Optionally use LoRA/PEFT to efficiently adapt a model

Fine-tuning a large language model (LLM) from scratch can be expensive, slow, and GPU-heavy. LoRA (Low-Rank Adaptation) and the PEFT library (Parameter-Efficient Fine-Tuning) solve this by letting you adapt big models with far fewer trainable parameters — while keeping performance high.

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✅ Why Use LoRA/PEFT?

• Save compute: You train only a small set of extra weights, not the whole model.

• Use less memory: Perfect for fine-tuning big models on small GPUs.

• Keep the base model frozen: Your custom tweaks sit on top.

• Reuse and share: Upload just your LoRA adapter — collaborators can reuse it with the same base model.

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✅ How It Works

🔹 LoRA adds tiny extra matrices into attention layers or other parts of the transformer. 🔹 These learn the task-specific changes while the core model stays the same. 🔹 At inference, the LoRA adapters adjust the outputs on the fly.

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✅ Hugging Face PEFT Library

Hugging Face’s peft library makes LoRA easy:

• Plug it into any transformers model.

• Works with Trainer or custom loops.

• Supports multiple PEFT techniques, not just LoRA.

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✅ Quick Example

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1️⃣ Install PEFT

pip install peft

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2️⃣ Load Your Base Model

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "bigscience/bloom-560m"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

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3️⃣ Add LoRA

from peft import LoraConfig, get_peft_model, TaskType

lora_config = LoraConfig(
    task_type=TaskType.CAUSAL_LM,   # Type of model
    r=8,                             # Low-rank dimension
    lora_alpha=16,                   # Scaling factor
    lora_dropout=0.05,
    target_modules=["query_key_value"],  # Model-specific layers to inject LoRA
)

model = get_peft_model(model, lora_config)

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4️⃣ Train Like Normal

Use transformers.Trainer or your favorite loop:

from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir="./lora_outputs",
    per_device_train_batch_size=2,
    num_train_epochs=3,
)

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

trainer.train()

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5️⃣ Save Only the LoRA Adapter

model.save_pretrained("./my_lora_adapter")

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6️⃣ Reload Later

Combine your frozen base + adapter for inference:

from peft import PeftModel

model = AutoModelForCausalLM.from_pretrained(model_name)
model = PeftModel.from_pretrained(model, "./my_lora_adapter")

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✅ When to Use This

✔️ Your GPU has limited VRAM. ✔️ You want to experiment fast. ✔️ You plan to release your adapters (instead of the full model). ✔️ You want to keep multiple task-specific adapters for the same base LLM.

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🗝️ Key Takeaway

LoRA + PEFT = smarter, faster, lighter fine-tuning. This is the standard trick for customizing large open LLMs without breaking your budget or your laptop.

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➡️ Next: You’ll learn how to add Retrieval-Augmented Generation (RAG) so your assistant can handle fresh, domain-specific info!