GeomLlama-xyz is a fine-tune of Llama-3.1-8B-Instruct that generates 3D
molecular conformer geometries directly from a SMILES string, emitting each
structure as Cartesian XYZ coordinates (one element x y z line per atom). It
is one of two models from our paper; the companion model,
Llama-3.1-8B-GeomLlama-zmatrix,
emits Fenske–Hall Z-matrix internal coordinates instead.
The model was trained jointly ("hybrid") on GEOM-QM9 and GEOM-Drugs, so it
covers both small molecules and larger drug-like molecules with a single set of
weights.
Quick start
The model was trained in the Alpaca instruction format. Reproduce the exact
inference prompt used for the paper's numbers:
python
1import torch
2from transformers import AutoModelForCausalLM, AutoTokenizer
34model_id ="THGLab/Llama-3.1-8B-GeomLlama-xyz"5tok = AutoTokenizer.from_pretrained(model_id)6model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")78smiles ="Cc1cccc(CSc2nnnn2-c2ccccc2)c1"9prompt =(10"### Instruction:\n"11"You can generate accurate molecular coordinates from a prompt "12"containing a SMILES string.\n\n"13"### Input:\n"14"Generate a realistic equilibrium geometry for the molecule with the "15f"following SMILES string in xyz format: {smiles}\n\n"16"### Response:\n"17)1819inputs = tok(prompt, return_tensors="pt").to(model.device)20out = model.generate(**inputs, max_new_tokens=3072, do_sample=True, temperature=1.0, top_p=0.95)21print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
Sample many completions per SMILES (each is one candidate conformer) to build a
conformer ensemble. T=1.0, top_p=0.95 and T=1.2, top_p=0.95 are good defaults.
Output format
One atom per line, element x y z in Ångström:
C -4.344237 -2.044144 -0.978303
C -3.501234 ...
...
Parse directly as an XYZ block (no header row is emitted).
Data: GEOM-QM9 + GEOM-Drugs, Cartesian XYZ targets (ori_xyz), plus the
Alpaca instruction dataset for
general-instruction rehearsal
Citation
Paper: How Well Can Frontier Large Language Models Generate Structures? High Quality
Prediction of Molecular Geometries with Help from Fine-Tuning —
arXiv:2607.13350. Please cite the paper and the
underlying GEOM dataset (Axelrod & Gómez-Bombarelli, Scientific Data, 2022) if you
use this model.
bibtex
1@misc{cavanagh2026geomllama,
2 title = {How Well Can Frontier Large Language Models Generate Structures?
3 High Quality Prediction of Molecular Geometries with Help from Fine-Tuning},
4 author = {Cavanagh, Joseph M. and Arnold, Jonathan B. and Alteri, Giovanni Battista
5 and Gritsevskiy, Andrew and Head-Gordon, Teresa},
6 year = {2026},
7 eprint = {2607.13350},
8 archivePrefix = {arXiv},
9 url = {https://arxiv.org/abs/2607.13350}
10}
