Instructions to use openbmb/MiniCPM-o-4_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/MiniCPM-o-4_5 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("openbmb/MiniCPM-o-4_5", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add LongShOTBench evaluation results
Add the LongShOTBench (NeurIPS 2026 Evaluations & Datasets Track) benchmark authors' published results for MiniCPM-o-4_5 on overall, core_perception, reasoning, information and multimodal. Each value is a rubric-weighted score on a 0-100 scale; higher is better.
LongShOTBench evaluates long-form omni-modal video understanding: 3,401 open-ended single- and multi-turn samples (4,882 QA turns) over 274 real-world videos (~41 min avg) that require combining visual, speech and ambient-audio evidence. Answers are graded by an LLM judge (Qwen3-14B) against human-verified, weighted criterion-level rubrics.
Source: https://longshot.cvmbzuai.com/leaderboard
Benchmark: https://huggingface.co/datasets/MBZUAI/longshot-bench
Paper: https://arxiv.org/abs/2512.16978