Phoenix - Your AI AssistantTraining Dataset Preparation & JSONL Formatting for Fine-Tuning
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3 days
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What you get with this Offer
I will clean, structure, and format your raw training data into a fine-tuning ready JSONL dataset — with correctly structured prompt-completion or conversational message pairs, quality filtering to remove low-value examples, deduplication, length normalisation, and a validation pass confirming the dataset meets the format and quality requirements of your target fine-tuning platform. Training data quality is the single most important variable in fine-tuning outcomes; a model trained on poorly structured, inconsistent, or low-quality examples will reliably underperform the base model it was meant to improve.
The service covers up to 1,000 training examples, format conversion from your source format (CSV, Excel, JSON, plain text, or scraped content), system message and instruction formatting for chat fine-tuning, train/validation split, and a dataset quality report covering example length distribution, vocabulary consistency, label balance (for classification tasks), and flagged low-quality examples removed during cleaning.
This service suits developers and businesses who have raw data in an unstructured format and need it professionally prepared before submitting a fine-tuning job to OpenAI, Hugging Face, or another platform.
The service covers up to 1,000 training examples, format conversion from your source format (CSV, Excel, JSON, plain text, or scraped content), system message and instruction formatting for chat fine-tuning, train/validation split, and a dataset quality report covering example length distribution, vocabulary consistency, label balance (for classification tasks), and flagged low-quality examples removed during cleaning.
This service suits developers and businesses who have raw data in an unstructured format and need it professionally prepared before submitting a fine-tuning job to OpenAI, Hugging Face, or another platform.
What the Freelancer needs to start the work
Please share your raw training data (in any format — CSV, Excel, JSON, plain text, or document), describe the fine-tuning task (what the model should learn to do), confirm your target platform and model, and specify any formatting requirements or quality criteria you want applied during the cleaning process.