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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Data leakage is one of the main reasons why ML demos look impressive... and then fail in production. 📉 The model didn't become smarter. It just happened to see the correct answers in advance. In 4 minutes, you'll understand where data leaks hide. 🔍 Let's break it down below: 👇 1. Data Leakage 🕳️ Data leakage occurs when information that won't be available at the time of actual prediction is used during the model training process. Because of this, metrics on the validation stage can look much better than the actual quality of the model on new, previously unseen data. 2. Model Evaluation ⚖️ The test set isn't just "additional data". It's a simulation of the future. Only train the model on the information that would have been available to you at the time of prediction. Evaluate it on examples that the model couldn't have influenced during training. 3. Direct Leakage 🚨 This is the most obvious type of leakage. Examples: - a field with information from the future; - an ID that encodes the target variable; - a variable that appears only after an event has occurred; - duplicate records in both the training and test sets. If a feature doesn't exist at the time of inference (prediction), then it's likely a source of data leakage. 4. Indirect Leakage 🕵️ This is the type of leakage that most often traps teams. You perform normalization, imputation, feature selection, outlier removal, or dimensionality reduction before splitting the data into a training and test set. The model didn't directly see the data from the test set. But your preprocessing pipeline already saw it. 5. Train/Test Split ✂️ Wrong:

fit the scaler on all data → split the data → evaluate

Right:

split the data → fit the scaler only on the training set → apply it to both the training and test sets

The same idea applies to imputers, encoders, feature selection, PCA, and any preprocessing step that is trained on the data. 6. Cross-Validation 🔄 Each fold is a mini-experiment with a training and test set. Therefore, preprocessing should be performed within each fold. If you prepared the entire dataset once and then ran cross-validation, each fold would already have had access to its held-out data. 7. Pipelines 🛠️ A pipeline isn't just a way to make the code cleaner. It's also a defense against data leakage. Combine preprocessing, feature selection, and the model into a single pipeline, and then pass this pipeline to cross-validation or hyperparameter search (grid search). 8. AI Engineering Version 🤖 Data leaks also occur in RAG systems and when evaluating LLMs. Leakage occurs when you tune chunks, prompts, re-rankers, thresholds, or examples on the same evaluation dataset that you later present as "held-out". As a result, your benchmark turns into training data. 9. Leakage Checklist ✅ Before trusting the obtained metric, ask yourself: - Could this feature exist at the time of prediction? - Was any transformation (transform) step trained (fit) on the test data? - Did cross-validation include the entire pipeline? - Were we tuning parameters on the final evaluation dataset? If the answer is "yes", then the metric likely doesn't reflect the actual quality of the model. #MachineLearning #DataScience #MLOps #DataLeakage #ArtificialIntelligence #TechTips ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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Transformer implementations for vision, audio, and AI agents Repo: https://github.com/Nicolepcx/transformers-the-definitive-guide