Base Model Selection

Select a base model that aligns with your project’s requirements. SeekrFlow provides various pretrained models. Selecting the appropriate base model for fine-tuning in a training run is a crucial decision that can significantly impact the performance and effectiveness of the resulting model. Here are several key considerations to keep in mind when choosing a base model for fine-tuning with SeekrFlow:

Model Size and Capacity

Factors to Consider:

Compute Resources: Larger models, such as GPT-3, require significant computational resources for both training and inference. Ensure that you have the necessary hardware and budget to support the model.

Performance Needs: Larger models generally provide better performance and can handle more complex tasks, but they may also be slower and more costly to run.

Pretrained Knowledge

Factors to Consider:

Domain-Specific Pre-training: Select a base model that has been pre-trained on data relevant to your specific domain. This can significantly reduce the amount of fine-tuning required.

General vs. Specialized Models: General models are versatile and can be fine-tuned for a wide range of tasks, while specialized models may already include knowledge pertinent to your domain.

Example:

BioBERT: A version of BERT pre-trained on biomedical literature, making it highly suitable for healthcare and life sciences applications.

Transfer Learning Potential

Factors to Consider:

Transferability: Consider how well the model can transfer its learned knowledge to your specific task. Models with high transfer learning potential can adapt more effectively to new tasks with less data.

Baseline Performance: Evaluate the base model’s performance on tasks similar to yours. A model that performs well on related tasks is likely a good candidate.

Example:

BERT: Known for its strong transfer learning capabilities, making it a popular choice for a wide range of NLP tasks, from text classification to named entity recognition.

Fine-Tuning Efficiency

Factors to Consider:

Data Requirements: Some models require large amounts of data for effective fine-tuning, while others can achieve good performance with less data. SeekrFlow’s principle alignment feature can also be particularly useful when data is limited.

Training Time: Consider the time required to fine-tune the model. Smaller models may be quicker to train, while larger models may take longer but offer better performance.

Example:

DistilBERT: A smaller, distilled version of BERT that is faster to fine-tune and requires fewer resources, making it suitable for scenarios with limited computational power.

Hardware Selection

Selecting the appropriate hardware for your training run is essential for ensuring efficient and cost-effective fine-tuning of your large language model (LLM) with SeekrFlow. The right hardware can significantly impact the speed, performance, and cost of your training process. Here are several key considerations to keep in mind:

Compute Power

Factors to Consider:

AI Accelerators vs CPU: Specialized training hardware (such as GPUs, HPUs, or TPUs) are typically preferred for deep learning tasks due to their parallel processing capabilities, which can significantly speed up training times compared to CPUs.

Memory Capacity

Factors to Consider:

Model Size: Larger models require more memory to store the model parameters and intermediate calculations. Ensure that the chosen hardware has sufficient memory to accommodate the model you are fine-tuning.

Batch Size: Larger batch sizes can help speed up training but also require more memory. Balance batch size with available memory to optimize training efficiency.

Example:

NVIDIA A100: Offers 40 GB or 80 GB of memory, making it suitable for very large models like GPT-3.

NVIDIA T4: More affordable with 16 GB of memory, suitable for smaller models or when budget constraints are significant.

Hyperparameters

Hyperparameters play a crucial role in the training process of machine learning models, impacting both performance and training efficiency. Proper tuning of these parameters is essential to achieving optimal results. Here’s an expanded guide on the key hyperparameters to consider and how to set their values:

Learning Rate

Factors to Consider:

Impact on Convergence: The learning rate controls how much the model’s weights are updated with respect to the loss gradient. A high learning rate can lead to rapid convergence but risks overshooting the optimal solution, while a low learning rate ensures stable convergence but may require more training epochs.

Start with a Small Value: A common practice is to start with a small learning rate (e.g., 0.001) and adjust based on the training performance.

Batch Size

Factors to Consider:

Memory Constraints: Larger batch sizes require more memory but can lead to faster and more stable training due to more accurate gradient estimates.

Training Speed: Smaller batch sizes can lead to noisier updates but may converge faster due to more frequent weight updates.

Experimentation: Start with a moderate batch size (e.g., 32 or 64) and adjust based on memory availability and training speed.

Number of Epochs

Factors to Consider:

Overfitting: More epochs allow the model to learn more from the data but also increase the risk of overfitting.

Training Time: The number of epochs impacts the total training time. Ensure that the chosen number of epochs balances training time with model performance.

Max Length

Factors to Consider:

Sequence Length: The maximum length of input sequences the model will handle. Longer sequences can capture more context but require more memory and computation.

Task Requirements: Set based on the typical length of the input data for your task.

Balanced Length: Choose a length that balances capturing sufficient context with computational efficiency.

Bf16 (Bfloat16 Precision)

Factors to Consider:

Memory Efficiency: Bfloat16 reduces memory usage, allowing for larger models or batch sizes.

Training Stability: Maintains training stability while offering computational efficiency.

Hardware Support: Ensure your selected hardware supports bf16 precision.

Gradient Checkpointing

Factors to Consider:

Memory Usage: Gradient checkpointing trades increased computation for reduced memory usage, allowing for training larger models.

Training Time: May increase training time due to additional computations during backpropagation.

Complex Models: Particularly useful for training very large models where memory is a constraint.

Practical Example

Here’s a comprehensive example of setting hyperparameters for a training run with SeekrFlow. API reference can be found here: https://docs.seekr.com/reference/post_flow-fine-tunes

import requests 

url = "https://build.seekr.com/v1/flow/fine-tunes" 

payload = { 

    "infrastructure_config": { 

        "n_cpu": 86, 

        "n_gpu": 8, 

        "memory": 2400 

    }, 

    "training_config": { 

        "training_files": ["string"], 

        "n_epochs": 10, 

        "batch_size": 32, 

        "learning_rate": 0.0001, 

        "model": "meta-llama/Llama-2-7b-hf", 

        "hf_token": "string", 

        "experiment_name": "string", 

        "max_length": 512, 

        "bf16": True, 

        "gradient_checkpointing": True 

    } 

} 

headers = { 

    "accept": "application/json", 

    "content-type": "application/json" 

} 

response = requests.post(url, json=payload, headers=headers) 

print(response.text)