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How to Fine-Tune an LLM for AI Video Prompting

Writing structured prompts for AI video generation — camera movement, motion, temporal consistency, duration, and audio across current text-to-video models.

• 640 instruction-response pairs
• Format JSONL
• Pack AI Filmmaking
• Dataset $39 one-time

$39 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 Video Prompt Mastery, the dataset focuses on writing structured prompts for ai video generation — camera movement, motion, temporal consistency, duration, and audio across current text-to-video models. 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:

• writing the four-part camera-motion formula: movement and speed, subject anchor, what's revealed, what stays locked; writing a prompt that specifies duration within a model's hard architectural clip-length limit; writing prompts that specify native audio generation versus planning for a separate audio pass; troubleshooting temporal flicker or morphing in a generated clip through prompt adjustment; writing model-specific negative prompts for artifact prevention like waxy skin, rubbery motion, or style drift
• avoiding contradictory camera-movement instructions stacked in a single prompt; writing an image-to-video prompt that constrains motion from a locked starting frame; writing prompts for multi-shot sequences with consistent temporal pacing; specifying physically plausible motion to avoid implausible or unnatural movement; writing prompts for slow-motion or time-lapse effects
• adapting a single scene concept across different video models' preferred prompt dialects; writing dialogue-timing prompts for a two-character conversation scene; writing prompts for multi-character action sequences without losing individual identity; explaining the trade-off between text-to-video and image-to-video for a given shot; writing prompts that specify a locked lighting setup and mood held across a clip's full duration
• diagnosing why a video generation produced a generic default like a static shot with no sound; writing prompts with camera-movement choices suited to vertical 9:16 versus widescreen 16:9 formats; explaining a current model's duration and audio-support limits when recommending an approach for a shot

How Rebiha prepared the training material

This page contains 640 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

640 instruction-response pairs covering writing structured prompts for ai video generation — camera movement, motion, temporal consistency, duration, and audio across current text-to-video models. 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
• Multi-Turn Conversation — 466 pairs
• Rewriting & Editing — 854 pairs
• Summarization — 781 pairs

Start fine-tuning