Custom AI Model Fine-Tuning

OmniTensor provides a sophisticated infrastructure for fine-tuning custom AI models leveraging its decentralized compute network. This guide explores advanced techniques for optimizing pre-trained models to suit specific use cases within the OmniTensor ecosystem.

Overview

Fine-tuning allows developers to adapt pre-existing models to specific domains or tasks, significantly reducing the time and resources required compared to training from scratch. OmniTensor's distributed fine-tuning pipeline enables efficient and scalable model optimization across its decentralized network.

Key Components

1. Distributed Fine-Tuning Orchestrator

2. Adaptive Learning Rate Scheduler

3. Gradient Accumulation and Sharding

4. Automated Hyperparameter Optimization

5. Cross-Validation on Decentralized Data

Fine-Tuning Process

1. Preparing Your Model and Data

Initialize your fine-tuning job with the OmniTensor SDK:

from omnitensor import FineTuningJob, ModelRepository, DatasetManager

base_model = ModelRepository.get("gpt-3-small")
custom_dataset = DatasetManager.load("my_domain_specific_data")

job = FineTuningJob(
    base_model=base_model,
    dataset=custom_dataset,
    task_type="text_classification"
)

2. Configuring Fine-Tuning Parameters

Set up advanced fine-tuning parameters:

from omnitensor.finetuning import AdaptiveLearningRate, GradientAccumulation

job.set_hyperparameters(
    epochs=10,
    batch_size=32,
    learning_rate=AdaptiveLearningRate(
        initial_lr=5e-5,
        warmup_steps=1000,
        decay_strategy="cosine"
    ),
    gradient_accumulation=GradientAccumulation(
        num_micro_batches=4
    )
)

3. Implementing Custom Loss Functions

Define a custom loss function for your specific task:

import torch
from omnitensor.finetuning import LossFunction

class CustomFocalLoss(LossFunction):
    def __init__(self, alpha=0.25, gamma=2.0):
        self.alpha = alpha
        self.gamma = gamma

    def calculate(self, predictions, targets):
        bce_loss = torch.nn.functional.binary_cross_entropy_with_logits(predictions, targets, reduction='none')
        pt = torch.exp(-bce_loss)
        focal_loss = self.alpha * (1 - pt) ** self.gamma * bce_loss
        return focal_loss.mean()

job.set_loss_function(CustomFocalLoss(alpha=0.3, gamma=2.5))

4. Leveraging Distributed Training

Utilize OmniTensor's distributed training capabilities:

from omnitensor.finetuning import DistributedConfig

job.set_distributed_config(DistributedConfig(
    num_nodes=5,
    gpu_per_node=4,
    communication_backend="nccl"
))

5. Implementing Custom Callbacks

Create custom callbacks for monitoring and intervention during training:

from omnitensor.finetuning import Callback

class EarlyStoppingCallback(Callback):
    def __init__(self, patience=3, min_delta=0.001):
        self.patience = patience
        self.min_delta = min_delta
        self.best_score = float('inf')
        self.counter = 0

    def on_epoch_end(self, epoch, logs):
        current_score = logs['val_loss']
        if current_score < self.best_score - self.min_delta:
            self.best_score = current_score
            self.counter = 0
        else:
            self.counter += 1
            if self.counter >= self.patience:
                self.model.stop_training = True

job.add_callback(EarlyStoppingCallback(patience=5, min_delta=0.0005))

6. Automated Hyperparameter Optimization

Leverage OmniTensor's automated hyperparameter tuning:

from omnitensor.finetuning import HyperparameterOptimization, HyperparameterSpace

hp_space = HyperparameterSpace(
    learning_rate={"min": 1e-5, "max": 1e-3, "scale": "log"},
    batch_size=[16, 32, 64, 128],
    num_layers=[2, 3, 4, 5]
)

hp_optimization = HyperparameterOptimization(
    strategy="bayesian",
    num_trials=50,
    metric_to_optimize="val_f1_score",
    direction="maximize"
)

job.enable_hp_optimization(hp_space, hp_optimization)

7. Cross-Validation on Decentralized Data

Implement cross-validation across the decentralized network:

from omnitensor.finetuning import DecentralizedCrossValidation

cross_val = DecentralizedCrossValidation(
    num_folds=5,
    stratify=True,
    shuffle=True
)

job.set_cross_validation(cross_val)

8. Launching the Fine-Tuning Job

Start the fine-tuning process:

fine_tuned_model = job.run()
print(f"Fine-tuning completed. Model accuracy: {fine_tuned_model.evaluate()}")

Advanced Techniques

1. Progressive Layer Unfreezing

Gradually unfreeze layers during fine-tuning:

from omnitensor.finetuning import ProgressiveUnfreezing

unfreezing = ProgressiveUnfreezing(
    initial_frozen_layers=12,
    unfreeze_every_n_epochs=2
)

job.set_layer_unfreezing(unfreezing)

2. Knowledge Distillation

Implement knowledge distillation for model compression:

from omnitensor.finetuning import KnowledgeDistillation

distillation = KnowledgeDistillation(
    teacher_model=ModelRepository.get("gpt-3-large"),
    temperature=2.0,
    alpha=0.5
)

job.enable_knowledge_distillation(distillation)

3. Adversarial Training

Enhance model robustness with adversarial training:

from omnitensor.finetuning import AdversarialTraining

adversarial_config = AdversarialTraining(
    attack_type="pgd",
    epsilon=0.01,
    alpha=0.001,
    num_steps=10
)

job.enable_adversarial_training(adversarial_config)

Monitoring and Analytics

Utilize OmniTensor's real-time monitoring tools during fine-tuning:

from omnitensor.monitoring import FineTuningMonitor

monitor = FineTuningMonitor(job)
monitor.start()

# Fine-tuning process runs here

metrics = monitor.get_metrics()
print(f"Training loss: {metrics.train_loss}")
print(f"Validation accuracy: {metrics.val_accuracy}")
print(f"GPU utilization: {metrics.gpu_utilization}%")

monitor.stop()