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How to Fine-Tune an LLM for Multi-Turn Conversations

Maintaining context, coherence, and natural flow across multi-turn dialogues rather than single-shot exchanges.

• 466 instruction-response pairs
• Format JSONL
• Pack General Assistant
• Dataset $19 one-time

$19 for the dataset. Training is priced separately by model, starting at $47.

What can this kind of fine-tuning teach an LLM?

A general-purpose model may already know some of this subject. Fine-tuning serves a different purpose: it repeatedly demonstrates the kinds of requests, response patterns, terminology, decisions and workflows you want the model to handle more consistently.

For Multi-Turn Conversation, the dataset focuses on maintaining context, coherence, and natural flow across multi-turn dialogues rather than single-shot exchanges. Fine-tuning shapes recurring model behavior; it does not turn the model into a live database or guarantee correctness on every new case.

What training data should cover

Useful supervised fine-tuning data should resemble the work you expect the model to perform. This Rebiha dataset's domain definition is organized around 18 coverage areas:

• maintaining context across a topic that evolves over several turns; correctly resolving a pronoun or reference to something said earlier; handling a user who changes the subject abruptly; a multi-turn troubleshooting conversation that narrows down an issue; a multi-turn planning conversation that refines a plan
• handling a user correcting themselves mid-conversation; a conversation where the user asks a follow-up that depends on the prior answer; maintaining a consistent persona or tone across turns; a multi-turn negotiation or back-and-forth decision process; handling a user who returns to an earlier point later
• a conversation that builds a piece of writing over several turns; clarifying a misunderstanding that emerged from an earlier turn; a multi-turn conversation with increasing specificity; handling interruptions and returning to the original topic; a conversation where preferences stated early affect later turns
• summarizing the conversation so far when asked; a multi-turn conversation involving a decision with trade-offs; handling a user who asks 'what did I just say' or references history

How Rebiha prepared the training material

This page contains 466 instruction-response pairs from the existing Rebiha dataset. Its domain definition was designed around the coverage areas above so the training material spans multiple realistic tasks rather than one narrow prompt pattern.

Rebiha's broader dataset workflow uses structured quality review to help identify problems such as contradictions, unsupported claims, unsupported assumptions, calculation errors and other weak training examples. You can inspect real examples on this page before deciding whether the dataset fits your model and use case.

What is in this dataset

466 instruction-response pairs covering maintaining context, coherence, and natural flow across multi-turn dialogues rather than single-shot exchanges. Each pair is a realistic instruction with a full written response, 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.

Who this is for

Builders who want their fine-tuned model to reliably perform this specific capability, as part of a broader assistant or agent.

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:

• Qwen — Qwen3.8, Qwen3.6, Qwen3.5, Qwen3, Qwen2.5, Qwen2.5-Coder
• Granite — Granite 4.1 (3B, 8B, 30B)
• OLMo — OLMo 3, OLMo 3.1
• GLM — GLM-4
• Gemma, Phi, DeepSeek, Mistral, and Llama families

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 behavior and structure without prompting for it every time. This dataset teaches the shape of this capability, not facts to look up.

How managed fine-tuning works

1. Inspect the dataset examples and choose a compatible base model.
2. Rebiha runs the training job on managed GPU infrastructure using LoRA or QLoRA as appropriate.
3. Download portable model assets for your own workflow, including the available ready-to-run and developer outputs for the selected training path.

Fine-tuning or RAG?

Use fine-tuning when the objective is recurring behavior: response structure, domain patterns, terminology, workflow, or how the model approaches a class of requests. Use RAG when answers depend on changing or proprietary facts that should be retrieved at request time. The two approaches can also work together.

Know what goes in. Own what comes out.

You can inspect real examples before training, choose the model you want to adapt, and keep portable model assets when training is complete.

Related datasets

• General Instruction Following — 978 pairs
• Rewriting & Editing — 854 pairs
• Summarization — 781 pairs
• Mathematical Reasoning — 824 pairs

Start fine-tuning