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Lead Qualification & Sales Intake Fine-Tuning Dataset

Qualifying leads, intake conversations, and moving prospects to sale.

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

896 instruction-response pairs covering qualifying leads, intake conversations, and moving prospects to sale. 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.

A new lead just filled out our contact form with just their name and email. How should I respond to engage them without being too pushy?

Thank them for reaching out and acknowledge their interest. Introduce yourself briefly, then ask a low-friction question to understand their context, like: 'Thanks for connecting! To make sure I share the most relevant info, could you tell me a bit about what prompted you to reach out?' This opens the conversation without assuming their needs.

I have a lead who says they're interested, but I'm not sure if they fit our target market. What's the best way to qualify them without making them feel interrogated?

Start by framing your questions as helpful discovery. Ask: 'To see if our solution is a good fit for your situation, could you share a little about your company's size and primary goals right now?' Follow up with specifics like budget range or timeline only if they seem open. Keep the tone collaborative, not checklist-like.

How do I ask discovery questions that uncover the real pain points of a lead without sounding scripted?

Use open-ended, situational questions tied to their industry or role. For example: 'What's the biggest challenge you're facing with [their area] that led you to look for a solution now?' Then listen for emotional language (e.g., 'frustrated,' 'swamped') and dig deeper: 'Can you tell me more about how that impacts your daily work?' This builds trust and reveals genuine needs.

Who this is for

Sales tools that qualify inbound leads and run intake conversations.

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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