Part 4: Fine-Tuning on Your Writing

Time to teach AI to write like you. This is the coolest part.

What is Fine-Tuning?

Take a pre-trained model (knows English)

• Your writing (knows your style)
  = Model that writes like you

Think: Training a parrot to mimic your voice.

LoRA: Fine-Tuning for Poor People

Problem: Fine-tuning usually needs $10K of GPUs.

Solution: LoRA (Low-Rank Adaptation) - Runs on your laptop's GPU.

Instead of updating all 7 billion parameters, LoRA updates tiny adapter layers.

Result: 100x less memory, 10x faster, same quality.

The Code (Runs on RTX 3060!)

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

# Load base model (Phi-2, small but powerful)
model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2")
tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2")

# Configure LoRA
lora_config = LoraConfig(
    task_type=TaskType.CAUSAL_LM,
    r=16,  # Rank
    lora_alpha=32,
    lora_dropout=0.1
)

# Wrap model with LoRA
model = get_peft_model(model, lora_config)

# Your writing as training data
dataset = load_dataset('json', data_files='data/processed/train.json')

# Train!
from transformers import Trainer, TrainingArguments

trainer = Trainer(
    model=model,
    args=TrainingArguments(
        output_dir='./results',
        num_train_epochs=3,
        per_device_train_batch_size=4,
        save_steps=100,
    ),
    train_dataset=dataset['train']
)

trainer.train()
model.save_pretrained('./my-llm-twin')

Training time: 2-4 hours on RTX 3060

Test Your Twin

from transformers import pipeline

twin = pipeline('text-generation', model='./my-llm-twin')

prompt = "My thoughts on AI in software development:"
output = twin(prompt, max_length=200)

print(output[0]['generated_text'])

Does it sound like you?

Next Week

Part 5: Build a FastAPI service. Use your twin from anywhere.

Series Progress: Part 4 of 6 ✓ Next: Part 5 - API Development (Aug 17)