You will get a 100-Sample Fine-Tuning Dataset for your AI Model
Project details
Eliminate model hallucinations and schema breaks before deployment. I engineer high-precision, production-grade SFT datasets and DPO/RLHF preference pairs designed specifically for enterprise LLM fine-tuning and post-training.
What I Deliver:
• Grounded SFT Datasets: High-quality prompt/response chains and system instructions that enforce factual alignment and eliminate hallucination loops.
• Schema Integrity: Strict JSON and JSONL datasets validated against custom Pydantic models to prevent database and training pipeline failures.
• RLHF & DPO Preference Alignment: Curated Chosen vs. Rejected pairs to optimize reasoning trajectories, system constraints, and tone.
Whether you need 100 rows to audit your data pipeline or 1,000+ validated samples for fine-tuning, you get audit-ready, deterministic dataset files formatted directly for OpenAI API, Hugging Face, Unsloth, or custom training pipelines.
What I Deliver:
• Grounded SFT Datasets: High-quality prompt/response chains and system instructions that enforce factual alignment and eliminate hallucination loops.
• Schema Integrity: Strict JSON and JSONL datasets validated against custom Pydantic models to prevent database and training pipeline failures.
• RLHF & DPO Preference Alignment: Curated Chosen vs. Rejected pairs to optimize reasoning trajectories, system constraints, and tone.
Whether you need 100 rows to audit your data pipeline or 1,000+ validated samples for fine-tuning, you get audit-ready, deterministic dataset files formatted directly for OpenAI API, Hugging Face, Unsloth, or custom training pipelines.
AI Development Type
Deep Learning, Knowledge Representation, Model TuningAI Tools
Amazon SageMaker, MLflow, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$50
|
Standard
$150
|
Advanced
$350
|
|---|---|---|---|
| Delivery Time | 2 days | 3 days | 5 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | - | - | |
Detailed Code Comments | |||
Knowledge Graph | - | - | - |
Model Documentation | - | ||
Ontology | - | - | - |
Source Code | |||
Taxonomy | - |
About Mayank
AI Data Engineer | LLM Fine-Tuning, RLHF & Structured Dataset Curation
Muzaffarpur, India - 4:23 am local time
I help AI engineering teams and tech startups eliminate edge-case errors, hallucinations, and schema breakages by building high-accuracy, domain-specific datasets.
Off-the-shelf models struggle with complex domain rules and structured outputs. I bridge the gap by curating, cleaning, and formatting custom training data that aligns models directly to production requirements.
Core Services:
• Structured Fine-Tuning Data (SFT): Converting raw unstructured text/documents into JSON Schema & Pydantic-compliant training pairs.
• RLHF & Preference Scoring: Ranking model outputs, evaluating edge cases, and constructing reward-model datasets.
• Data Cleaning & Anonymization: Scrubbing noisy datasets, removing PII, and enforcing strict data type constraints.
• Domain-Specific Extraction (NER/OCR): Tailoring dataset pipelines for Fintech, Legal Tech, and HealthTech applications.
Technical Tooling: Python, JSON/JSONL, Pandas, Label Studio, OpenAI Fine-Tuning API, Hugging Face Datasets.
Ready to improve your model's accuracy? Send a message to discuss a 48-hour dataset evaluation or sample audit.
Steps for completing your project
After purchasing the project, send requirements so Mayank can start the project.
Delivery time starts when Mayank receives requirements from you.
Mayank works on your project following the steps below.
Revisions may occur after the delivery date.
Data Audit & Schema Alignment
Review raw client data, establish target JSON schema/Pydantic rules, and identify edge cases.
Dataset Curation & Validation
Clean, format, and structure dataset pairs with strict schema enforcement and QA checks.