Update README.md

README.md

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 license: apache-2.0
 datasets:
 - JH976/Perovskite-RL
----
+---
+
+---
+license: other
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+base_model: Qwen/Qwen3-32B
+tags:
+- perovskite
+- materials-science
+- solar-cells
+- additive-engineering
+- sft
+- grpo
+- qwen3
+---
+
+# Perovskite-RL
+
+Perovskite-RL is a domain-adapted large language model for perovskite solar-cell additive engineering. It is trained to reason about additive molecules, defect passivation, crystallization modulation, interfacial protection, ion migration, electronic effects, and stability-related mechanisms.
+
+## Model Details
+
+- **Base model:** Qwen3-32B
+- **Training pipeline:** supervised fine-tuning followed by GRPO reinforcement learning
+- **Training framework:** ms-swift / Transformers / PEFT
+- **Primary domain:** perovskite photovoltaics and molecular additive design
+
+## Training Data
+
+Perovskite-RL was trained using curated perovskite-additive reasoning data.
+
+- **SFT training set:** 90,749 examples
+- **SFT validation set:** 1,000 examples
+- **GRPO dataset:** 5,800 examples
+
+The data include literature-derived mechanism reasoning, molecular-property reasoning, and additive-selection tasks.
+
+## Training Procedure
+
+### SFT
+
+The base model was first fine-tuned with LoRA on instruction-response examples for perovskite additive reasoning.
+
+Key settings:
+
+- LoRA fine-tuning
+- Learning rate: `3e-5`
+- Epochs: `2`
+- Batch size per device: `1`
+- Gradient accumulation: `16`
+- Scheduler: cosine
+- Seed: `42`
+
+### GRPO
+
+The SFT model was further optimized with GRPO using reward signals designed for mechanism-aware additive selection.
+
+Key settings:
+
+- GRPO
+- LoRA rank: `16`
+- LoRA alpha: `32`
+- LoRA dropout: `0.05`
+- Learning rate: `2e-5`
+- Epochs: `1`
+- Number of generations: `8`
+- Max length: `8192`
+- Reward focus: answer correctness, format compliance, content recall, and reasoning quality
+
+## Evaluation
+
+On the mechanism-consistency benchmark:
+
+| Model | Accuracy |
+|---|---:|
+| Perovskite-RL | 25 / 32, 78.1% |
+
+The benchmark tests whether a model can identify paper-specific mechanistic explanations rather than relying only on generic materials-science priors.
+
+## Intended Use
+
+Perovskite-RL is intended for research use in:
+
+- perovskite additive mechanism analysis
+- molecular additive hypothesis generation
+- mechanistic descriptor generation
+- literature-based reasoning for perovskite photovoltaics
+- assisting computational screening workflows
+
+## Limitations
+
+- The model is not a substitute for experimental validation.
+- Generated additive suggestions may be chemically invalid, commercially unavailable, or experimentally unsuitable.
+- The model may overstate mechanistic confidence when evidence is incomplete.
+- Use outputs as hypotheses, not final scientific conclusions.
+
+## Citation
+
+Citation information will be added after the manuscript is publicly available.