Basic Customization

This tutorial will guide you through the fundamental customization techniques for adapting Large Language Models (LLMs) to better suit your specific needs and use cases.

Understanding Model Customization

Before diving into technical details, let’s understand the spectrum of customization options:

Prompt Engineering: Lightweight, no model changes
Few-shot Learning: Using examples in prompts
Fine-tuning: Updating model weights
Adapter Methods: Adding small trainable components
Retrieval Augmentation: Enhancing models with external knowledge

1. Prompt Engineering Basics

Prompt engineering is the art and science of crafting inputs to get desired outputs from LLMs.

Prompt Structure

Effective prompts typically include:

Clear instructions
Context/background information
Format specification
Examples (optional)
Constraints or requirements

Basic Prompt Templates

Question Answering:

Answer the following question accurately and concisely:
[QUESTION]

Content Generation:

Write a [CONTENT TYPE] about [TOPIC] in the style of [STYLE]. The [CONTENT TYPE] should include [REQUIREMENTS].

Classification:

Classify the following text into one of these categories: [CATEGORY LIST]

Text: [TEXT]

Prompt Optimization Techniques

Be Specific: Clearly define what you want
Provide Context: Give background information
Control Output Format: Specify how results should be structured
Use System Messages: Set the tone and role where supported
Chain of Thought: Ask the model to reason step-by-step

Example: Improving a Basic Prompt

Basic Prompt:

Summarize this article.

Improved Prompt:

Summarize the following article in 3-5 bullet points, focusing on the key findings and implications. Each bullet point should be 1-2 sentences long.

Article: [ARTICLE TEXT]

2. Few-Shot Learning

Few-shot learning involves providing examples within the prompt to demonstrate the desired pattern.

Example: Few-shot Classification

Classify the following customer feedback as Positive, Neutral, or Negative.

Example 1:
Feedback: "Your product completely solved my problem! I'm amazed at how well it works."
Classification: Positive

Example 2:
Feedback: "The product works as described but the setup process was confusing."
Classification: Neutral

Example 3:
Feedback: "This is the worst experience I've ever had. Nothing works as advertised."
Classification: Negative

Now classify this feedback:
Feedback: "[NEW FEEDBACK]"
Classification:

Guidelines for Effective Few-Shot Learning

Use Diverse Examples: Cover different cases and patterns
Match Example Format to Target: Use similar complexity and structure
Order Matters: Consider example sequence (simple to complex often works well)
Quality Over Quantity: 3-5 well-chosen examples often suffice

3. Basic Fine-tuning

Fine-tuning adapts a pre-trained model to specific domains or tasks by updating its weights with new training data.

When to Fine-tune

Consider fine-tuning when:

You need consistent outputs in a specialized domain
You have a specific task with available training data
Prompt engineering alone doesn’t achieve desired results
You need to reduce prompt length for efficiency

Preparing Your Dataset

A quality dataset is crucial for successful fine-tuning:

Format Your Data: Most frameworks use instruction-response pairs:

{
  "instruction": "Classify this news headline as business, sports, entertainment, or politics",
  "input": "Tesla Stock Soars After Earnings Report",
  "output": "business"
}

Dataset Size Guidelines:
- Small models: 100-1000+ examples
- Medium models: 500-5000+ examples
- Large models: 1000-10000+ examples
Data Quality Checks:
- Ensure diversity in examples
- Check for biases or problematic content
- Validate consistency in formatting
- Split into training/validation sets (80%/20%)

Fine-tuning with Hugging Face

from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
from datasets import load_dataset

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

# Prepare dataset
dataset = load_dataset("json", data_files="your_dataset.json")
dataset = dataset.map(lambda examples: tokenizer(examples["text"], truncation=True, padding="max_length"))

# Define training arguments
training_args = TrainingArguments(
    output_dir="./fine-tuned-model",
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    learning_rate=2e-5,
    num_train_epochs=3,
    save_steps=500,
)

# Initialize Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset["train"],
    eval_dataset=dataset["validation"],
)

# Start fine-tuning
trainer.train()

# Save the model
model.save_pretrained("./fine-tuned-model")
tokenizer.save_pretrained("./fine-tuned-model")

Parameter-Efficient Fine-tuning (PEFT)

