Transform cleaned enterprise data into fine-tuning-ready JSONL and vector-ready Parquet datasets. Structured for Axolotl, LLaMA-Factory, Unsloth, and every major RAG framework — entirely on-premise.
LLM dataset preparation is the process of taking cleaned, validated data and structuring it into the precise formats that fine-tuning frameworks and retrieval-augmented generation (RAG) pipelines require. It is the bridge between raw data cleaning and the actual training or inference infrastructure.
Even perfectly clean data in the wrong format will fail at training time. Fine-tuning frameworks are strict about schema: field names, message role conventions, token count limits per example, and instruction/response pairing format all need to be exactly right. A single malformed record can corrupt an entire training run.
VaultData's dataset preparation service handles schema design, format conversion, train/validation/test splitting, token budget management, and vector embedding preparation — all running inside your infrastructure with zero external dependencies.
The standard format for supervised fine-tuning across all major frameworks. We support every major schema:
Columnar storage optimized for large-scale embedding generation and vector database ingestion:
We map your cleaned source data fields to the target fine-tuning schema. For instruction datasets, we design system prompts and user/assistant pairing logic based on your task type (summarization, classification, Q&A, code generation). For RAG, we define chunk boundaries, overlap strategy, and metadata schema.
Every record is tokenized using the target model's actual tokenizer (e.g., tiktoken for GPT-family, sentencepiece for LLaMA-family). Records exceeding the context window limit are split, truncated, or flagged based on your configured strategy. Token distribution histograms are included in the output report.
Stratified splits are generated to ensure balanced representation of topics, sources, and response lengths across all three partitions. Default split is 90/5/5; custom ratios are supported. Contamination checking prevents any near-duplicate from appearing in both train and eval sets.
For retrieval pipelines, documents are chunked using a semantic boundary strategy (paragraph-aware, sentence-aware, or fixed-token sliding window with configurable overlap). Chunk size is calibrated to your embedding model's optimal input length (typically 256–512 tokens for most BERT-class encoders).
After structuring, each record receives a composite quality score based on instruction/response length ratio, response informativeness, and coherence. Low-scoring records are flagged for review before export, preventing them from polluting your training distribution.
The final dataset is validated by loading a sample through the target framework's data loader (Axolotl, LLaMA-Factory, Hugging Face datasets) inside your environment. Zero-error validation is confirmed before handoff — no post-processing surprises at training time.
Output validated for direct loading by:
Parquet output tested with:
Schema and tokenizer support for: