Advanced Fine-Tuning with LLaMA: A Deep Dive with Code Examples

The LLaMA (Large Language Model Meta AI) series by Meta has quickly become a foundation in the world of open-access large language models. Its lightweight design, high performance, and open weights make it an excellent candidate for fine-tuning tasks, especially when cost and compute constraints matter.

This guide is aimed at technical professionals with a solid grounding in PyTorch, Hugging Face Transformers, and LLMs in general. We’ll walk through advanced fine-tuning strategies for LLaMA using parameter-efficient techniques like LoRA/QLoRA, mixed-precision training, memory optimisation, and more.

Fine-tuning LLaMA for domain-specific tasks can drastically improve performance while retaining its core strengths. In this article, we’ll cover:

• A concise overview of LLaMA architecture
• Environment setup for large-scale training
• Dataset preprocessing with Hugging Face Datasets
• LoRA-based fine-tuning using PEFT
• Training loop with mixed precision, gradient accumulation
• Evaluation and inference
• Optimisation techniques like quantisation and FSDP

Model Architecture Overview

LLaMA differs from other models like GPT and BERT in a few key ways:

LLaMA models are optimized for efficiency and perform well even at smaller sizes, which makes them suitable for fine-tuning with modest resources using techniques like LoRA.

Environment Setup

Dependencies:

Code

  pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
  pip install transformers datasets accelerate peft bitsandbytes sentencepiece                          
      

 GPU & Memory Optimization

• Enable bitsandbytes for 4-bit/8-bit quantized models
• Use torch.cuda.empty_cache() and accelerate for memory profiling
• Ensure CUDA_VISIBLE_DEVICES is set properly

export CUDA_VISIBLE_DEVICES=0

Dataset Preparation

Let’s load a custom text dataset and tokenise it using the LLaMA tokeniser:

Code

  from datasets import load_dataset
  from transformers import AutoTokenizer
  
  # Load dataset
  dataset = load_dataset("json", data_files="custom_data.json", split="train")
  
  # Load LLaMA tokenizer
  tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
  
  # Preprocess
  def tokenize_fn(examples):
      return tokenizer(examples["text"], padding="max_length", truncation=True, max_length=512)
  
  tokenized_dataset = dataset.map(tokenize_fn, batched=True)
  tokenized_dataset.set_format(type="torch", columns=["input_ids", "attention_mask"])                       
      

Loading LLaMA with PEFT (LoRA)

We’ll now fine-tune using PEFT (Parameter-Efficient Fine-Tuning).

Code

  from peft import get_peft_model, LoraConfig, TaskType
  from transformers import AutoModelForCausalLM, BitsAndBytesConfig
  
  # Load 4-bit quantized model using bitsandbytes
  bnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_use_double_quant=True)
  
  model = AutoModelForCausalLM.from_pretrained(
      "meta-llama/Llama-2-7b-hf",
      quantization_config=bnb_config,
      device_map="auto",
      trust_remote_code=True,
  )
  
  # Apply LoRA
  lora_config = LoraConfig(
      r=8,
      lora_alpha=32,
      target_modules=["q_proj", "v_proj"],
      lora_dropout=0.1,
      bias="none",
      task_type=TaskType.CAUSAL_LM
  )
  
  model = get_peft_model(model, lora_config)
  model.print_trainable_parameters()                          
      

Memory Advantage: LoRA drastically reduces the trainable parameters, enabling fine-tuning on consumer GPUs.

Training Loop

from transformers import Trainer, TrainingArguments

Code

  training_args = TrainingArguments(
    output_dir="./llama-finetuned",
    per_device_train_batch_size=4,
    gradient_accumulation_steps=8,
    evaluation_strategy="steps",
    save_strategy="steps",
    save_steps=500,
    logging_steps=100,
    num_train_epochs=3,
    learning_rate=2e-4,
    fp16=True,  # or bf16=True on A100
    optim="adamw_torch",
    warmup_steps=100,
    weight_decay=0.01,
    logging_dir="./logs"
  )

  from transformers import DataCollatorForLanguageModeling
  data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)

  trainer = Trainer(
      model=model,
      args=training_args,
      train_dataset=tokenized_dataset,
      data_collator=data_collator,
  )
  trainer.train()                      
      

 Tips:

• Use gradient_accumulation_steps to simulate large batch sizes
• Monitor wandb or tensorboard for training metrics

Evaluation & Inference

Code

  from transformers import pipeline

  # Load inference pipeline
  pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
  
  prompt = "Explain how transformers revolutionized NLP:"
  outputs = pipe(prompt, max_new_tokens=100, do_sample=True, top_p=0.9)
  
  print(outputs[0]["generated_text"])                      
      

You can compare results between the base model and your fine-tuned version for domain-specific prompts.

Saving and Loading Models

Code

  # Save
  model.save_pretrained("llama-finetuned-lora")
  tokenizer.save_pretrained("llama-finetuned-lora")
  
  # Load later
  from transformers import AutoModelForCausalLM, AutoTokenizer
  from peft import PeftModel
  
  base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf", device_map="auto")
  model = PeftModel.from_pretrained(base_model, "llama-finetuned-lora")                      
      

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Best Practices & Optimisation Tips

Training Efficiency

• Use mixed precision (fp16/bf16) to cut memory in half
• Try Deepspeed or FSDP for multi-GPU and large model handling

Quantization

• Use 4-bit QLoRA with bnb_4bit_quant_type="nf4" for extreme memory savings

Sampling Strategy

• Filter long/short samples to ensure uniform token distribution
• Balance multi-domain datasets using class weights or sampling ratios

Checkpoints

• Use save_total_limit to retain only recent checkpoints and save disk space

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Conclusion

Fine-tuning LLaMA with modern tools like LoRA and quantisation unlocks massive performance gains on custom tasks without requiring vast compute resources. With careful dataset preparation, memory-efficient training loops, and evaluation strategies, even small teams can effectively fine-tune SOTA LLMs.

Further Resources:

• LLaMA Official Paper
• PEFT GitHub
• QLoRA Paper
• Hugging Face LLaMA models

Want help fine-tuning LLaMA models for your domain-specific use case? Our expert team can assist with setup, optimization, and deployment. Contact us today to accelerate your AI capabilities.