To prepare and format training data for fine-tuning a foundation model on AWS Bedrock using JSONL files, you’ll need to structure your data into prompt-completion pairs. Each line in the JSONL file should be a standalone JSON object containing a “prompt” (input text) and a “completion” (desired output). For example, a line might look like: {"prompt": "Translate to French: Hello", "completion": "Bonjour"}. Bedrock requires this format to map inputs to outputs during training. Ensure that every training example is on its own line, with no commas separating entries, as JSONL is distinct from a JSON array. Use UTF-8 encoding and validate line breaks to avoid parsing errors. Check Bedrock’s documentation for specifics like file size limits or reserved keys to ensure compatibility.
Next, preprocess your data to improve model performance. Clean the text by removing irrelevant characters, normalizing whitespace, and ensuring consistency in prompts and completions. For instance, if your task is classification, standardize labels (e.g., always use “positive” instead of “pos”). Split your data into training and validation sets (e.g., 90/10) to evaluate model performance. If your dataset is small, consider techniques like data augmentation (rephrasing prompts) or oversampling underrepresented classes. Tokenization alignment is also critical—ensure your text aligns with the model’s tokenizer (e.g., avoiding mid-word splits). Tools like the json library in Python can help automate formatting, and AWS Glue or custom scripts can handle large-scale preprocessing.
Finally, validate your JSONL files before uploading to Bedrock. Use a linter or a script to check for syntax errors, missing keys, or inconsistent structures. For example, run a Python script that loads each line with json.loads() and verifies the presence of “prompt” and “completion” keys. Test a subset of data by fine-tuning a small model to catch issues like misaligned pairs or overfitting. AWS CLI tools or the Bedrock console can help upload and validate files. After testing, monitor training metrics like loss or accuracy to ensure the data is effective. If errors arise, revisit preprocessing steps—common fixes include balancing dataset diversity or adjusting prompt-completion ratios. Properly formatted and validated data ensures the model learns the intended patterns efficiently.