Datasets greater than 1 GB in size can take advantage of efficient processing techniques to ensure faster and more scalable fine-tuning operations, including:
  • Faster preprocessing by tokenizing data in advance
  • Efficient data loading using streaming and out-of-core processing
  • Multi-GPU, data-parallel distributed training

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Multi-GPU distributed training

Predibase supports running your fine-tuning jobs using multiple GPUs in parallel. Each GPU processes a different partition of the dataset (shuffled between epochs) and replicates the model weights on each GPU. Using GPUs with fast inerconnects (e.g., H100 SXM), distributed training provides near-linear scaling in throughput as you train with more GPUs, allowing you to fine-tune models on very large datasets in a fraction of the time.
Currently, distributed training is an enterprise-only feature, and requires purchasing reserved capacity to access. Reach out to sales@predibase.com to learn more.
Currently, distributed training is only supported for sft and continued_pretraining task types. It is not available for classification or GRPO task types at this time.

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Pretokenizing your dataset

To take advantage of large dataset fine-tuning, Predibase requires that such datasets are tokenized in advance and saved in partitioned arrow format compatible with HuggingFace’s Datasets library. This step allows datasets to be streamed into memory efficiently and processed in parallel across multiple GPUs (if available to your tenant), in addition to speeding up preprocessing time by skipping tokenization during training. This guide will walk you through the process of preparing your data for large training jobs in Predibase. You can follow along in the following notebook: Open In Colab
1

Load your dataset

You can load your dataset either in-memory or out-of-core, depending on its size and your available resources. Here’s an example using the Hugging Face datasets library:
from datasets import load_dataset

# Load an in-memory dataset
dataset = load_dataset("csv", data_files="your_dataset.csv")

# For out-of-core processing, use streaming
dataset = load_dataset("csv", data_files="your_dataset.csv", streaming=True)
2

Initialize the tokenizer

Use the transformers library to load the appropriate tokenizer for the LLM that you want to use. Note that some LLMs like Llama-3 and Mistral require creating a Huggingface account and requesting access since they are gated.
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
if not tokenizer.pad_token_id:
    tokenizer.pad_token_id = tokenizer.eos_token_id
3

Batch tokenize your data

You will need to tokenize the data ahead of time.
  • If you’re doing instruction tuning, you have to tokenize the prompt and completion columns independently.
  • If you’re doing completions style training, you just need to tokenize your text column.
def tokenize_data(examples):
    prompt_tokens = tokenizer(examples["prompt"], truncation=True)['input_ids']
    completion_tokens = tokenizer(examples["completion"], truncation=True)['input_ids']
    return {"prompt": prompt_tokens, "completion": completion_tokens}

tokenized_dataset = dataset.map(tokenize_data, batched=True)
4

Create input_ids and labels

Next, you need to concatenate the tokenized prompt and completion, then add an EOS token at the end. The process varies based on your training approach:
  • For instruction tuning, you need to concat your prompt tokens with your completion tokens
  • For completions style training, you can just re-use your input tokens as your labels.
def prepare_train_features(examples):
    prompt_ids = examples["prompt"]
    completion_ids = examples["completion"]

    input_ids = prompt_ids + completion_ids + [tokenizer.eos_token_id]

    # For instruction tuning
    labels = [-100] * len(prompt_ids) + completion_ids + [tokenizer.eos_token_id]

    # For completions style training
    # labels = input_ids.copy()

    # For multi-turn conversation
    # labels = [-100 if token in user_role_tokens else token for token in input_ids]

    return {"input_ids": input_ids, "labels": labels}

prepared_dataset = tokenized_dataset.map(prepare_train_features)
5

Create a split column (optional)

Add a split column to your dataset for training and evaluation:
def add_split_column(example):
    example["split"] = "train" if random.random() > 0.95 else "evaluation"
    return example

final_dataset = prepared_dataset.map(add_split_column)
final_dataset = final_dataset.remove_columns(
    [col for col in final_dataset.column_names if col not in ['input_ids', 'labels', 'split']]
)
6

Save the dataset

Save your prepared dataset to disk or directly to S3 using the Huggingface Datasets library. Note, it must be saved as a multi-partition arrow file using the Huggingface Datasets format.
from datasets import Dataset

# Save locally
final_dataset.save_to_disk("path/to/local/directory")

# Or save directly to S3
final_dataset.save_to_disk(
    "s3://your-bucket-name/path/to/dataset",
    key="<your_aws_access_key_id>",
    secret="<your_aws_secret_access_key>"
)
If you decide to save locally, you will need to upload your dataset folder to S3.
7

Upload the dataset to Predibase

Finally, use the Predibase S3 connector to upload your dataset through the product interface.