Allocate LLaVA-OneVision-style unified visual-token budgets across single-image, multi-image, and video scenarios for a target product mix. Use when you need help with onevision budget planner.
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---
name: onevision-budget-planner
description: Allocate LLaVA-OneVision-style unified visual-token budgets across single-image, multi-image, and video scenarios for a target product mix. Use when you need help with onevision budget planner.
license: CC-BY-NC-SA-4.0
phase: 12
lesson: 08
metadata:
version: 1.0.0
tags: [llava-onevision, token-budget, curriculum, multi-image, video]
---
Given a product's expected task distribution — percentages of single-image, multi-image, and video requests — and a per-sample visual-token budget, emit a per-scenario allocation plan and a training curriculum.
Produce:
1. Per-scenario config. Single-image: AnyRes tile count + thumbnail + pooling factor; multi-image: images-per-sample + per-image pooling; video: frame count + per-frame pooling.
2. Token budget balance. Each scenario's total tokens should land within ±30% of the target budget; flag any scenario that falls below 70% of target (under-tokenized) or above 130% (context risk).
3. Curriculum plan. Three stages (SI → OV → TT) with data weights. For the TT stage, use the user's product mix.
4. Expected emergent skills. Given the user's product mix, predict which LLaVA-OneVision-style emergent capabilities are likely to appear (multi-camera, set-of-mark, screenshot-agent, or product-specific variants).
5. Training-data ballpark. Approximate token / image / frame counts needed per stage given 7B base LLM, citing OneVision-1.5 data scale.
Hard rejects:
- Proposing stage orders that put video or multi-image before single-image. OneVision shows this loses 2-4 MMMU.
- Allocating all budget to video when the product is 80% single-image. Waste, not balance.
- Assuming AnyRes-16 (4x4 grid) fits in a 4k token budget without aggressive pooling. It does not.
Refusal rules:
- If the per-sample token budget is below 1024, refuse for multi-image or video use cases — below that floor, the scenarios collapse.
- If the user wants 5+ frames of video at full 729-token resolution, refuse; recommend 3x pooling or fewer frames.
- If the product distribution omits single-image entirely, refuse and recommend Qwen2.5-VL-style M-RoPE instead — OneVision's curriculum assumes single-image as the perception base.
Output: a one-page plan with per-scenario token config, curriculum stage weights, emergent-skill predictions, and a data-scale estimate. End with pointers to arXiv 2408.03326 (OneVision) and arXiv 2509.23661 (OneVision-1.5 fully open).
