--- title: "RFD4-Proteina-Diffusers" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/9dbcd74f-6cd7-4625-8a43-b13ac25d7e05" updated: "2026-11-13" --- # RFD4-Proteina-Diffusers > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs the RFD4-Proteina protein design model through the Diffusers library, so no native RFD4 package is needed. It suits researchers who design molecules around a target on a CUDA GPU. RFD4-Proteina-Diffusers is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by nvidia ## About ### What it is This repository is the Diffusers version of RFD4-Proteina, a flow-matching model that designs protein sequences and all-atom structures together. It holds the same weights as the original release. You load it with one pipeline class, and the native RFD4 package is not needed at run time. ### Who it suits It suits computational biologists who already work with Diffusers and want to design molecules, such as a binder for a target. A CUDA GPU is recommended. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Biotech | Protein designers | Generate a binder around a target structure | Designing a binder of 50 to 120 residues for a given target | | Research | Machine learning researchers | Run the model through a familiar library | Loading the pipeline in a Diffusers workflow | | Structural biology | Structure analysts | Read designed coordinates back from CIF files | Inspecting the output structure of a design run | People who prefer the native RFD4 package can use the original release instead. ### Where to use it - Python 3.11 or newer with Diffusers - A computer with a CUDA GPU - The rfd4-proteina-prep package, which prepares the inputs | Use case | Who | What they get back | | --- | --- | --- | | Binder design | Protein designers | Designed atom positions and residue types, saved to a CIF file | | Pipeline research | Machine learning researchers | A pipeline made of a transformer, an autoencoder and a scheduler | ### How to set it up - Python 3.11 or newer - Diffusers installed from GitHub, since the card says the next release is still pending - The matching rfd4-proteina-prep package, which supplies chirality and element features - A CUDA GPU, which the card recommends 1. Install the Hugging Face hub package. 2. Download the model files. 3. Install Diffusers from GitHub and the prep package, as the card's quickstart shows. ```sh pip install -U huggingface_hub ``` ```sh hf download nvidia/RFD4-Proteina-Diffusers ``` ### How to use it Load the pipeline in BF16 and move it to the GPU. Fetch an annotated CIF file that marks the parts to keep and the parts to design, then prepare its features. An older Diffusers fails to load this repository instead of sampling wrongly. ```python import torch from huggingface_hub import hf_hub_download from diffusers import RFD4ProteinaPipeline from rfd4_proteina_prep import prepare_cif, write_cif pipe = RFD4ProteinaPipeline.from_pretrained("nvidia/RFD4-Proteina-Diffusers", dtype=torch.bfloat16).to("cuda:0") ``` ```python input_cif = hf_hub_download("nvidia/RFD4-Proteina-Diffusers", "examples/protein_binder/input.cif") features, atoms = prepare_cif(input_cif, seed=42, device=pipe.device) ``` | Tool | What it does | | --- | --- | | RFD4ProteinaPipeline | The pipeline that bundles the transformer, the autoencoder and the scheduler | | prepare\_cif | Turns an annotated CIF file into features for the pipeline | | write\_cif | Writes the designed result back to an annotated CIF file | ### Example You fetch the example input from this repository, which sets a PD-L1 target and asks for a binder of 50 to 120 residues. You prepare its features and run the pipeline. The card's quickstart is cut off before the output step. ### For newcomers and experts This is a research pipeline, so start with the card's example input rather than your own molecule. Make sure the GPU, Python version and both packages are in place first. The weights come from the pretrained base after a LoRA fine-tune was merged in. The original base and the adapter are kept on a separate branch. The released sampler recipe is called thinking\_medium. It uses 60 steps and 180 trunk passes. The model runs in BF16, though the files are stored in FP32. Annotated CIF structure → prepare\_cif builds the features → Pipeline runs transformer and autoencoder → Result is written back to CIF ### Good to know - License: BSD 3-Clause, covering the model and weights. - Cost to run: none named. You need a suitable GPU, and the card names no paid service. - Data it reaches: it downloads files from the Hugging Face Hub, and fetches the example input from this repository. - Not for: older Diffusers versions, which cannot load this repository. Released sampler recipe | Label | Value | | --- | --- | | Steps | 60 steps | | Trunk passes | 180 passes | ## README ````markdown # RFD4-Proteina-Diffusers Runs the RFD4-Proteina protein design model through the Diffusers library, so no native RFD4 package is needed. It suits researchers who design molecules around a target on a CUDA GPU. Made by nvidia on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: This repository is the Diffusers version of RFD4-Proteina, a flow-matching model that designs protein sequences and all-atom structures together. It holds the same weights as the original release. You load it with one pipeline class, and the native RFD4 package is not needed at run time. Who it suits: It suits computational biologists who already work with Diffusers and want to design