Fine-Tuning AI Models on Cloud GPUs: PyTorch, LoRA, and QLoRA

Why Fine-Tune?

Pre-trained models are powerful but generic. Fine-tuning on your own data creates a model that understands your domain — your writing style, your product catalog, your medical terminology.

Choosing a GPU for Training

Step 1: Deploy a TurboGPU Instance

Choose the Power tier (A6000, 48 GB VRAM) for maximum flexibility. Connect via SSH:

ssh -p <port> user@<your-ip>

Step 2: Set Up the Environment

# Create a virtual environment
python -m venv llm-train
source llm-train/bin/activate

# Install dependencies
pip install torch transformers datasets peft accelerate bitsandbytes

Step 3: Prepare Your Dataset

from datasets import load_dataset

# Load your custom dataset (JSON format)
dataset = load_dataset("json", data_files="training_data.jsonl")

# Format: {"instruction": "...", "input": "...", "output": "..."}

Step 4: LoRA Training

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

model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Meta-Llama-3-8B",
    load_in_4bit=True,  # QLoRA: fits in 12 GB VRAM
)

lora_config = LoraConfig(
    r=16, lora_alpha=32,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
)

model = get_peft_model(model, lora_config)

trainer = SFTTrainer(
    model=model,
    train_dataset=dataset["train"],
    args=TrainingArguments(
        output_dir="./output",
        num_train_epochs=3,
        per_device_train_batch_size=4,
        learning_rate=2e-4,
    ),
)

trainer.train()
trainer.save_model("./my-custom-model")

Cost Breakdown

Training a LoRA adapter on Llama 3 8B with 10K examples:

Compare that to fine-tuning via OpenAI's API: $25+ for similar data volumes.

Tips

Start training →