LLaVA-OneVision-2 is an openly released multimodal AI model and training framework that processes images, long-form video, and spatial information. Researchers use it to train, evaluate, and reproduce vision-language models with the project’s released data, encoders, checkpoints, and training records. The catalogue skills support work with this model and its training resources.
Borrowing it
Nothing to install: this file belongs to EvolvingLMMs-Lab/LLaVA-OneVision-2. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/EvolvingLMMs-Lab/LLaVA-OneVision-2/main/.opencode/skills/llava-onevision2-consistency/SKILL.mdgit clone --depth 1 https://github.com/EvolvingLMMs-Lab/LLaVA-OneVision-2Wrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/evolvinglmms-lab/llava-onevision-2/llava-onevision2-consistency)<a href="https://agentmods.dev/skills/evolvinglmms-lab/llava-onevision-2/llava-onevision2-consistency"><img src="https://agentmods.dev/badge/skills/evolvinglmms-lab/llava-onevision-2/llava-onevision2-consistency/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/evolvinglmms-lab/llava-onevision-2/llava-onevision2-consistency"><img src="https://agentmods.dev/badge/skills/evolvinglmms-lab/llava-onevision-2/llava-onevision2-consistency.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00032 | $0.05119 |
| Opus 5 | $0.00016 | $0.02559 |
| Sonnet 5 | $0.00006 | $0.01024 |
| Haiku 4.5 | $0.00003 | $0.00512 |
Grade A, and why
llava-onevision2-consistency scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 372 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose / 用途
Use this skill when validating whether a HuggingFace checkpoint and a Megatron/MCore checkpoint are behaviorally consistent in this repository.
在这个仓库里，需要验证 HuggingFace checkpoint 和 Megatron/MCore checkpoint 是否行为一致时，使用这个 skill。
There are two test systems in this repo:
本仓库有两套测试系统：
1. pytest test suite (recommended / 推荐)
-
tests/consistency/conftest.py— session fixtures, HF→mcore conversion, Megatron initialization -
tests/consistency/test_model_consistency.py— 6 integration tests -
tests/consistency/test_consistency_utils.py— 10 utility functions + 11 unit tests -
tests/consistency/run_consistency_tests.sh— shell wrapper with auto-conversion + torchrun -
tests/consistency/conftest.py—— session 级 fixture、HF→mcore 转换、Megatron 初始化 -
tests/consistency/test_model_consistency.py—— 6 个集成测试 -
tests/consistency/test_consistency_utils.py—— 10 个工具函数 + 11 个单元测试 -
tests/consistency/run_consistency_tests.sh—— shell 入口，自动转换 + torchrun
2. Legacy monolithic script (reference only / 仅供参考)
examples/llava_onevision2/check_model_consistency.shexamples/llava_onevision2/check_model_consistency.py
仅作历史参考，新的工作请用 pytest 套件。
Architecture / 架构
Direction: HF → mcore
The pytest suite assumes only the HF checkpoint exists as input. The mcore checkpoint is generated automatically via conversion.
pytest 测试套件假设 只有 HF checkpoint 作为输入。mcore checkpoint 通过转换 自动生成。
HF auto-model (input)
→ convert_4b_hf_to_mcore.sh (auto-run by conftest.py or run_consistency_tests.sh)
→ mcore checkpoint (generated)
→ both models loaded → 6 tests run
Direction: mcore → HF (reverse / deploy / round-trip) / 反向：mcore → HF（部署 / 回环）
The pytest suite does not exercise the reverse path. For the p14m2 variant, two scripts ship for this:
| Script | Use case |
|---|---|
examples/llava_onevision2/convert/convert_4b_p14m2_mcore_to_hf.sh |
Single mcore→HF pass (deploy, inference debug) |
examples/llava_onevision2/convert/convert_4b_p14m2_mcore_to_release.sh |
Re-shard mcore via HF round-trip (change TP/PP without retraining) |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 372 lines · 32 tokens per session scan A f4767c5faae1
llava-onevision2-consistency is a skill published in the GitHub repository EvolvingLMMs-Lab/LLaVA-OneVision-2 (1,200 stars, last pushed yesterday), licensed Apache-2.0. It adds 32 tokens to every session and 5,119 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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