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Business & Finance Fine-Tuning Dataset

Business strategy, finance, economics, entrepreneurship

Fine-tune a model on this dataset

$29 for the dataset. Training is priced separately by model, starting at $47.

What is in this dataset

956 instruction-response pairs covering business strategy, finance, economics, entrepreneurship. Each pair is a realistic question with a full written answer, in the JSONL format used for supervised fine-tuning. No synthetic filler, no duplicated rows.

Example pairs

Real rows from the dataset, shown in full.

I'm looking at a company's income statement and balance sheet. Net income is $5M, but operating cash flow is only $2M. Accounts receivable increased by $3M and inventory by $1.5M. What's the likely story here?

The divergence suggests the company is booking profit but not collecting cash efficiently. The $5M net income is partly 'paper profit' from accrual accounting. The $3M AR increase means customers are paying slower or sales are growing on credit. The $1.5M inventory build could indicate overstocking or unsold goods. This strains liquidity—despite profitability, the firm may need to borrow to fund working capital. Management should tighten credit terms or improve inventory turnover. A healthy company should have operating cash flow closer to net income over time.

Can you explain EBITDA in plain terms and why it's used in valuation? Give an example.

EBITDA stands for Earnings Before Interest, Taxes, Depreciation, and Amortization. It measures a company’s operating profitability by stripping out financing decisions, tax environments, and non-cash accounting charges (depreciation/amortization). Investors use it to compare firms across industries or capital structures. Example: Company A has net income of $10M, interest expense $2M, taxes $3M, depreciation $4M, amortization $1M. EBITDA = $10M + $2M + $3M + $4M + $1M = $20M. This shows the core business generated $20M in cash-like earnings before debt costs and tax. However, EBITDA ignores capital expenditure needs—a capital-intensive firm may appear healthier than it actually is.

I'm drafting a business plan for a subscription-based meal kit startup. What should the key sections be, and what's critical for each?

Your business plan should have these sections: 1) Executive Summary—one-page pitch: problem (convenient, healthy meals), solution (weekly kits), target market (busy urban professionals), revenue model ($X/month subscription). 2) Company Description—mission, legal structure, location. 3) Market Analysis—size of meal kit industry ($X billion, growing Y%), key competitors (Blue Apron, HelloFresh), your differentiation (local ingredients, diet-specific plans). 4) Product/Service—menu variety, sourcing, delivery logistics. 5) Marketing & Sales—channels (social media, influencer partnerships), customer acquisition cost (CAC) estimate. 6) Operational Plan—supply chain, kitchen facilities, tech…

Who this is for

Fintech tools and assistants that discuss financial statements and strategy.

Which models this works with

The dataset is plain JSONL, so it works with any instruction-tuned open model. On Rebiha you can train it directly on:

Training uses LoRA, or QLoRA on a 4-bit base for larger models. Your base model's weights are never modified — training produces a separate adapter.

How it works

  1. Pick this dataset and a base model.
  2. Rebiha runs the job on a managed GPU. You set up nothing.
  3. Download your model: a ready-to-run GGUF, plus developer assets (adapter, tokenizer, configs).

Fine-tuning or RAG?

Use RAG when the model needs facts that change — your prices, your documents, today's inventory. Use fine-tuning when you want consistent style and structure without prompting for it every time. This dataset teaches the shape of a good answer in this domain, not facts to look up.

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