Fine-tuning a pre-trained model like bigcode/starencoder
on a large collection of Solidity source code without any labeling can be done through unsupervised learning, specifically using masked language modeling (MLM). Here’s a step-by-step guide to fine-tuning the model for your specific needs:
Ensure you have a large collection of Solidity source code files. Combine these files into a single or multiple text files.
Example: Combining Solidity Files into a Text File
cat *.sol > all_solidity_code.txt
Make sure you have the necessary libraries installed:
pip install transformers torch datasets
Here’s a detailed script to fine-tune the bigcode/starencoder
model on your Solidity code dataset using the Hugging Face transformers
library.
Fine-Tuning Script:
import os
from datasets import load_dataset, Dataset
from transformers import AutoTokenizer, AutoModelForMaskedLM, DataCollatorForLanguageModeling, Trainer, TrainingArguments
# Load the tokenizer and model
model_name = "bigcode/starencoder"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForMaskedLM.from_pretrained(model_name)
# Load and preprocess the dataset
data_files = {"train": "path_to_your/all_solidity_code.txt"}
dataset = load_dataset('text', data_files=data_files)
def tokenize_function(examples):
return tokenizer(examples['text'], padding="max_length", truncation=True, max_length=512)
tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=["text"])
# Data collator for language modeling
data_collator = DataCollatorForLanguageModeling(
tokenizer=tokenizer,
mlm=True,
mlm_probability=0.15
)
# Define training arguments
training_args = TrainingArguments(
output_dir="./results",
overwrite_output_dir=True,
num_train_epochs=3,
per_device_train_batch_size=8,
save_steps=10_000,
save_total_limit=2,
prediction_loss_only=True,
logging_dir='./logs',
logging_steps=100
)
# Initialize the Trainer
trainer = Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=tokenized_datasets["train"]
)
# Fine-tune the model
trainer.train()
# Save the fine-tuned model
trainer.save_model("fine-tuned-starencoder-solidity")
tokenizer.save_pretrained("fine-tuned-starencoder-solidity")
-
Load the Tokenizer and Model: Load the
bigcode/starencoder
tokenizer and model using the Hugging FaceAutoTokenizer
andAutoModelForMaskedLM
. -
Prepare the Dataset: Load your combined Solidity code text file into a Hugging Face
datasets
dataset. Thetokenize_function
tokenizes the text data, ensuring it fits the model’s expected input format. -
Data Collator: Use
DataCollatorForLanguageModeling
to handle the creation of masked language model inputs. -
Training Arguments: Set up
TrainingArguments
to specify training parameters, such as the output directory, number of training epochs, batch size, and logging steps. -
Initialize Trainer: Initialize the
Trainer
class with the model, training arguments, data collator, and tokenized dataset. -
Fine-Tune the Model: Call
trainer.train()
to start the fine-tuning process. -
Save the Fine-Tuned Model: After fine-tuning, save the model and tokenizer to a directory.
-
Data Size and Epochs: Depending on the size of your dataset, you might need to adjust the number of training epochs and batch size to ensure efficient training.
-
Evaluation: Consider adding an evaluation step using a separate validation set to monitor the model's performance during training.
-
Compute Resources: Fine-tuning large models can be resource-intensive. Ensure you have access to adequate compute resources (e.g., GPU) to speed up the training process.
By following this guide, you can fine-tune the bigcode/starencoder
model on your collection of Solidity source code, adapting it to understand and generate Solidity code more effectively.