Browse categories
Explore
Fiverr Pro
English
$
USD
I will create high quality training datasets from your documents for llm fine tuning
AI Training Data Specialist Documents to Fine Tuning Datasets
Founder of UMELLE, a custom software company serving the insurance and finance sectors. I build AI-powered document intelligence systems and create training datasets from business documents for LLM fi...
About this Gig
Message me before ordering so I can confirm your documents fit your chosen package.
I create multi-angle training datasets from your business documents that teach LLMs to actually reason about your domain.
HOW IT WORKS:
Send me your PDFs, Word docs, or policy manuals. I generate pairs per document chunk across three reasoning angles:
Factual: "What types of water damage are excluded under Section 4?"
Conditional: "If a laptop is stolen while being used for freelance work, is it covered?"
Exclusion: "What is NOT covered when annual revenue exceeds $50,000?"
Every pair is verified against the source text, then I review for accuracy before delivery.
WHAT YOU GET:
- Alpaca-format JSONL file ready for any fine-tuning pipeline (Unsloth, LLaMA Factory, OpenAI, etc.)
- Multi-angle pairs (factual, conditional, and exclusion reasoning)
- Cross-document synthesis pairs connecting knowledge across related files
- 2-3x more pairs per chunk than single-question competitors
BEST FOR:
Insurance, legal, compliance, product documentation, corporate
Get the full model: https://www.fiverr.com/s/Ld5qPg4
Programming Language:
Python
Data Type:
Text
AI Engine:
GPT
•
DeepSeek
•
Llama
•
Langchain
•
PyTorch
FAQ
What format is the dataset delivered in?
Alpaca-format JSONL — the industry standard for LLM fine-tuning. Each entry has instruction, input, and response fields. Works directly with Unsloth, LLaMA Factory, Axolotl, OpenAI fine-tuning API, and any HuggingFace-compatible pipeline.
What types of documents do you work with?
Any text-heavy business document: insurance policies, legal contracts, compliance manuals, product documentation, employee handbooks, healthcare protocols, corporate SOPs, technical manuals.
How many QA pairs will I get?
Typically 2-3 verified pairs per document chunk. A 10-page PDF usually produces 40-80 high-quality pairs. The exact count depends on document density — policy documents with many conditions and exclusions produce more pairs than simple narrative text.
What makes your datasets different from other sellers?
Three things. First, multi-angle generation — each chunk produces factual, conditional, AND exclusion reasoning pairs. Second, cross-document synthesis — pairs that connect knowledge across related documents. Third, every pair is verified and manually reviewed against the source text before delivery
Can you also fine-tune the model for me?
This gig covers dataset creation only. Message me to discuss fine-tuning options.
