Instructions to use luca0621/polyedit-repo-retrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use luca0621/polyedit-repo-retrained with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-3B-Instruct") model = PeftModel.from_pretrained(base_model, "luca0621/polyedit-repo-retrained") - Notebooks
- Google Colab
- Kaggle
Add portable adapter metadata and model card
Browse files
README.md
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---
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base_model: Qwen/Qwen2.5-3B-Instruct
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library_name: peft
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tags:
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- polymers
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- molecular-optimization
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- reinforcement-learning
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- repo
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- polyedit
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---
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# PolyEdit RePO-polymer-retrained
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This LoRA adapter uses the public RePO `XGRPOTrainer` with PolyEdit polymer references.
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It is **not** an upstream RePO checkpoint: the official repository publishes training
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code and recipes but no trained weights.
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Training uses all eight properties with equal property mass, only PolyEdit train
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components, Qwen2.5-3B-Instruct, four sampled generations per prompt, a verifiable reward
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combining two-anchor validity, structural locality, and a train-only property verifier,
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the reference-guidance loss, and KL regularization. Exact training and full-test metrics
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are recorded in the linked repository and `training_meta.json`.
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Upstream RePO code: <https://github.com/tmlr-group/RePO>
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PolyEdit implementation and record-level evaluation: <https://github.com/promotion-kim/POLYEDIT/tree/tsyou/balanced-polymer-baseline-eval>
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