Fine-Tuning Large Language Models for Code Completion in Niche Programming Languages

Introduction

Large language models (LLMs) have revolutionized the field of natural language processing and have been increasingly applied to programming tasks, including code completion. However, pre-trained LLMs may not perform optimally for niche programming languages due to the limited amount of training data available for these languages. Fine-tuning LLMs for code completion in niche programming languages can significantly improve developer productivity and code quality. In this post, we will explore the process of fine-tuning LLMs for code completion in niche programming languages, including the preparation of training data, the selection of suitable models, and the optimization of hyperparameters.

Preparation of Training Data

The preparation of high-quality training data is crucial for fine-tuning LLMs. The training data should be representative of the niche programming language and should include a diverse range of code snippets, including functions, classes, and modules. The data can be collected from various sources, including open-source repositories, code snippets from online forums, and code written by experienced developers.

To prepare the training data, we can use the following steps:

1# Import necessary libraries
2import pandas as pd
3import torch
4from transformers import AutoTokenizer, AutoModelForCausalLM
5
6# Load the collected data into a pandas dataframe
7data = pd.read_csv('code_snippets.csv')
8
9# Preprocess the data by tokenizing the code snippets
10tokenizer = AutoTokenizer.from_pretrained('codebert-base')
11tokenized_data = []
12for snippet in data['code']:
13    inputs = tokenizer(snippet, return_tensors='pt')
14    tokenized_data.append(inputs)
15
16# Save the preprocessed data to a file
17torch.save(tokenized_data, 'preprocessed_data.pth')

Selection of Suitable Models

The selection of a suitable model is critical for fine-tuning LLMs. The model should be able to capture the syntax and semantics of the niche programming language and should have a sufficient capacity to learn from the training data. Some popular models for code completion include CodeBERT, GraphCodeBERT, and PLBART.

To select a suitable model, we can use the following criteria:

The model should be pre-trained on a large corpus of code and should have a good performance on code completion tasks.
The model should be able to capture the syntax and semantics of the niche programming language.
The model should have a sufficient capacity to learn from the training data.

Fine-Tuning the Model

Fine-tuning the model involves adjusting the model's parameters to fit the training data. This can be done using a variety of optimization algorithms, including stochastic gradient descent (SGD) and Adam.

To fine-tune the model, we can use the following code:

1# Load the preprocessed data
2preprocessed_data = torch.load('preprocessed_data.pth')
3
4# Load the pre-trained model
5model = AutoModelForCausalLM.from_pretrained('codebert-base')
6
7# Define the device (GPU or CPU)
8device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
9
10# Move the model to the device
11model.to(device)
12
13# Define the optimizer and the loss function
14optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)
15loss_fn = torch.nn.CrossEntropyLoss()
16
17# Fine-tune the model
18for epoch in range(5):
19    model.train()
20    total_loss = 0
21    for batch in preprocessed_data:
22        input_ids = batch['input_ids'].to(device)
23        attention_mask = batch['attention_mask'].to(device)
24        labels = batch['labels'].to(device)
25
26        optimizer.zero_grad()
27
28        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
29        loss = loss_fn(outputs, labels)
30
31        loss.backward()
32        optimizer.step()
33
34        total_loss += loss.item()
35    print(f'Epoch {epoch+1}, Loss: {total_loss / len(preprocessed_data)}')

Evaluation of the Model

Evaluating the model's performance is critical to determine its effectiveness. We can use a variety of metrics, including accuracy, precision, recall, and F1-score, to evaluate the model's performance.

To evaluate the model, we can use the following code:

1# Load the test data
2test_data = pd.read_csv('test_code_snippets.csv')
3
4# Preprocess the test data
5test_tokenized_data = []
6for snippet in test_data['code']:
7    inputs = tokenizer(snippet, return_tensors='pt')
8    test_tokenized_data.append(inputs)
9
10# Evaluate the model
11model.eval()
12total_correct = 0
13with torch.no_grad():
14    for batch in test_tokenized_data:
15        input_ids = batch['input_ids'].to(device)
16        attention_mask = batch['attention_mask'].to(device)
17        labels = batch['labels'].to(device)
18
19        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
20        _, predicted = torch.max(outputs.scores, dim=1)
21        total_correct += (predicted == labels).sum().item()
22
23accuracy = total_correct / len(test_tokenized_data)
24print(f'Accuracy: {accuracy:.4f}')

Common Pitfalls and Mistakes to Avoid

Fine-tuning LLMs can be challenging, and there are several common pitfalls and mistakes to avoid:

Overfitting: Overfitting occurs when the model is too complex and fits the training data too closely, resulting in poor performance on unseen data. To avoid overfitting, we can use regularization techniques, such as dropout and weight decay.
Underfitting: Underfitting occurs when the model is too simple and fails to capture the underlying patterns in the data. To avoid underfitting, we can use more complex models or increase the size of the training data.
Data quality: The quality of the training data is critical for fine-tuning LLMs. We should ensure that the data is accurate, complete, and consistent.

Best Practices and Optimization Tips

To optimize the performance of the model, we can use the following best practices and optimization tips:

Use pre-trained models: Pre-trained models can provide a good starting point for fine-tuning and can reduce the amount of training time required.
Use transfer learning: Transfer learning involves using a pre-trained model as a starting point and fine-tuning it on a smaller dataset. This can be particularly useful when the amount of training data is limited.
Use data augmentation: Data augmentation involves generating additional training data by applying transformations to the existing data. This can help to increase the size of the training data and improve the model's performance.

Conclusion

Fine-tuning LLMs for code completion in niche programming languages can significantly improve developer productivity and code quality. By following the steps outlined in this post, developers can adapt LLMs to their specific needs and overcome the limitations of pre-trained models. However, fine-tuning LLMs can be challenging, and there are several common pitfalls and mistakes to avoid. By using pre-trained models, transfer learning, and data augmentation, we can optimize the performance of the model and achieve state-of-the-art results.