Instructions to use EdisonScientific/MarkushGlyph with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use EdisonScientific/MarkushGlyph with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-2B-Base") model = PeftModel.from_pretrained(base_model, "EdisonScientific/MarkushGlyph") - Notebooks
- Google Colab
- Kaggle
MarkushGlyph
Installing the code
The commands below use the glyph package. Install it from the code repository:
git clone https://github.com/EdisonScientific/glyph
cd glyph
uv venv && uv sync --extra markush # or: pip install -e ".[markush]"
See the repo's README.md (quickstart + inference) and REPRODUCE.md (frozen eval contract).
Markush chemical structure recognition: image -> CXSMILES. A LoRA adapter (r=128, alpha=128) over
Qwen/Qwen3.5-2B-Base.
Results - IP5-M (n=878, official DS4SD paired scoring contract)
| System | Accuracy |
|---|---|
| MarkushGrapher-2 (baseline) | 467/878 = 53.19% |
| MarkushGlyph - greedy | 511/878 = 58.20% |
| MarkushGlyph - MV@8 | 532/878 = 60.59% |
Usage
python -m glyph.markush.eval.fast_eval \
--checkpoint EdisonScientific/MarkushGlyph --base-model Qwen/Qwen3.5-2B-Base --eval-data <eval_ip5m.jsonl> ...
Scored with the pinned DS4SD get_scores (official contract). See the code repository's REPRODUCE.md.
License: pending. Citation: to be added on publication.
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