Fine-Tuning Gemma 3n with LoRA for Custom Tasks

Gemma 3n’s base models are incredibly powerful, but its true potential is unlocked when you fine-tune it for your specific domain or task. Whether you want to create a chatbot that understands your company’s jargon or a code assistant that knows your proprietary libraries, fine-tuning is the key.

In this guide, we’ll explore one of the most popular and efficient methods for this: Low-Rank Adaptation (LoRA).

What is LoRA and Why Use It?

Traditional fine-tuning requires updating all the weights of a massive model, which is computationally expensive and memory-intensive. It’s like re-training the entire model from scratch, but with your new data.

LoRA takes a smarter approach. Instead of changing the original weights, it “freezes” them and injects smaller, trainable “rank decomposition matrices” into the layers of the Transformer architecture.

The key benefits are:

Drastically Reduced Memory: You only need to train and store the small adapter matrices, not a full copy of the model for each task.
Faster Training: Fewer parameters to update means training completes much faster.
Easy Switching: You can easily swap LoRA adapters on the fly to change the model’s specialized skill.

Step 1: Setting Up Your Environment

First, let’s get our environment ready. You’ll need Python and a package manager like pip. We’ll be using Hugging Face’s transformers, peft (Parameter-Efficient Fine-Tuning), and datasets libraries.

pip install transformers torch peft datasets accelerate

Make sure you have a GPU available for this process, as fine-tuning, even with LoRA, is demanding.

Step 2: Preparing Your Dataset

For this example, let’s imagine we want to fine-tune Gemma 3n to generate witty, one-line movie reviews. Our dataset might look like this in a simple .jsonl file:

{"text": "A cinematic masterpiece that redefines the genre."}
{"text": "Two hours I'll never get back. Avoid at all costs."}
{"text": "Visually stunning, but the plot was thinner than my patience."}

You can load this easily using the datasets library.

from datasets import load_dataset

# Load your custom dataset
dataset = load_dataset('json', data_files='my_movie_reviews.jsonl')

Step 3: Configuring the LoRA Adapter

This is where the magic happens. We’ll load the base Gemma 3n model and then define our LoRA configuration using the peft library.

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import LoraConfig, get_peft_model

model_name = "google/gemma-3n-base" # Fictional model name for demonstration
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Define the LoRA configuration
lora_config = LoraConfig(
    r=16,  # Rank of the update matrices. Higher can mean more expressive, but more params.
    lora_alpha=32, # Alpha scaling factor.
    target_modules=["q_proj", "v_proj"], # Target the query and value projections in attention layers.
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM"
)

# Apply the LoRA adapter to the model
lora_model = get_peft_model(model, lora_config)

# You can print the trainable parameters to see the difference!
lora_model.print_trainable_parameters()
# trainable params: 4,194,304 || all params: 2,510,422,016 || trainable%: 0.16707

Notice how few parameters are actually trainable! That’s the power of PEFT.

Step 4: Training the Model

Now, we use the standard Trainer from the transformers library, but we pass it our lora_model instead of the original one.

from transformers import TrainingArguments, Trainer

training_args = TrainingArguments(
    output_dir="./gemma3n-lora-movie-reviews",
    per_device_train_batch_size=4,
    num_train_epochs=3,
    logging_steps=10,
    save_steps=50,
)

trainer = Trainer(
    model=lora_model,
    args=training_args,
    train_dataset=dataset['train'],
    # You would also pass your tokenizer and a data collator here
)

# Start fine-tuning!
trainer.train()

Step 5: Inference with Your Fine-Tuned Model

After training, the base model is still untouched. The trainer saves the LoRA adapter weights in the output directory. To use your fine-tuned model, you load the base model and then apply the trained adapter.

from peft import PeftModel

# Load the base model again
base_model = AutoModelForCausalLM.from_pretrained(model_name)

# Load the LoRA adapter and merge it
lora_model = PeftModel.from_pretrained(base_model, "./gemma3n-lora-movie-reviews")

# Now you can generate text with your specialized model
input_text = "The new sci-fi blockbuster was"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids

outputs = lora_model.generate(input_ids)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# Expected output: ... a visually stunning but narratively weak experience.

And that’s it! You’ve successfully fine-tuned Gemma 3n on a custom task using LoRA, creating a specialized, efficient, and powerful new model.