Downloads · 30 days
0
junwoo00/Llama-3.1-8B-MR-MoL
Llama-3.1-8B-MR-MoL is a machine learning model from junwoo00. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as llama3.1.
Downloads · 30 days
0
Access
Public
Updated Sep 2, 2026
Repo size
847 MB
Likes
0
Public
Click a slice to open those files.
.pt847 MB · 100%
From the Hugging Face model README
Built with Llama.
Trained checkpoints for MR-MoL (Multi-Granular Rationale-Guided Molecular LLM for Property Prediction, arXiv:2608.10480).
| file | size | contents |
|---|---|---|
stage1_epoch10.pt | 400 MB | Stage 1 |
final.pt | 447 MB | Stage 2 |
Both files are needed to evaluate. The graph encoder is frozen during
Stage 2, so it is not stored in final.pt, it is read from
stage1_epoch10.pt.
The Llama-3.1-8B-Instruct base weights are not included and are not merged
into these files. final.pt carries LoRA adapters that are applied at load time. The base
weights are gated and must be obtained separately from
meta-llama/Llama-3.1-8B-Instruct.
git clone https://github.com/skku-aihclab/MR-MoL.git && cd MR-MoL
uv sync
hf download junwoo00/Llama-3.1-8B-MR-MoL stage1_epoch10.pt --local-dir checkpoints/trained/stage1
hf download junwoo00/Llama-3.1-8B-MR-MoL final.pt --local-dir experiments/stage2
Evaluation reads the rationale-augmented data, so fetch the dataset too:
hf download junwoo00/MR-MoL --repo-type dataset \
--include "data_prep/*" "rationale/*" --local-dir .
uv run python scripts/run_stage2.py --config configs/stage2.yaml --eval-only
@article{park2026multi,
title={Multi-Granular Rationale-Guided Molecular LLM for Property Prediction},
author={Park, Junwoo and Shin, Minyoung and Lee, Cheol Soon and Lee, Sujee},
journal={arXiv preprint arXiv:2608.10480},
year={2026}
}
These weights are a fine-tune of Llama-3.1-8B-Instruct and are governed by the
Llama 3.1 Community License — see LICENSE in this
repository, and NOTICE for the required attribution. Your use must also comply
with Meta's Acceptable Use Policy.
The source code in the GitHub repository is separately licensed under Apache-2.0.
Builds on MolCA (Liu et al., EMNLP 2023), Mole-BERT (Xia et al., ICLR 2023),
SME (Wu et al., Nature Communications 2023), and BLIP-2 / LAVIS (Li et al.,
ICML 2023). See NOTICE in the code repository for what each contributed.