Fine-tuning Llama 3.2 3B for RAG - Analytics Vidhya

Small language models (SLMs) are making a significant impact in AI. They provide strong performance while being efficient and cost-effective. One standout example is the Llama 3.2 3B. It performs exceptionally well in Retrieval-Augmented Generation (RAG) tasks, cutting computational costs and memory usage while maintaining high accuracy. This article explores how to fine-tune the Llama 3.2 3B model. Learn how smaller models can excel in RAG tasks and push the boundaries of what compact AI solutions can achieve.

Table of contents

• What is Llama 3.2 3B?
• Finetuning Llama 3.2 3B
• LoRA
• Libraries Required
• Import the Libraries
• Initialize the Model and Tokenizers
• Initialize the Model for PEFT
• Data Processing
• Setting-up the Trainer Parameters
• Fine-tuning the Model
• Test and Save the Model
• Conclusion
• Frequently Asked Questions

What is Llama 3.2 3B?

The Llama 3.2 3B model, developed by Meta, is a multilingual SLM with 3 billion parameters, designed for tasks like question answering, summarization, and dialogue systems. It outperforms many open-source models on industry benchmarks and supports diverse languages. Available in various sizes, Llama 3.2 offers efficient computational performance and includes quantized versions for faster, memory-efficient deployment in mobile and edge environments.

Also Read: Top 13 Small Language Models (SLMs)

Finetuning Llama 3.2 3B

Fine-tuning is essential for adapting SLM or LLMs to specific domains or tasks, such as medical, legal, or RAG applications. While pre-training enables language models to generate text across diverse topics, fine-tuning re-trains the model on domain-specific or task-specific data to improve relevance and performance. To address the high computational cost of fine-tuning all parameters, techniques like Parameter Efficient Fine-Tuning (PEFT) focus on training only a subset of the model’s parameters, optimizing resource usage while maintaining performance.

LoRA

One such PEFT method is Low Rank Adaptation (LoRA).

In Lora, the weight matrix in SLM or LLM is decomposed into a product of two low-rank matrices.

W = WA * WB

If W has m rows and n columns, then it can be decomposed into WA with m rows and r columns, and WB with r rows and n columns. Here r is much less than m or n. So, rather than training m*n values, we can only train r*(m n) values. r is called rank which is the hyperparameter we can choose.

def lora_linear(x):<br>   h = x @ W # regular linear<br>   h  = scale * (x @ W_A @ W_B) # low-rank update<br>   return h

Checkout: Parameter-Efficient Fine-Tuning of Large Language Models with LoRA and QLoRA

Let’s implement LoRA on the Llama 3.2 3B model.

Libraries Required

• unsloth – 2024.12.9
• datasets – 3.1.0

Installing the above sloth version will also install the compatible pytorch, transformers, and Nvidia GPU libraries. We can use google colab to access the GPU.

Let’s look at the implementation now!

Import the Libraries

from unsloth import FastLanguageModel, is_bfloat16_supported, train_on_responses_only

from datasets import load_dataset, Dataset

from trl import SFTTrainer, apply_chat_template

from transformers import TrainingArguments, DataCollatorForSeq2Seq, TextStreamer

import torch

Initialize the Model and Tokenizers

max_seq_length = 2048 
dtype = None # None for auto-detection.
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

model, tokenizer = FastLanguageModel.from_pretrained(
	model_name = "unsloth/Llama-3.2-3B-Instruct",
	max_seq_length = max_seq_length,
	dtype = dtype,
	load_in_4bit = load_in_4bit,
	# token = "hf_...", # use if using gated models like meta-llama/Llama-3.2-11b
)

For other models supported by Unsloth, we can refer to this document.

Initialize the Model for PEFT

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",
	use_gradient_checkpointing = "unsloth",
	random_state = 42,
	use_rslora = False, 
	loftq_config = None,
)

Description for Each Parameter

• r: Rank of LoRA; higher values improve accuracy but use more memory (suggested: 8–128).  
• target_modules: Modules to fine-tune; include all for better results 
• lora_alpha: Scaling factor; typically equal to or double the rank r.  
• lora_dropout: Dropout rate; set to 0 for optimized and faster training.  
• bias: Bias type; “none” is optimized for speed and minimal overfitting.  
• use_gradient_checkpointing: Reduces memory for long-context training; “unsloth” is highly recommended.  
• random_state: Seed for deterministic runs, ensuring reproducible results (e.g., 42).  
• use_rslora: Automates alpha selection; useful for rank-stabilized LoRA.  
• loftq_config: Initializes LoRA with top r singular vectors for better accuracy, though memory-intensive.

Data Processing

We will use the RAG data to finetune. Download the data from huggingface.

dataset = load_dataset("neural-bridge/rag-dataset-1200", split = "train")

The dataset has three keys as follows:

Dataset({ features: [‘context’, ‘question’, ‘answer’], num_rows: 960 })

The data needs to be in a specific format depending on the language model. Read more details here.

So, let’s convert the data into the required format:

def convert_dataset_to_dict(dataset):
    dataset_dict = {
        "prompt": [],
        "completion": []
    }

    for row in dataset:
        user_content = f"Context: {row['context']}\nQuestion: {row['question']}"
        assistant_content = row['answer']

        dataset_dict["prompt"].append([
            {"role": "user", "content": user_content}
        ])
        dataset_dict["completion"].append([
            {"role": "assistant", "content": assistant_content}
        ])
    return dataset_dict
    
    
converted_data = convert_dataset_to_dict(dataset)
dataset = Dataset.from_dict(converted_data)
dataset = dataset.map(apply_chat_template, fn_kwargs={"tokenizer": tokenizer})

The dataset message will be as follows:

Setting-up the Trainer Parameters

We can initialize the trainer for finetuning the SLM:

trainer = SFTTrainer(
	model = model,
	tokenizer = tokenizer,
	train_dataset = dataset,
	max_seq_length = max_seq_length,
	data_collator = DataCollatorForSeq2Seq(tokenizer = tokenizer),
	dataset_num_proc = 2,
	packing = False, # Can make training 5x faster for short sequences.
	args = TrainingArguments(
    	per_device_train_batch_size = 2,
    	gradient_accumulation_steps = 4,
    	warmup_steps = 5,
    	# num_train_epochs = 1, # Set this for 1 full training run.
    	max_steps = 6, # using small number to test
    	learning_rate = 2e-4,
    	fp16 = not is_bfloat16_supported(),
    	bf16 = is_bfloat16_supported(),
    	logging_steps = 1,
    	optim = "adamw_8bit",
    	weight_decay = 0.01,
    	lr_scheduler_type = "linear",
    	seed = 3407,
    	output_dir = "outputs",
    	report_to = "none", # Use this for WandB etc
	),
)

Description of some of the parameters:

• per_device_train_batch_size: Batch size per device; increase to utilize more GPU memory but watch for padding inefficiencies (suggested: 2).
• gradient_accumulation_steps: Simulates larger batch sizes without extra memory usage; increase for smoother loss curves (suggested: 4).
• max_steps: Total training steps; set for faster runs (e.g., 60), or use `num_train_epochs` for full dataset passes (e.g., 1–3).
• learning_rate: Controls training speed and convergence; lower rates (e.g., 2e-4) improve accuracy but slow training.

Make the model train on responses only by specifying the response template:

trainer = train_on_responses_only(
	trainer,
	instruction_part = "user\n\n",
	response_part = "assistant\n\n",
)

Fine-tuning the Model

trainer_stats = trainer.train()

Here’s the training stats:

Test and Save the Model

Let’s use the model for inference:

FastLanguageModel.for_inference(model)

messages = [
	{"role": "user", "content": "Context: The sky is typically clear during the day. Question: What color is the water?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	tokenize = True,
	add_generation_prompt = True,
	return_tensors = "pt",
).to("cuda")

text_streamer = TextStreamer(tokenizer, skip_prompt = True)
_ = model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 128,
               	use_cache = True, temperature = 1.5, min_p = 0.1)

To save the trained including LoRA weights, use the below code

model.save_pretrained_merged("model", tokenizer, save_method = "merged_16bit")

Checkout: Guide to Fine-Tuning Large Language Models

Conclusion

Fine-tuning Llama 3.2 3B for RAG tasks showcases the efficiency of smaller models in delivering high performance with reduced computational costs. Techniques like LoRA optimize resource usage while maintaining accuracy. This approach empowers domain-specific applications, making advanced AI more accessible, scalable, and cost-effective, driving innovation in retrieval-augmented generation and democratizing AI for real-world challenges.

Also Read: Getting Started With Meta Llama 3.2

Frequently Asked Questions

Q1. What is RAG?  

A. RAG combines retrieval systems with generative models to enhance responses by grounding them in external knowledge, making it ideal for tasks like question answering and summarization.  

Q2. Why choose Llama 3.2 3B for fine-tuning?  

A. Llama 3.2 3B offers a balance of performance, efficiency, and scalability, making it suitable for RAG tasks while reducing computational and memory requirements.  

Q3. What is LoRA, and how does it improve fine-tuning?  

A. Low-Rank Adaptation (LoRA) minimizes resource usage by training only low-rank matrices instead of all model parameters, enabling efficient fine-tuning on constrained hardware.  

Q4. What dataset is used for fine-tuning in this article?  

A. Hugging Face provides the RAG dataset, which contains context, questions, and answers, to fine-tune the Llama 3.2 3B model for better task performance.

Q5. Can the fine-tuned model be deployed on edge devices?  

A. Yes, Llama 3.2 3B, especially in its quantized form, is optimized for memory-efficient deployment on edge and mobile environments. 

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