Principle:PacktPublishing LLM Engineers Handbook Finetuning Dataset Preparation
| Field | Value |
|---|---|
| Principle Name | Finetuning Dataset Preparation |
| Category | Loading and Formatting Datasets for LLM Fine-tuning |
| Workflow | LLM_Finetuning |
| Repo | PacktPublishing/LLM-Engineers-Handbook |
| Implemented by | Implementation:PacktPublishing_LLM_Engineers_Handbook_HuggingFace_Load_Dataset |
Overview
Dataset Preparation for Fine-tuning encompasses the process of loading datasets from external sources (typically HuggingFace Hub), combining multiple data sources, and transforming them into the specific text format required by the training objective. This is a critical preprocessing step that directly impacts the quality and effectiveness of fine-tuning.
Theory
The Importance of Data Formatting
LLM fine-tuning is highly sensitive to the format of training data. Different training objectives require different data structures:
| Training Objective | Required Data Format | Example |
|---|---|---|
| SFT (Supervised Fine-Tuning) | Instruction-response text pairs | Alpaca format: ### Instruction: ... ### Response: ...
|
| DPO (Direct Preference Optimization) | Prompt with chosen and rejected responses | {"prompt": ..., "chosen": ..., "rejected": ...}
|
Dataset Concatenation
A common strategy in fine-tuning is to combine multiple datasets to increase training data diversity:
- Primary dataset: Domain-specific data (e.g., the project's own
llmtwindataset). - Supplementary dataset: General-purpose instruction data (e.g.,
mlabonne/FineTome-Alpaca-100k) to prevent catastrophic forgetting and improve general capabilities.
Concatenation is performed after loading but before formatting, ensuring all data passes through the same formatting pipeline.
The Alpaca Format
For SFT, the Alpaca format is a widely adopted template for structuring instruction-following data:
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction text}
### Response:
{response text}
The formatting function applies this template to each example in the dataset via dataset.map(), creating a single "text" field that the SFT trainer consumes.
Train-Test Split
After formatting, the dataset is split into train and test partitions (typically 95/5). The test split is used for evaluation during training to monitor for overfitting and track validation loss.
Data Pipeline Architecture
HuggingFace Hub Local Processing
+-------------------+ +----------------------------+
| Dataset A | load | |
| (llmtwin) | ----------> | Concatenate datasets |
+-------------------+ | | |
| v |
+-------------------+ | Apply format template |
| Dataset B | load | (Alpaca / DPO format) |
| (FineTome-100k) | ----------> | | |
+-------------------+ | v |
| Train/Test split (95/5) |
| | |
| v |
| DatasetDict |
| {"train": ..., "test": .} |
+----------------------------+
When to Use
- When preparing training data in the correct format for SFT or DPO fine-tuning.
- When combining multiple data sources (domain-specific + general-purpose) for training.
- When the raw dataset columns do not match the expected input format of the trainer.
When Not to Use
- When using pre-formatted datasets that already match the trainer's expected format.
- When the training framework handles formatting internally (some newer trainers accept raw columns).
Key Considerations
- Data Quality: The quality of formatted data directly impacts model performance. Ensure instruction-response pairs are coherent and well-written.
- Format Consistency: All examples in a dataset must follow the same format template. Mixing formats can confuse the model.
- Dataset Size: The supplementary dataset size should be balanced with the primary dataset to avoid dominating the training signal.
- Tokenization Alignment: The formatted text must be compatible with the model's tokenizer (special tokens, chat templates).