Beyond Prompts: Fine-Tuning Your LLM

WHY FINE TUNING?

While both prompt engineering and fine-tuning aim to enhance the capabilities of large language models (LLMs), they tackle different challenges. Here's a breakdown of some key limitations addressed by fine-tuning but not by prompt engineering, along with illustrative examples:

Prompt Challenge 1: Knowledge Gap

Example: Imagine asking an LLM to diagnose an illness. A well-crafted prompt can guide it through symptoms, but without medical knowledge, the LLM might miss crucial details.

Solution: Fine-tuning exposes the LLM to a vast dataset of labeled medical cases, equipping it with the knowledge needed for accurate diagnoses.

Prompt Challenge 2: Limited Control

Example: You ask an LLM to write a persuasive essay. While a prompt might outline the arguments, the LLM might struggle to maintain a coherent flow or address counter-arguments effectively.

Solution: Fine-tuning can train the LLM on specific reasoning patterns and argument structures, enabling it to construct logical arguments and build a compelling case.

Fine-Tuning LLMs for Real-World Tasks: A Step-by-Step Approach

Data Acquisition:

# The instruction dataset to use
dataset_name = "mlabonne/guanaco-llama2-1k"        

Model LLM:

# The model that you want to train from the Hugging Face hub
model_name = "NousResearch/Llama-2-7b-chat-hf"
# Fine-tuned model name
new_model = "Llama-2-7b-chat-finetune"        

Specify Fine Tuning Parameters

################################################################################
# QLoRA parameters
################################################################################

# LoRA attention dimension
lora_r = 64

# Alpha parameter for LoRA scaling
lora_alpha = 16

# Dropout probability for LoRA layers
lora_dropout = 0.1

################################################################################
# bitsandbytes parameters
################################################################################

# Activate 4-bit precision base model loading
use_4bit = True

# Compute dtype for 4-bit base models
bnb_4bit_compute_dtype = "float16"

# Quantization type (fp4 or nf4)
bnb_4bit_quant_type = "nf4"

# Activate nested quantization for 4-bit base models (double quantization)
use_nested_quant = False


################################################################################
# SFT parameters
################################################################################

# Maximum sequence length to use
max_seq_length = None

# Pack multiple short examples in the same input sequence to increase efficiency
packing = False

# Load the entire model on the GPU 0
device_map = {"": 0}        

Fine Tuning Configuration

# Load LLaMA tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right" # Fix weird overflow issue with fp16 training

# Load LoRA configuration
peft_config = LoraConfig(
    lora_alpha=lora_alpha,
    lora_dropout=lora_dropout,
    r=lora_r,
    bias="none",
    task_type="CAUSAL_LM",
)

# Set training parameters
training_arguments = TrainingArguments(
    output_dir=output_dir,
    num_train_epochs=num_train_epochs,
    per_device_train_batch_size=per_device_train_batch_size,
    gradient_accumulation_steps=gradient_accumulation_steps,
    optim=optim,
    save_steps=save_steps,
    logging_steps=logging_steps,
    learning_rate=learning_rate,
    weight_decay=weight_decay,
    fp16=fp16,
    bf16=bf16,
    max_grad_norm=max_grad_norm,
    max_steps=max_steps,
    warmup_ratio=warmup_ratio,
    group_by_length=group_by_length,
    lr_scheduler_type=lr_scheduler_type,
    report_to="tensorboard"
)

# Set supervised fine-tuning parameters
trainer = SFTTrainer(
    model=model,
    train_dataset=dataset,
    peft_config=peft_config,
    dataset_text_field="text",
    max_seq_length=max_seq_length,
    tokenizer=tokenizer,
    args=training_arguments,
    packing=packing,
)        

Model Training & Saving

# Train model
trainer.train()
# Save trained model
trainer.model.save_pretrained(new_model)        

Conclusion: Remember, choosing the right technique depends on your needs. Prompting offers flexibility, while fine-tuning empowers the LLM with deeper knowledge and stronger control - like choosing the perfect tools for the job!