Jara-Converse---Transformer-based-supervised-LLM

JaraConverse model is an advanced Transformer-based supervised Language Model (LLM) tailored specifically for generating Python code snippets.

• Date : 2024/12/20
• Author : Hammad Hussain, Abdul Moez
• Version : 0.1

MIT License

Dependencies

• Python 3.9+
• Tensorflow <=2.15
• Datasets
• Transformers
• codecarbon
• plotly

License

This project is licensed under the MIT License.

Table of Contents

• Installation
• Input Dataset
• Training the Model
• Visualizing Training Progress
• Running the Demo
• Configuration Details
  • GlobalVariables.py

Installation

Before training the model, ensure you have all necessary dependencies installed. You can do this by running:

pip install -r requirements.txt

Input Dataset

This model uses an SQLite3 database by default, requiring two columns: title and code inside the snippets table. You can change these default settings in the DataBaseConfiguration enum in GlobalVariables.py if you are using a different format. A sample image of the required dataset structure is attached.

Training the Model

To train the JaraConverse model, execute the following command:

python JaraConverseTrainer.py

Ensure your input data is formatted correctly in the SQLite3 database with columns for title and code. You can adjust these default column names in the GlobalVariables.py file, which holds all the configurations for the model.

Visualizing Training Progress

JaraConverse uses TensorBoard for monitoring the training process. After training, you can visualize the training progress and other metrics by running:

python JaraConverseVisualizer.py

This will launch TensorBoard and allow you to view detailed graphs and metrics of the training process.

Running the Demo

The demo script loads the model from a checkpoint and generates code snippets based on the input data. Run the demo script with:

python JaraConverseDemo.py

By default, JaraConverseDemo.py loads the model from a checkpoint. This is due to compatibility issues when training on Colab and using the model on another system. Ensure you use the same parameters for loading the checkpoint as those used during training.

Configuration Details

The GlobalVariables.py file contains all the configuration parameters for the JaraConverse model. Below is a detailed explanation of each configuration parameter to help developers understand and customize the model.

GlobalVariables.py

VariableParameters

This enum class holds the general parameters for model training and setup.

class VariableParameters(Enum):
    MODEL_NAME: str = "JaraConverse"
    SET_LIMIT_ON_GPU: bool = False
    MAX_GPU_UTILIZATION_ON_LIMIT: int = 5

    SET_LIMIT_ON_CPU: bool = False
    OMP_THREADS: int = 5
    MKL_THREADS: int = 5
    INTER_AND_INTRA_OP_PARALLELISM_THREADS: int = 0

    SAVED_STATES_NAME: str = "saved_states.pkl"
    SAVED_HISTORY_NAME: str = "saved_history.pkl"
    SAVED_MODEL_NAME: str = "JaraConverse.keras"
    SAVED_MODEL_WEIGHTS_NAME: str = "saved_weights.h5"
    CHECKPOINT_NAME: str = "cp.ckpt"

    BASE_PATH: str = Path(__file__).parent.__str__()
    MODEL_BASE_PATH: str = path.join(BASE_PATH, f"{MODEL_NAME}Model").__str__()
    CHECKPOINT_DIR: str = path.join(MODEL_BASE_PATH, "model_checkpoints").__str__()
    TENSORBOARD_DIR: str = path.join(MODEL_BASE_PATH, "tensorboard").__str__()

    SAVED_STATES_DIR: str = path.join(MODEL_BASE_PATH, "model_saved_states").__str__()
    CLEANED_DATASET_DIR: str = path.join(MODEL_BASE_PATH, "cleaned_dataset").__str__()
    SAVED_MODEL_DIR: str = path.join(MODEL_BASE_PATH, "trained_model").__str__()

    SAVED_MODEL_WEIGHTS_DIR: str = path.join(MODEL_BASE_PATH, "trained_weights").__str__()
    VISUALIZER_DIR: str = path.join(MODEL_BASE_PATH, "training_visualization").__str__()

    SAVED_HISTORY_PATH: str = path.join(SAVED_STATES_DIR, SAVED_HISTORY_NAME).__str__()

DataBaseConfiguration

This enum class configures the database parameters for training.

class DataBaseConfiguration(Enum):
    TRAINING_DATABASE_PATH: str = path.join(VariableParameters.BASE_PATH.value, "python_code_snippets.db").__str__()
    DATABASE_TABLE_NAME: str = "snippets"
    UNNECESSARY_COLUMNS_IN_DB: list[str] = None

    INPUT_DATA_COLUMN_NAME: str = "title"
    OUTPUT_DATA_COLUMN_NAME: str = "code"
    SPLIT_DATASET: bool = True

    SPLIT_PERCENTAGE: float = 0.2
    SHUFFLE_DATASET: bool = True
    FORCE_REPROCESS_DATASET: bool = False

TransformersTokenizerConfiguration

This enum class configures the tokenizer parameters for the model.

class TransformersTokenizerConfiguration(Enum):
    TOKENIZER_PATH: str = path.join(VariableParameters.MODEL_BASE_PATH.value, "JaraConverseTokenizer").__str__()

    TRAIN_TOKENIZER: bool = False
    TRAINING_TOKENIZER_DATA_COLUMN: str = "code"
    TRAINING_TOKENIZER_VOCAB_SIZE: int = 52000

    TRAINING_SEED: int = 2050
    TRAINING_BATCH_SIZE: int = 32
    VALIDATION_BATCH_SIZE: int = 8

JaraConverseModelConfiguration

This enum class configures the model parameters.

class JaraConverseModelConfiguration(Enum):
    MAX_MODEL_INPUT_SIZE: int = 512
    MAX_MODEL_OUTPUT_SIZE: int = 512
    MAX_POSITIONAL_ENCODING_LENGTH: int = MAX_MODEL_OUTPUT_SIZE + 50

    NUMBER_OF_LAYERS: int = 6
    DIMENSIONALITY_OF_MODEL_EMBEDDINGS: int = 212
    FF_DIMENSION: int = 212

    NUM_OF_HEADS: int = 8
    LEARNING_DROPOUT_RATE: float = 0.001
    IS_FIXED_LEARNING_RATE: bool = False
    FIXED_LEARNING_RATE: float = 2.5e-5

    MODEL_EPOCHS: int = 2
    MODEL_EARLY_STOPPING_PATIENCE: int = 5

    ADAM_SCHEDULER_WARMUP_STEPS: int = 4000
    ADAM_OPTIMIZER_BETA_1: float = .9
    ADAM_OPTIMIZER_BETA_2: float = .98

    ADAM_OPTIMIZER_EPSILON: float = 1e-9

    GRADIENT_ACCUMULATION_STEPS = 4

AutoCalculateModelParams

This class automatically calculates certain model parameters based on configurations.

class AutoCalculateModelParams(object):
    STEP_PER_TRAINING_EPOC: int = TransformersTokenizerConfiguration.TRAINING_BATCH_SIZE.value
    STEP_PER_VALIDATION_EPOC: int = TransformersTokenizerConfiguration.VALIDATION_BATCH_SIZE.value