For resource-constrained environments, consider PEFT methods:

from peft import get_peft_model, LoraConfig, TaskType

# Define LoRA configuration
peft_config = LoraConfig(
    task_type=TaskType.CAUSAL_LM,
    r=8,  # Rank
    lora_alpha=32,
    lora_dropout=0.1,
    target_modules=["q_proj", "v_proj"]
)

# Get PEFT model
peft_model = get_peft_model(model, peft_config)

# Continue with training as above, but using peft_model instead of model

4. Retrieval-Augmented Generation (RAG)

RAG enhances LLMs by retrieving relevant information from external sources to inform responses.

Basic RAG Implementation

from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains import RetrievalQA
from langchain.llms import HuggingFacePipeline

# Load documents
documents = TextLoader("your_knowledge_base.txt").load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
chunks = text_splitter.split_documents(documents)

# Create embeddings and vector store
embeddings = HuggingFaceEmbeddings()
vectorstore = FAISS.from_documents(chunks, embeddings)

# Set up retrieval chain
qa_chain = RetrievalQA.from_chain_type(
    llm=HuggingFacePipeline.from_model_id(
        model_id="your-fine-tuned-model",
        task="text-generation",
    ),
    chain_type="stuff",
    retriever=vectorstore.as_retriever(),
)

# Query the system
response = qa_chain.run("What is the capital of France?")
print(response)

Key Components of RAG

Document Processing: Chunking text into manageable pieces
Embedding Generation: Converting text chunks to vector representations
Vector Storage: Efficient storage and retrieval of embeddings
Similarity Search: Finding relevant content for a query
Response Generation: Synthesizing retrieved information into coherent answers

5. Customizing Output Formats

Control how your model structures its responses:

JSON Output

Generate a JSON object with information about the following person.
The JSON should have these fields: name, age, occupation, skills (array).

Person description: John is a 34-year-old software engineer who knows Python, JavaScript, and database design.

Markdown Formatting

Create a markdown-formatted product description with:
- A level 2 heading with the product name
- A paragraph describing the product
- A bulleted list of features
- A level 3 heading for "Technical Specifications"
- A table with specifications

Product: Wireless noise-cancelling headphones with 30-hour battery life, Bluetooth 5.0, and memory foam ear cushions.

Custom Templates

Complete the following template with appropriate information:

TITLE: [Generate an engaging title]

SUMMARY: [Write a 2-3 sentence summary]

KEY POINTS:
1. [First main point]
2. [Second main point]
3. [Third main point]

CONCLUSION: [Write a brief conclusion]

Topic: The impact of artificial intelligence on healthcare

6. Model Evaluation

Evaluate your customized model to ensure it meets your requirements:

Basic Evaluation Metrics

Accuracy: Correctness of responses
Relevance: Response alignment with intent
Consistency: Reliability across similar inputs
Safety: Avoiding harmful or inappropriate content

Evaluation Code Example

from datasets import load_dataset
import json
import numpy as np

# Load test dataset
test_data = load_dataset("json", data_files="test_samples.json")["train"]

results = []
for sample in test_data:
    prompt = sample["prompt"]
    expected = sample["expected_response"]
    
    # Get model response (implementation depends on your setup)
    response = get_model_response(prompt)
    
    # Simple exact match evaluation
    is_match = response.strip() == expected.strip()
    
    results.append({
        "prompt": prompt,
        "expected": expected,
        "response": response,
        "is_match": is_match
    })

# Calculate accuracy
accuracy = np.mean([r["is_match"] for r in results])
print(f"Accuracy: {accuracy:.2%}")

# Save detailed results
with open("evaluation_results.json", "w") as f:
    json.dump(results, f, indent=2)

Next Steps

Once you’ve mastered these basic customization techniques, you can:

Explore Advanced Features for more sophisticated customization
Learn about Ethical Considerations when customizing AI models
Review Performance Optimization Strategies to ensure your customized AI runs efficiently in real-world scenarios. Remember that effective customization is often iterative—start simple, evaluate results, and refine your approach based on feedback and performance metrics.

Basic Customization

Dr. Ruperto Pedro Bonet Chaple