Llama-3.1-8B-GeomLlama-xyz-Drugs-Large
Built with Llama. Built with Axolotl.
GeomLlama-xyz-Drugs-Large 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).
Unlike the jointly-trained "hybrid" GeomLlama models (zmatrix, xyz), this is a specialized model trained on GEOM-Drugs-Large alone, focusing it on larger drug-like molecule geometries. It is one of a family of four specialized models (Fenske–Hall Z-matrix or Cartesian XYZ, on GEOM-QM9 or GEOM-Drugs-Large); the others are:
- Llama-3.1-8B-GeomLlama-zmat-QM9-Large
- Llama-3.1-8B-GeomLlama-xyz-QM9-Large
- Llama-3.1-8B-GeomLlama-zmat-Drugs-Large
Quick start
The model was trained in the Alpaca instruction format. Reproduce the exact inference prompt used for the paper's numbers:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "THGLab/Llama-3.1-8B-GeomLlama-xyz-Drugs-Large"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
smiles = "Cc1cccc(CSc2nnnn2-c2ccccc2)c1"
prompt = (
"### Instruction:\n"
"You can generate accurate molecular coordinates from a prompt "
"containing a SMILES string.\n\n"
"### Input:\n"
"Generate a realistic equilibrium geometry for the molecule with the "
f"following SMILES string in xyz format: {smiles}\n\n"
"### Response:\n"
)
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=3072, do_sample=True, temperature=1.0, top_p=0.95)
print(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).
View z-matrix or xyz coordinates easily at doublemolview.streamlit.app.
Training
- Base: meta-llama/Llama-3.1-8B-Instruct
- Framework: Axolotl
- Method: LoRA (r = 32, α = 16, dropout 0.05, all linear layers), merged into the base weights
- Epochs: 4 · LR: 3e-4, cosine · Optimizer: adamw_bnb_8bit
- Sequence length: 4096, sample packing · Precision: bf16
- Data: GEOM-Drugs-Large — the full DMCG-replicated split (19,860 training molecules, ~2.0M conformers, with hydrogens), Cartesian XYZ targets (
ori_xyz), plus a ~393k-example subset of the Tulu-3 SFT mixture 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.
@misc{cavanagh2026geomllama,
title = {How Well Can Frontier Large Language Models Generate Structures?
High Quality Prediction of Molecular Geometries with Help from Fine-Tuning},
author = {Cavanagh, Joseph M. and Arnold, Jonathan B. and Alteri, Giovanni Battista
and Gritsevskiy, Andrew and Head-Gordon, Teresa},
year = {2026},
eprint = {2607.13350},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.13350}
}
License
Governed by the Llama 3.1 Community License. By using this model you agree to its terms. Built with Llama.