_helpfulness(response),
        }
        return sum(criteria.values()) / len(criteria)

    def _score_relevance(self, prompt, response):
        # Score 1-5
        pass

    def _score_coherence(self, response):
        # Score 1-5
        pass

Common Challenges & Solutions

Challenge: Catastrophic Forgetting

Model forgets pre-trained knowledge while adapting to new domain.

Solutions:

Use lower learning rates (2e-5 to 5e-5)
Smaller training epochs (1-3)
Regularization techniques
Continual learning approaches

# Conservative training settings
training_args = TrainingArguments(
    learning_rate=2e-5,  # Lower learning rate
    num_train_epochs=2,   # Few epochs
    weight_decay=0.01,    # L2 regularization
    warmup_steps=500,
    save_total_limit=3,
    load_best_model_at_end=True,
)

Challenge: Overfitting

Model performs well on training data but poorly on new data.

Solutions:

Use more training data
Implement dropout
Early stopping
Validation monitoring

training_args = TrainingArguments(
    eval_strategy="steps",
    eval_steps=50,
    load_best_model_at_end=True,
    early_stopping_patience=3,
    metric_for_best_model="eval_loss",
)

Challenge: Insufficient Training Data

Few examples for fine-tuning.

Solutions:

Data augmentation
Use PEFT (LoRA) instead of full fine-tuning
Few-shot learning with prompting
Transfer learning

# Use LoRA when data is limited
lora_config = LoraConfig(
    r=8,
    lora_alpha=16,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
)

Best Practices

Before Fine-Tuning

✓ Start with a strong base model
✓ Prepare high-quality training data (100+ examples recommended)
✓ Define clear evaluation metrics
✓ Set up proper train/validation splits
✓ Document your objectives

During Fine-Tuning

✓ Monitor training/validation loss
✓ Use appropriate learning rates
✓ Save checkpoints regularly
✓ Validate on held-out data
✓ Watch for overfitting/underfitting

After Fine-Tuning

✓ Evaluate on test set
✓ Compare against baseline
✓ Perform qualitative analysis
✓ Document configuration and results
✓ Version your fine-tuned models

Llm Fine Tuning Guide

LLM Fine-Tuning Guide

Overview

When to Fine-Tune

When NOT to Fine-Tune

Quick Start

Fine-Tuning Approaches

1. Full Fine-Tuning

2. Parameter-Efficient Fine-Tuning (PEFT)

LoRA (Low-Rank Adaptation)

QLoRA (Quantized LoRA)

Prefix Tuning

3. Instruction Fine-Tuning

4. Domain-Specific Fine-Tuning

Legal Domain Example

Data Preparation

1. Dataset Quality

2. Data Augmentation

3. Train/Validation Split

Training Techniques

1. Learning Rate Scheduling

2. Gradient Accumulation

3. Mixed Precision Training

4. Multi-GPU Training

Popular Models for Fine-Tuning

Open Source Models

Llama 3.2 (Meta)

Gemma 3 (Google)

Mistral 7B

Commercial Models

OpenAI Fine-Tuning API

Evaluation and Metrics

1. Perplexity

2. Task-Specific Metrics

3. Human Evaluation

Common Challenges & Solutions

Challenge: Catastrophic Forgetting

Challenge: Overfitting

Challenge: Insufficient Training Data

Best Practices

Before Fine-Tuning

During Fine-Tuning

After Fine-Tuning

Implementation Checklist

Resources

Frameworks

Models

Papers