Fine tune a model unsupervised learning

fine-tune.model.md

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:

Steps to Fine-Tune the Model

1. Prepare the Dataset

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

2. Install Required Libraries

Make sure you have the necessary libraries installed:

pip install transformers torch datasets

3. Create a Python Script for Fine-Tuning

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")

Explanation of the Script

1. Load the Tokenizer and Model: Load the bigcode/starencoder tokenizer and model using the Hugging Face AutoTokenizer and AutoModelForMaskedLM.

2. Prepare the Dataset: Load your combined Solidity code text file into a Hugging Face datasets dataset. The tokenize_function tokenizes the text data, ensuring it fits the model’s expected input format.

3. Data Collator: Use DataCollatorForLanguageModeling to handle the creation of masked language model inputs.

4. Training Arguments: Set up TrainingArguments to specify training parameters, such as the output directory, number of training epochs, batch size, and logging steps.

5. Initialize Trainer: Initialize the Trainer class with the model, training arguments, data collator, and tokenized dataset.

6. Fine-Tune the Model: Call trainer.train() to start the fine-tuning process.

7. Save the Fine-Tuned Model: After fine-tuning, save the model and tokenizer to a directory.

Additional Tips

• 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.