# Data Preparation

Preparing your data correctly is essential for successful training with Megatron Core.

## Data Format

Megatron Core expects training data in JSONL (JSON Lines) format, where each line is a JSON object:

```json
{"text": "Your training text here..."}
{"text": "Another training sample..."}
{"text": "More training data..."}
```

## Preprocessing Data

Use the `preprocess_data.py` tool to convert your JSONL data into Megatron's binary format:

```bash
python tools/preprocess_data.py \
    --input data.jsonl \
    --output-prefix processed_data \
    --tokenizer-type HuggingFaceTokenizer \
    --tokenizer-model /path/to/tokenizer.model \
    --workers 8 \
    --append-eod
```

### Key Arguments

| Argument | Description |
|----------|-------------|
| `--input` | Path to input JSON/JSONL file |
| `--output-prefix` | Prefix for output binary files (.bin and .idx) |
| `--tokenizer-type` | Tokenizer type (`HuggingFaceTokenizer`, `GPT2BPETokenizer`, etc.) |
| `--tokenizer-model` | Path to tokenizer model file |
| `--workers` | Number of parallel workers for processing |
| `--append-eod` | Add end-of-document token |

## Output Files

The preprocessing tool generates two files:
- `processed_data.bin` - Binary file containing tokenized sequences
- `processed_data.idx` - Index file for fast random access

## Using Preprocessed Data

Reference your preprocessed data in training scripts:

```bash
--data-path processed_data \
--split 949,50,1  # Train/validation/test split
```

## Common Tokenizers

### HuggingFace Tokenizers

```bash
--tokenizer-type HuggingFaceTokenizer \
--tokenizer-model /path/to/tokenizer.model
```

### GPT-2 BPE Tokenizer

```bash
--tokenizer-type GPT2BPETokenizer \
--vocab-file gpt2-vocab.json \
--merge-file gpt2-merges.txt
```