Part 3: Creating AIChapter 9 of 12

Fine-tuning - Making AI Your Personal Assistant

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Fine-tuning is like getting a suit tailored. You start with a quality off-the-rack suit (pre-trained model), then customize it to fit you perfectly (specialized knowledge).

👔The Tailored Suit Analogy

Off-the-rack Suit (Pre-trained Model)

  • •Fits most people okay
  • •Generic style
  • •Good quality
  • •Ready to wear

Tailored Suit (Fine-tuned Model)

  • ✓Fits you perfectly
  • ✓Your style preferences
  • ✓Same quality base
  • ✓Customized details

Real Examples of Fine-tuning Success

🏥

Example 1: Medical Diagnosis Assistant

Base Model: GPT-3.5
Training Data: 50,000 radiology reports
Training Time: 8 hours
Result:
94% accuracy in identifying abnormalities
(Compared to 76% before fine-tuning)
⚖️

Example 2: Legal Document Analyzer

Base Model: LLaMA-2-7B
Training Data: 100,000 legal contracts
Training Time: 12 hours
Result:
Identifies risky clauses that lawyers sometimes miss
💬

Example 3: Customer Service Bot

Base Model: Mistral-7B
Training Data: Company's 10,000 support tickets
Training Time: 3 hours
Result:
Resolves 70% of queries without human help

The Fine-tuning Process: Step by Step

Step 1: Choose Your Base Model

Like choosing a car to modify:

🚗Small models (1-3B): Honda Civic
  • • Efficient, reliable
  • • Great for simple tasks
  • • Easy to modify
🚙Medium models (7-13B): BMW 3-Series
  • • Balanced performance
  • • Versatile
  • • Most popular choice
🏎️Large models (30B+): Ferrari
  • • Maximum performance
  • • Expensive to run
  • • For specialized needs

Step 2: Prepare Your Training Data

My actual example from the PostgreSQL project:

{
  "conversations": [
    {
      "from": "system",
      "value": "You are a PostgreSQL expert."
    },
    {
      "from": "human",
      "value": "How do I handle connection pooling?"
    },
    {
      "from": "assistant",
      "value": "For PostgreSQL connection pooling, you have several options:

1. **PgBouncer** (most popular):
   - Lightweight and efficient
   - Three pool modes: session, transaction, statement
   - Configuration example:
   ```
   [databases]
   mydb = host=localhost dbname=mydb
   [pgbouncer]
   pool_mode = transaction
   max_client_conn = 100
   ```

2. **Built-in connection pooling** (PostgreSQL 14+):
   - Simpler setup
   - Less feature-rich than PgBouncer

3. **Application-level** (like HikariCP for Java):
   - More control
   - Language-specific

For most cases, PgBouncer in transaction mode offers the best balance."
    }
  ]
}

Step 3: The Actual Training

Using Google Colab (Free):

# This is real code that works!

# 1. Install necessary libraries
!pip install transformers datasets accelerate peft

# 2. Load the base model
from transformers import AutoModelForCausalLM, AutoTokenizer

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

# 3. Load your data
from datasets import load_dataset
dataset = load_dataset("json", data_files="your_training_data.json")

# 4. Configure training (LoRA for efficiency)
from peft import LoraConfig, get_peft_model

lora_config = LoraConfig(
    r=16,  # Rank - higher = more capacity
    lora_alpha=32,  # Scaling parameter
    target_modules=["q_proj", "v_proj"],  # What to train
    lora_dropout=0.1  # Prevent overfitting
)

model = get_peft_model(model, lora_config)

# 5. Train!
from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir="./my-fine-tuned-model",
    num_train_epochs=3,
    per_device_train_batch_size=4,
    learning_rate=2e-4,
    logging_steps=10
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset["train"],
    tokenizer=tokenizer
)

trainer.train()

