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Abhishek Aman

LLM Finetuning Types and PEFT: Optimizing Language Models Efficiently

Published on December 1, 2023

As Large Language Models (LLMs) continue to grow in size and capability, the need for efficient fine-tuning methods becomes increasingly important. In this post, we'll explore various LLM finetuning types and dive into Parameter-Efficient Fine-Tuning (PEFT) techniques, complete with code examples.

LLM Finetuning Types

  1. Full Finetuning: This involves updating all parameters of the pre-trained model. While effective, it's computationally expensive and requires significant resources.
  2. Selective Finetuning: Only certain layers or components of the model are updated, reducing computational costs while still achieving good performance.
  3. Prompt Tuning: Instead of modifying the model's parameters, this method optimizes a set of continuous prompt embeddings for specific tasks.
  4. PEFT Methods: These techniques aim to fine-tune models with high performance while updating only a small subset of parameters.

PEFT Techniques

PEFT methods have gained popularity due to their efficiency and effectiveness. Let's explore some key PEFT techniques with code examples using the Hugging Face Transformers library.

1. LoRA (Low-Rank Adaptation)

LoRA adds trainable rank decomposition matrices to the original model weights, significantly reducing the number of trainable parameters.


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

model = AutoModelForCausalLM.from_pretrained("gpt2")
peft_config = LoraConfig(
    task_type=TaskType.CAUSAL_LM,
    r=8,
    lora_alpha=32,
    lora_dropout=0.1
)
peft_model = get_peft_model(model, peft_config)

2. Prefix Tuning

Prefix Tuning prepends trainable continuous prompts to the input, keeping the original model parameters frozen.


from peft import PrefixTuningConfig, get_peft_model

peft_config = PrefixTuningConfig(
    task_type=TaskType.CAUSAL_LM,
    num_virtual_tokens=20,
    prefix_projection=True
)
peft_model = get_peft_model(model, peft_config)

3. P-Tuning

P-Tuning optimizes continuous prompts that are inserted at specific positions in the input sequence.


from peft import PromptTuningConfig, get_peft_model

peft_config = PromptTuningConfig(
    task_type=TaskType.CAUSAL_LM,
    num_virtual_tokens=10,
    prompt_tuning_init=PromptTuningInit.TEXT,
    prompt_tuning_init_text="Classify the sentiment:"
)
peft_model = get_peft_model(model, peft_config)

Advantages of PEFT

  • Reduced memory and computational requirements
  • Faster training and inference times
  • Easier model distribution and updates
  • Mitigation of catastrophic forgetting

Conclusion

PEFT techniques offer a powerful solution to the challenges of fine-tuning large language models. By allowing efficient adaptation of LLMs to specific tasks with minimal resource requirements, PEFT opens up new possibilities for deploying and customizing state-of-the-art language models across a wide range of applications.

As research in this area continues to evolve, we can expect to see even more innovative PEFT methods that further push the boundaries of what's possible with LLMs.

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