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Model: THGLab/Llama-3.1-8B-GeomLlama-zmatrix
Source: Original Platform

README.md

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+---
+license: llama3.1
+base_model: meta-llama/Llama-3.1-8B-Instruct
+library_name: transformers
+pipeline_tag: text-generation
+language:
+- en
+tags:
+- chemistry
+- molecular-geometry
+- conformer-generation
+- smiles
+- z-matrix
+- geom
+- llama
+- axolotl
+- arxiv:2607.13350
+datasets:
+- GEOM
+---
+
+# Llama-3.1-8B-GeomLlama-zmatrix
+
+**Built with Llama. Built with Axolotl.**
+
+GeomLlama-zmatrix 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 a **Fenske–Hall Z-matrix** (internal coordinates: bond length, bond
+angle, dihedral, referenced to previously placed atoms). It is one of two models
+from our paper; the companion model, [Llama-3.1-8B-GeomLlama-xyz](https://huggingface.co/THGLab/Llama-3.1-8B-GeomLlama-xyz),
+emits Cartesian XYZ 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
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "THGLab/Llama-3.1-8B-GeomLlama-zmatrix"
+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 Fenske-Hall Z-matrix 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
+
+Each line places one atom. The first token is the element; the remaining tokens
+are the internal coordinates relative to earlier atoms:
+
+```
+O 1                          # atom 1: reference origin
+C 1 1.388                    # atom 2: bonded to atom 1 at 1.388 Å
+H 2 1.090 1 105.769          # atom 3: bond to 2, angle to 1
+C 2 1.547 1 117.304 3 129.0  # atom 4: bond, angle, dihedral
+...
+```
+
+Atoms are emitted in an arbitrary order with **no atom labels or connectivity
+block** — the model learns geometry from the SMILES alone. Parse the Z-matrix back
+to Cartesian coordinates with a standard NeRF/internal-to-Cartesian routine.
+
+> View z-matrix or xyz coordinates easily at
+> [doublemolview.streamlit.app](https://doublemolview.streamlit.app).
+
+## Training
+
+- **Base:** meta-llama/Llama-3.1-8B-Instruct
+- **Framework:** [Axolotl](https://github.com/axolotl-ai-cloud/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-QM9 + GEOM-Drugs, Fenske–Hall Z-matrix targets (`ori_fh`), plus the
+  [Alpaca](https://huggingface.co/datasets/tatsu-lab/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](https://arxiv.org/abs/2607.13350). Please cite the paper and the
+underlying GEOM dataset (Axelrod & Gómez-Bombarelli, *Scientific Data*, 2022) if you
+use this model.
+
+```bibtex
+@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](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE).
+By using this model you agree to its terms. Built with Llama.