Fine-Tuning Open-Source LLMs with Unsloth and Llama 3.3 on Consumer GPUs in Python (2026 Tutorial)

Making Enterprise LLM Customization 5x Faster with 80% Less VRAM

Fine-tuning modern Large Language Models traditionally required multi-GPU server clusters costing thousands of dollars per training run. Unsloth rewrites PyTorch attention kernels in native Triton language, accelerating LoRA / QLoRA fine-tuning by up to 5x while slashing VRAM requirements by 80%, enabling full 16-bit LoRA training on affordable single-GPU instances.

Documented by Saizul Amin for AI engineers and machine learning practitioners.

Fine-Tuning Script with Unsloth and HuggingFace

from unsloth import FastLanguageModel
import torch
from trl import SFTTrainer
from transformers import TrainingArguments

max_seq_length = 2048
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/Llama-3.2-3B-Instruct",
    max_seq_length=max_seq_length,
    load_in_4bit=True
)

# Attach LoRA Adapters
model = FastLanguageModel.get_peft_model(
    model,
    r=16,
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
    lora_alpha=16,
    lora_dropout=0,
    bias="none"
)

# Execute SFT Training
trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=dataset,
    dataset_text_field="text",
    max_seq_length=max_seq_length,
    args=TrainingArguments(
        per_device_train_batch_size=2,
        gradient_accumulation_steps=4,
        warmup_steps=5,
        max_steps=60,
        learning_rate=2e-4,
        fp16=not torch.cuda.is_bf16_supported(),
        bf16=torch.cuda.is_bf16_supported(),
        logging_steps=1,
        output_dir="outputs"
    )
)
trainer.train()