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| language: |
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| - en |
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| license: apache-2.0 |
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| base_model: Qwen/Qwen3.6-27B |
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| library_name: peft |
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| tags: |
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| - perovskite |
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| - photovoltaics |
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| - scientific-reasoning |
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| - lora |
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| - openperov |
| - arxiv:2611.00633 |
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| --- |
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| # OpenPerov Flash |
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| OpenPerov Flash is the 27B expert language model in OpenPerov, developed for perovskite photovoltaics. It learns relationships between scientific questions, experimental evidence, mechanistic explanations and research decisions. |
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| **Paper:** [Evidence-grounded large language models guide perovskite solar cell design and optimization](https://arxiv.org/abs/2611.00633) |
| **Authors:** YanKai Chen, Zhi Wan, ZiHao Li and Tao Jing. |
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| **Code, PSM-Bench and evaluation records:** https://github.com/Yan-Kai-Chen/OpenPerov |
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| ## Single combined adapter |
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| The combined adapter is available. This repository distributes **one combined LoRA adapter**, approximately 1.40 GB, to load on Qwen3.6-27B. It does not distribute the 27B base weights or the intermediate training adapters. Users load the base model and this adapter once. |
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| The adapter consolidates all three training-stage LoRA updates by rank concatenation, with rank 48 and alpha 96. It preserves the original float32 adapter tensor blocks without rank truncation or SVD compression. `composition.json` records the artifact hash and numerical checks. This additive composition preserves the learned LoRA updates; sequential low-precision full-model merging can introduce different rounding. The exported combined adapter has not been separately re-benchmarked. |
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| ```python |
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| from transformers import Qwen3_5ForConditionalGeneration |
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| from peft import PeftModel |
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| base = Qwen3_5ForConditionalGeneration.from_pretrained( |
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| 'Qwen/Qwen3.6-27B', dtype='auto', device_map='auto' |
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| ) |
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| model = PeftModel.from_pretrained(base, 'ChenYanKai2002/OpenPerov-Flash') |
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| ``` |
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| Use the OpenPerov GitHub repository for generation settings and evaluation code. A local copy of the recorded base model may be supplied instead of the upstream model ID. |
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| ## OpenPerov Pro and evaluation |
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| OpenPerov Pro uses Flash as its answer backbone and adds evidence retrieval and local answer revision. Its source-retention selector (0.6B), scientific-relevance reranker (8B), and task-specific later-study evidence selector (8B) are available as separate final adapters in the OpenPerov collection. |
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| The paper's frozen Flash deployment scored 87.5659 on the 800-question PSM-Bench. Questions, criteria, answers, score components and evaluation protocols are available in the accompanying code repository. |
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| ## Data and license |
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| The adapter is released under Apache-2.0. The literature corpus, training examples, private retrieval index and evidence packets remain private. Upstream model licenses and notices are retained. |
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| ## Citation |
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| If you use OpenPerov models, code or evaluation resources, please cite the paper: |
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| ```bibtex |
| @misc{chen2026openperov, |
| title = {Evidence-grounded large language models guide perovskite solar cell design and optimization}, |
| author = {YanKai Chen and Zhi Wan and ZiHao Li and Tao Jing}, |
| year = {2026}, |
| eprint = {2611.00633}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.AI}, |
| doi = {10.48550/arXiv.2611.00633}, |
| url = {https://arxiv.org/abs/2611.00633} |
| } |
| ``` |
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