Build your own custom, lightweight transformer with Langformers

In the world of machine learning, bigger isn't always better — especially when it comes to deploying models in resource-constrained environments like mobile apps or real-time systems. Fortunately, Langformers offers an elegant solution: you can train a smaller model to mimic the embeddings of a large pretrained model.

This technique is heavily inspired by previous works like Hinton et al., 2015, Reimers and Gurevych, 2020, and Lamsal et al., 2025. In this blog, we’ll dive into how to replicate the embedding space of a teacher model using Langformers and build your own custom, lightweight transformer!

Why Mimic a Pretrained Model?

Mimicking, or knowledge distillation, is particularly valuable when you want to:

• Shrink a model without losing too much performance.
• Customize a model architecture to better fit specific memory, latency, or domain constraints.
• Accelerate inference on edge devices, servers, or mobile platforms.

Imagine you have a powerful model like roberta-base or sentence-transformers/all-mpnet-base-v2. Instead of using these heavyweight models in production, you can train a smaller model to match their output embeddings closely, offering fast, efficient, and scalable deployment options.

How Mimicking Works

The core idea is simple:

1. Pass a large set of sentences through both the teacher model and the student model.
2. Calculate the difference between their embeddings using Mean Squared Error (MSE) loss.
3. Adjust the student's weights to minimize this difference.

Over time, the student model learns to replicate the vector space of the teacher — while remaining lightweight and tailored to your needs. Langformers provides two ready-to-use datasets for the training process:

• General-purpose models (if the model is general purpose)
• Social media models (if the model is for social media posts. E.g., tweets)

Mimicking a Pretrained model

Here’s how you can train your custom student model.

First, make sure you have Langformers installed in your environment. If not, install it using pip:

pip install -U langformers

Load a Text Corpus

Load all the sentences that you’ll use for training. In this guide, since we're mimicking the embeddings of roberta-base, we'll be using the dataset targeted for general-purpose models, i.e., langformers/allnli-mimic-embedding.

from datasets import load_dataset

# Load the allnli mimic dataset
data = load_dataset("langformers/allnli-mimic-embedding")

Define Your Student Model Architecture

You have full flexibility to design your student's architecture! Here's a simple configuration:

student_config = {
    "max_position_embeddings": 130,  # Maximum input length
    "num_attention_heads": 8,
    "num_hidden_layers": 8,
    "hidden_size": 128,
    "intermediate_size": 256,
}

You can adjust these hyperparameters based on your specific needs like model size, speed, and memory usage.

Set Up Training Configuration

Define how you want your student model to be trained:

training_config = {
    "num_train_epochs": 10,
    "learning_rate": 5e-5,
    "batch_size": 128,  # Large batch size helps stable MSE training
    "dataset_path": data['train']['sentence'],  # List of sentences
    "logging_steps": 100,
}

Create and Train the Mimicker

Now the fun part: putting everything together!

from langformers import tasks

# Create the mimicker
mimicker = tasks.create_mimicker(
    teacher_model="roberta-base",
    student_config=student_config,
    training_config=training_config
)

# Train your student model
mimicker.train()

And that’s it!

During training, Langformers automatically saves the best model checkpoint whenever it sees an improvement in the loss.

Additional Details

• The student model uses the same tokenizer and vocabulary as the teacher model. No need to train a new tokenizer.
• You can easily swap out teacher models and tweak the student’s depth, hidden size, or attention heads.

View official documentation here: https://langformers.com/mimick-a-model.html