Machine Learning

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Optimizing the model's performance through Prompt Tuning with the PEFT library. ✨ Full-fledged fine-tuning of language models requires a huge amount of video memory and completely overwrites the network's weights. We will apply the Prompt Tuning method (retraining virtual token prompts), which freezes the main model and adjusts only a tiny matrix of virtual embeddings. This allows adapting AI to a narrow task using a regular user's graphics card and without the risk of destroying the neural network's basic knowledge. 📦 First, we will install the necessary libraries for working with transformers and effective fine-tuning methods (PEFT).

pip install torch transformers peft

✅ The packages have been successfully installed in the system and are ready for configuring lightweight training. We will create a basic Prompt Tuning configuration for training just twenty virtual tokens instead of billions of model parameters.

from peft import PromptTuningConfig, PromptTuningInit, get_peft_model
from transformers import AutoModelForCausalLM

peft_config = PromptTuningConfig(
    task_type="CAUSAL_LM",
    prompt_tuning_init=PromptTuningInit.TEXT,
    num_virtual_tokens=20,
    prompt_tuning_init_text="Classify the sentiment of this text:",
    tokenizer_name_or_path="gpt2"
)

🔄 The configuration is initialized and links the text prompt to the trainable virtual embeddings. We will wrap the base model in a PEFT container to freeze the main weights and leave only the new tokens available for gradient descent.

base_model = AutoModelForCausalLM.from_pretrained("gpt2")
peft_model = get_peft_model(base_model, peft_config)
peft_model.print_trainable_parameters()

🚀 The model is ready for training, and the percentage of active parameters will be displayed on the screen (usually less than 0.01%).

python3 -c "from peft import PromptTuningConfig; print('PEFT Setup: OK')"

📝 Expected output: PEFT Setup: OK

pip uninstall peft -y

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