---
license: mit
---
# La-Ribo
### RNA Co-Design via Geometry–Latent Flow Matching
Generate RNA sequences and three-dimensional heavy-atom structures together.
[](https://arxiv.org/abs/2610.12236)
[](https://github.com/GENTEL-lab/La-Ribo)
[](https://huggingface.co/GENTEL-Lab/La-Ribo/tree/main/weights)
[](https://github.com/GENTEL-lab/La-Ribo/blob/main/LICENSE)
[Quickstart](#quickstart) · [Models](#models) · [Files](#release-files) · [Dataset](#dataset) · [Citation](#citation)
---
La-Ribo jointly samples a phosphate–sugar–base scaffold and residue-wise latent vectors. A shared autoencoder decodes this representation into nucleotide identities and heavy-atom coordinates.
This repository provides **two pretrained flow models**, their shared autoencoder and normalization statistics, and **10,631 trRosettaRNA2-predicted RNA structures** released with the paper.
## Models
| | La-Ribo-Base | La-Ribo-Tri |
|---|---|---|
| Pair-feature updates | No triangle updates | Triangle updates every 2 blocks |
| Flow layers / latent dimension | 12 / 16 | 12 / 16 |
| Flow checkpoint | `la_ribo_base.pt` | `la_ribo_tri.pt` |
| Config in the code repository | `config/la-ribo-base.yaml` | `config/la-ribo-tri.yaml` |
Both variants use **EMA flow weights**, the same VAE, and the same statistics files. The default sampling settings are **200 Euler steps**, scaffold power **1.25**, and latent power **2.0**.
## Quickstart
### 1. Install the inference code
Install [uv](https://docs.astral.sh/uv/getting-started/installation/), then:
```bash
git clone https://github.com/GENTEL-lab/La-Ribo.git
cd La-Ribo
uv python install 3.14
uv sync --locked
```
**Requirements:** Python 3.14+; CPU, including macOS, or NVIDIA CUDA on Linux. CUDA inference requires a CUDA 13-compatible driver.
### 2. Download the model bundle
Run this from the cloned code directory. The dataset is optional and is not included in this command.
```bash
uvx --from huggingface_hub hf download GENTEL-Lab/La-Ribo \
config.json \
weights/la_ribo_base.pt \
weights/la_ribo_tri.pt \
weights/la_ribo_vae.pt \
weights/latent_stats.pt \
weights/coarse_stats.pt \
--local-dir .
```
### 3. Generate with La-Ribo-Tri
```bash
uv run --no-sync python sample_latent_flow.py \
--config config/la-ribo-tri.yaml \
--checkpoint weights/la_ribo_tri.pt \
--autoencoder_checkpoint weights/la_ribo_vae.pt \
--latent_stats weights/latent_stats.pt \
--coarse_stats weights/coarse_stats.pt \
--num_res 80 \
--seed 2027 \
--device auto \
--output samples/tri_80_seed2027.pdb
```
Generate with La-Ribo-Base
```bash
uv run --no-sync python sample_latent_flow.py \
--config config/la-ribo-base.yaml \
--checkpoint weights/la_ribo_base.pt \
--autoencoder_checkpoint weights/la_ribo_vae.pt \
--latent_stats weights/latent_stats.pt \
--coarse_stats weights/coarse_stats.pt \
--num_res 80 \
--seed 2027 \
--device auto \
--output samples/base_80_seed2027.pdb
```
| To change… | Set… |
|---|---|
| RNA length | `--num_res`; the paper benchmark covers 40–150 nt |
| Random sample | `--seed`, together with a new output filename |
| Compute device | `--device cpu` or `--device cuda`; `auto` selects CUDA when available, otherwise CPU |
| NVIDIA GPU | Prefix the command with `CUDA_VISIBLE_DEVICES=0`, replacing `0` with the desired index |
**Outputs:** a PDB containing the generated sequence and heavy-atom coordinates, plus a neighboring `.summary.json` with sampling parameters and provenance. Output directories are created automatically; existing samples are not overwritten.
These commands perform sampling. Refolding and designability evaluation are separate procedures. Matching CPU/CUDA seeds alone does not guarantee identical structures because the backends use different random-number implementations.
## Release files
| File | Contents |
|---|---|
| [`config.json`](https://huggingface.co/GENTEL-Lab/La-Ribo/blob/main/config.json) | Variant definitions, architecture dimensions, file locations, and sampling defaults |
| `weights/la_ribo_base.pt` | Base EMA state dictionary |
| `weights/la_ribo_tri.pt` | Tri EMA state dictionary |
| `weights/la_ribo_vae.pt` | Shared autoencoder state dictionary |
| `weights/latent_stats.pt` | Latent whitening statistics |
| `weights/coarse_stats.pt` | Scaffold scaling statistics |
The model files are **pure tensor state dictionaries**. EMA is already applied to the flow weights, so no `--use_ema` flag is required. Both statistics files are needed to reconstruct the correct latent and coordinate scales; they contain only inference-required values and format fields.
## Dataset
The `data/` directory contains **10,631 RNA sequences**, corresponding **trRosettaRNA2-predicted structures**, and **per-residue pLDDT scores**. It is optional for pretrained-model inference.
```text
data/
├── metadata.csv
└── trran_data_publish.tar.gz
└── /
├── model_1_relaxed200.pdb
└── plddt.csv
```
```bash
uvx --from huggingface_hub hf download GENTEL-Lab/La-Ribo \
--include "data/*" --local-dir .
tar -xzf data/trran_data_publish.tar.gz -C data
```
Dataset metadata and archive format
`metadata.csv` contains one row per RNA:
| Column | Description |
|---|---|
| `name` | Entry ID and archive directory name, e.g. `000011_bpRNA_RFAM_7168_L89` |
| `original_task_id` | Task ID from the prediction pipeline |
| `source_db` | `bpRNA-1m90` (6,458) or `RNAStrAlign` (4,173) |
| `source_id` | ID in the source database |
| `length` | Sequence length: 16–252 nt, median 77 |
| `mean_plddt` | Mean pLDDT, on a 0–1 scale: 0.80–0.99 |
| `min_residue_plddt` | Minimum per-residue pLDDT |
| `max_residue_plddt` | Maximum per-residue pLDDT |
| `cluster_rep_id90_cov95` | Sequence-cluster representative: 10,139 clusters |
| `sequence` | RNA sequence |
The approximately 539 MB archive extracts to `trran_data_publish/`. Each entry directory contains `model_1_relaxed200.pdb` and `plddt.csv`; the CSV columns are `Residue_Index` and `plddt`.
## Download statistics
The model-bundle command includes `config.json`, which Hugging Face uses as this repository's default download-counting signal. Downloading only `.pt` files or files under `data/` does not trigger that default model counter.
HF counts matching GET/HEAD requests server-side. The displayed metric is not a count of unique users or guaranteed completed weight transfers. [How model downloads are counted →](https://huggingface.co/docs/hub/models-download-stats)
## Citation
```bibtex
@article{ma2026laribo,
title = {La-Ribo: RNA Co-Design via Geometry-Latent Flow Matching},
author = {Ma, Runze and Hua, Will and Zheng, Shuangjia},
year = {2026},
eprint = {2610.12236},
archivePrefix = {arXiv},
primaryClass = {q-bio.BM},
url = {https://arxiv.org/abs/2610.12236}
}
```
Released under the **MIT License**. See the [code repository](https://github.com/GENTEL-lab/La-Ribo) for implementation details.