train_script.py

# Import necessary libraries
from transformers import AutoTokenizer
from datasets import load_dataset
import nextgenjax as ngj
import jax
import optax
import flax
import numpy as np
from tqdm import tqdm

# Initialize the NextGenJAX model and tokenizer
model_name = "VishwamAI/vishwamai"  # This will be replaced with the actual NextGenJAX model name
config = ngj.NextGenJAXConfig()  # Placeholder for actual config
model = ngj.NextGenJAXModel(config)
tokenizer = ngj.NextGenJAXTokenizer.from_pretrained(model_name)

# Load datasets
datasets = {
    "gaokao": load_dataset("MARIO-Math-Reasoning/Gaokao2023-Math-En"),
    "gemma": load_dataset("path_to_gemma_dataset"),  # Placeholder
    "phi": load_dataset("path_to_phi_dataset"),  # Placeholder
    "mmlu_math": load_dataset("path_to_mmlu_math_dataset")  # Placeholder
}

def preprocess_function(examples, dataset_name):
    if dataset_name == "gaokao":
        inputs = [f"Question: {q}\nAnswer:" for q in examples["question"]]
        targets = [f" {a}" for a in examples["answer"]]
    elif dataset_name in ["gemma", "phi", "mmlu_math"]:
        # Placeholder for dataset-specific preprocessing
        inputs = examples["input"]
        targets = examples["output"]

    model_inputs = tokenizer(inputs, max_length=512, truncation=True, padding="max_length")
    labels = tokenizer(targets, max_length=512, truncation=True, padding="max_length")
    model_inputs["labels"] = labels["input_ids"]
    return model_inputs

# Preprocess and combine datasets
combined_dataset = []
for name, dataset in datasets.items():
    tokenized_dataset = dataset["train"].map(
        lambda examples: preprocess_function(examples, name),
        batched=True,
        remove_columns=dataset["train"].column_names
    )
    combined_dataset.extend(tokenized_dataset)

# Split the combined dataset into train and evaluation sets
np.random.shuffle(combined_dataset)
split_index = int(len(combined_dataset) * 0.8)
train_dataset = combined_dataset[:split_index]
eval_dataset = combined_dataset[split_index:]

# Initialize optimizer
learning_rate = 1e-4
optimizer = optax.adam(learning_rate)
opt_state = optimizer.init(model.params)

# Training loop
num_epochs = 3
batch_size = 8

for epoch in range(num_epochs):
    # Training
    train_losses = []
    for i in tqdm(range(0, len(train_dataset), batch_size)):
        batch = train_dataset[i:i+batch_size]

        def loss_fn(params):
            logits = model.apply(params, batch['input_ids'])
            loss = jax.nn.cross_entropy_loss(logits, batch['labels'])
            return loss

        loss, grads = jax.value_and_grad(loss_fn)(model.params)
        updates, opt_state = optimizer.update(grads, opt_state)
        model.params = optax.apply_updates(model.params, updates)
        train_losses.append(loss)

    print(f"Epoch {epoch+1}/{num_epochs}, Train Loss: {np.mean(train_losses)}")

    # Evaluation
    eval_losses = []
    for i in range(0, len(eval_dataset), batch_size):
        batch = eval_dataset[i:i+batch_size]
        logits = model.apply(model.params, batch['input_ids'])
        loss = jax.nn.cross_entropy_loss(logits, batch['labels'])
        eval_losses.append(loss)

    print(f"Epoch {epoch+1}/{num_epochs}, Eval Loss: {np.mean(eval_losses)}")

    # Save checkpoint
    checkpoint_path = f"./checkpoints/model_epoch_{epoch+1}.ckpt"
    with open(checkpoint_path, 'wb') as f:
        f.write(flax.serialization.to_bytes(model.params))

# Final model saving (replace with appropriate method for NextGenJAX)
# model.save_pretrained("VishwamAI/vishwamai-finetuned")
# tokenizer.save_pretrained("VishwamAI/vishwamai-finetuned")

print("Training completed.")