Prompt tuning is a parameter-efficient approach that modifies input representations rather than model weights. Unlike traditional fine-tuning that updates all model parameters, prompt tuning adds and optimizes a small set of trainable tokens while keeping the base model frozen.
Prompt tuning works by prepending trainable "soft prompts" to the input. These soft prompts are continuous vectors that get optimized during training to help the model generate better outputs for specific tasks. This approach offers benefits in terms of efficiency: it maintains a minimal memory footprint by only storing prompt vectors, preserves the model's general capabilities, and allows easy switching between tasks by changing prompts rather than loading entire model copies.
The PEFT library makes implementing prompt tuning straightforward. Here's a basic example:
from peft import PromptTuningConfig, TaskType, get_peft_model
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load base model
model = AutoModelForCausalLM.from_pretrained("your-base-model")
tokenizer = AutoTokenizer.from_pretrained("your-base-model")
# Configure prompt tuning
peft_config = PromptTuningConfig(
task_type=TaskType.CAUSAL_LM,
num_virtual_tokens=8, # Number of trainable tokens
prompt_tuning_init="TEXT", # Initialize from text
prompt_tuning_init_text="Classify if this text is positive or negative:",
tokenizer_name_or_path="your-base-model",
)
# Create prompt-tunable model
model = get_peft_model(model, peft_config)The core of prompt tuning revolves around virtual tokens, which are trainable embeddings added to the input. These typically number between 8 and 32 tokens and can be initialized either randomly or from existing text. The initialization method plays a crucial role in the training process, with text-based initialization often performing better than random initialization.
During training, only the prompt parameters are updated while the base model remains frozen. This focused approach uses standard training objectives but requires careful attention to the learning rate and gradient behavior of the prompt tokens.
When compared to other PEFT approaches, prompt tuning stands out for its efficiency. While LoRA offers low parameter counts and memory usage but requires loading adapters for task switching, prompt tuning achieves even lower resource usage and enables immediate task switching. Full fine-tuning, in contrast, demands significant resources and requires separate model copies for different tasks.
| Method | Parameters | Memory | Task Switching |
|---|---|---|---|
| Prompt Tuning | Very Low | Minimal | Easy |
| LoRA | Low | Low | Requires Loading |
| Full Fine-tuning | High | High | New Model Copy |
When implementing prompt tuning, start with a small number of virtual tokens (8-16) and increase only if the task complexity demands it. Text initialization typically yields better results than random initialization, especially when using task-relevant text. The initialization strategy should reflect the complexity of your target task.
Training requires slightly different considerations than full fine-tuning. Higher learning rates often work well, but careful monitoring of prompt token gradients is essential. Regular validation on diverse examples helps ensure robust performance across different scenarios.
Prompt tuning excels in several scenarios, particularly for classification tasks and simple generation tasks. Its minimal resource requirements make it ideal for environments with limited computational resources. The ability to quickly switch between tasks also makes it valuable for multi-task applications where different behaviors are needed at different times.
To get hands-on experience with prompt tuning, try the Prompt Tuning Tutorial. This practical guide will walk you through implementing the technique with your own model and data.