# 6. Save your specialized model
trainer.save_model("my-assistant")

LoRA: The Secret to Affordable Fine-tuning

LoRA (Low-Rank Adaptation) is like teaching with sticky notes:

Traditional Fine-tuning

  • • Rewrite entire textbook
  • • Expensive, time-consuming
  • • Need lots of storage

LoRA Fine-tuning

  • • Add sticky notes to textbook
  • • Quick, cheap
  • • Original book stays intact
  • • Can remove/swap sticky notes

Real Numbers:

Full Fine-tuning of 7B model

  • Memory needed: 28GB
  • Storage: 13GB
  • Training time: 20 hours

LoRA Fine-tuning of 7B model

  • Memory needed: 8GB
  • Storage: 20MB (just the sticky notes!)
  • Training time: 2 hours

My Results: Before and After Fine-tuning

Before Fine-tuning (Generic LLaMA-7B)

Question:
"How to optimize slow PostgreSQL query?"
Answer:
"To optimize a slow database query, you can try adding indexes, checking your database settings, and making sure your hardware is adequate."
(Generic, not very helpful)

After Fine-tuning (My PostgreSQL model)

Question:
"How to optimize slow PostgreSQL query?"
Answer:
"For PostgreSQL query optimization:

1. Run EXPLAIN ANALYZE to see the actual execution plan
2. Look for Sequential Scans on large tables - add indexes:
   CREATE INDEX CONCURRENTLY idx_name ON table(column);
3. Check statistics are current: ANALYZE table_name;
4. For complex queries, consider:
   - Partial indexes for WHERE conditions
   - Covering indexes for index-only scans
   - Adjusting work_mem for sorts/hashes
5. Monitor with pg_stat_statements to find problem queries"
(Specific, actionable, expert-level)

Fine-tuning for Different Purposes

Style Transfer

Training data: Shakespeare's works
Result: AI writes in Shakespearean style
"Shall I compare thee to a summer's day?"

Domain Expertise

Training data: Medical journals
Result: AI understands medical terminology
"Differential diagnosis includes pneumonia, bronchitis..."

Company Voice

Training data: Company's marketing materials
Result: AI writes in brand voice
"At TechCorp, we innovate to empower..."

Language/Dialect

Training data: Regional dialect texts
Result: AI understands/generates local speech
"Y'all fixin' to head to the store?"

Common Fine-tuning Mistakes

Mistake 1: Too Little Data

Problem: Training with 10 examples
Result: AI memorizes, doesn't generalize
Solution: Minimum 100-1000 examples

Mistake 2: Inconsistent Format

Problem: Mixed formats in training data
Result: Confused model, poor performance
Solution: Standardize all training examples

Mistake 3: Overfitting to Specific Examples

Problem: Training too long on small dataset
Result: AI repeats training examples verbatim
Solution: Use validation set, early stopping
🎯

DIY Fine-tuning Project

Build Your Own Writing Assistant

1. Pick Your Style:

  • • Professional emails
  • • Creative stories
  • • Technical documentation
  • • Social media posts

2. Collect 100 Examples:

  • • Best examples of that style
  • • Diverse topics
  • • Consistent quality

3. Format Your Data:

{ "input": "Write about coffee", "output": "Your style here: Coffee, the morning elixir..." }

4. Use Free Tools:

  • • Google Colab for training
  • • Hugging Face for hosting
  • • Gradio for web interface
Total Cost: $0-10
Time: Weekend project
Result: Your personal AI assistant!

🎓 Key Takeaways

  • ✓Fine-tuning is like tailoring - customize a pre-trained model for your specific needs
  • ✓LoRA makes it affordable - 8GB RAM, 20MB storage, 2 hours instead of 28GB/13GB/20 hours
  • ✓Real examples prove it works - 94% accuracy for medical diagnosis, 70% query resolution for customer service
  • ✓Quality over quantity - 100-1000 good examples beat 10 poor ones
  • ✓You can do this - weekend project, $0-10 cost, build your own AI assistant
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