Principle:Axolotl ai cloud Axolotl Dataset Preparation
| Knowledge Sources | |
|---|---|
| Domains | Data_Preparation, NLP, Training_Pipeline |
| Last Updated | 2026-02-06 23:00 GMT |
Overview
A data pipeline pattern that loads, tokenizes, and formats training datasets from heterogeneous sources into a unified format suitable for language model fine-tuning.
Description
Dataset Preparation is the process of transforming raw text or structured data into tokenized sequences ready for model training. In LLM fine-tuning, this involves several stages: loading data from HuggingFace Hub or local files, applying prompt formatting templates (chat templates, instruction formats), tokenizing text into model-compatible token IDs, computing attention masks, and optionally splitting into train/eval sets.
The key challenge this solves is format heterogeneity: training data comes in many formats (Alpaca, ShareGPT, conversational, custom schemas) and must be normalized into a consistent tokenized format. Axolotl addresses this with a strategy pattern that selects the appropriate prompt formatter based on config, supporting 36+ prompt strategies.
Usage
Use this principle when you need to:
- Load training data from HuggingFace Hub, local JSONL/Parquet files, or S3 paths
- Apply chat template or instruction formatting to raw text
- Tokenize and prepare datasets for supervised fine-tuning (SFT)
- Compute total training steps for learning rate scheduler configuration
Theoretical Basis
Dataset preparation for LLM fine-tuning follows a pipeline pattern with these stages:
- Loading: Fetch data from sources (HuggingFace, local files, URLs)
- Formatting: Apply prompt templates to structure input/output pairs
- Tokenization: Convert text to token IDs with proper attention masks
- Splitting: Divide into train/eval sets with optional deduplication
- Packing: Optionally concatenate short sequences for GPU efficiency
Pseudo-code:
# Abstract dataset preparation algorithm
tokenizer = load_tokenizer(config)
raw_datasets = [load_dataset(spec) for spec in config.datasets]
formatted_datasets = [apply_prompt_strategy(ds, spec) for ds, spec in zip(raw_datasets, config.datasets)]
tokenized = [tokenize(ds, tokenizer) for ds in formatted_datasets]
merged = concatenate(tokenized)
train_set, eval_set = split(merged, config.val_set_size)
total_steps = compute_total_steps(len(train_set), config)
return TrainDatasetMeta(train_set, eval_set, total_steps)