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 complete balanced-test metrics
Browse files
README.md
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@@ -15,12 +15,20 @@ This LoRA adapter uses the public RePO `XGRPOTrainer` with PolyEdit polymer refe
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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,
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components, Qwen2.5-3B-Instruct, four sampled
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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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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, 4,096 examples from only
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PolyEdit train components, 256 optimizer steps, Qwen2.5-3B-Instruct, four sampled
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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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On all 8,176 balanced test requests, this adapter obtains 79.770% RDKit+TDC validity,
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51.248% two-anchor polymer validity, 33.745% changed outputs, 1.345% strict
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MIPS-retrained full-edit hits, and 0.489% observed-DFT strict full-edit hits at 24.352%
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DFT coverage. It does not outperform the polymer-adapted Molecular Optimization
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Transformer and should be treated as a reproducible RePO adaptation baseline rather than
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a claimed state-of-the-art result.
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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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