molecules, such as a binder for a target. A CUDA GPU is recommended. ```table Industry | Who uses it | How they use it | Example Biotech | Protein designers | Generate a binder around a target structure | Designing a binder of 50 to 120 residues for a given target Research | Machine learning researchers | Run the model through a familiar library | Loading the pipeline in a Diffusers workflow Structural biology | Structure analysts | Read designed coordinates back from CIF files | Inspecting the output structure of a design run ``` People who prefer the native RFD4 package can use the original release instead. Where to use it: - Python 3.11 or newer with Diffusers - A computer with a CUDA GPU - The rfd4-proteina-prep package, which prepares the inputs ```table Use case | Who | What they get back Binder design | Protein designers | Designed atom positions and residue types, saved to a CIF file Pipeline research | Machine learning researchers | A pipeline made of a transformer, an autoencoder and a scheduler ``` How to set it up: - Python 3.11 or newer - Diffusers installed from GitHub, since the card says the next release is still pending - The matching rfd4-proteina-prep package, which supplies chirality and element features - A CUDA GPU, which the card recommends 1. Install the Hugging Face hub package. 2. Download the model files. 3. Install Diffusers from GitHub and the prep package, as the card's quickstart shows. ```sh pip install -U huggingface_hub ``` ```sh hf download nvidia/RFD4-Proteina-Diffusers ``` How to use it: Load the pipeline in BF16 and move it to the GPU. Fetch an annotated CIF file that marks the parts to keep and the parts to design, then prepare its features. An older Diffusers fails to load this repository instead of sampling wrongly. ```python import torch from huggingface_hub import hf_hub_download from diffusers import RFD4ProteinaPipeline from rfd4_proteina_prep import prepare_cif, write_cif pipe = RFD4ProteinaPipeline.from_pretrained("nvidia/RFD4-Proteina-Diffusers", dtype=torch.bfloat16).to("cuda:0") ``` ```python input_cif = hf_hub_download("nvidia/RFD4-Proteina-Diffusers", "examples/protein_binder/input.cif") features, atoms = prepare_cif(input_cif, seed=42, device=pipe.device) ``` ```table Tool | What it does RFD4ProteinaPipeline | The pipeline that bundles the transformer, the autoencoder and the scheduler prepare_cif | Turns an annotated CIF file into features for the pipeline write_cif | Writes the designed result back to an annotated CIF file ``` Example: You fetch the example input from this repository, which sets a PD-L1 target and asks for a binder of 50 to 120 residues. You prepare its features and run the pipeline. The card's quickstart is cut off before the output step. For newcomers and experts: This is a research pipeline, so start with the card's example input rather than your own molecule. Make sure the GPU, Python version and both packages are in place first. The weights come from the pretrained base after a LoRA fine-tune was merged in. The original base and the adapter are kept on a separate branch. The released sampler recipe is called thinking_medium. It uses 60 steps and 180 trunk passes. The model runs in BF16, though the files are stored in FP32. ```flow Annotated CIF structure prepare_cif builds the features Pipeline runs transformer and autoencoder Result is written back to CIF ``` Good to know: - License: BSD 3-Clause, covering the model and weights. - Cost to run: none named. You need a suitable GPU, and the card names no paid service. - Data it reaches: it downloads files from the Hugging Face Hub, and fetches the example input from this repository. - Not for: older Diffusers versions, which cannot load this repository. ```chart Title: Released sampler recipe Steps: 60 steps Trunk passes: 180 passes ``` ## On Hugging Face, 13 November 2026 - Library: diffusers - Parameters: 178M - Weights: about 714 MB in F32; about 89 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 403 - Likes: 5 - License: bsd-3-clause - Languages: en - Based on: nvidia/RFD4-Proteina - Tags: diffusers, safetensors, biology, protein-design, biomolecular-design, structure-generation, flow-matching, annotated-cif ## Links - Model: https://huggingface.co/nvidia/RFD4-Proteina-Diffusers - Files: https://huggingface.co/nvidia/RFD4-Proteina-Diffusers/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download nvidia/RFD4-Proteina-Diffusers ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [nvidia/RFD4-Proteina-Diffusers on Hugging Face](https://huggingface.co/nvidia/RFD4-Proteina-Diffusers) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | RFD4-Proteina-Diffusers (this page) | 403 | 5 | 178M | bsd-3-clause | 2026-10-20 | | [RFD4-Proteina](https://mindket.com/p/e89dbf1d-b52e-4519-a7fe-5a631e5f48ef) | 14,235 | 45 | 436M | bsd-3-clause | 2026-10-30 | | [Hy-Nomas](https://mindket.com/p/9a697cce-2966-4f2d-bb74-d8d6874fbecb) | 90,896 | 153 | — | apache-2.0 | 2026-11-04 | | [Carbon-X](https://mindket.com/p/05be414c-f3d1-458d-8746-27178bdc319f) | 5,018 | 303 | 14.8B | apache-2.0 | 2026-10-29 | | [Cola DLM](https://mindket.com/p/b4dd84c1-dd8f-48de-b432-c7071e24c476) | 93 | 51 | — | apache-2.0 | 2026-06-18 | | [PAN 2](https://mindket.com/p/b0dad2b4-3f72-4c44-83bd-34dbe74c0172) | 10 | 83 | 29.2B | apache-2.0 | 2026-11-12 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-20 - First published: 2026-10-15 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open nvidia/RFD4-Proteina-Diffusers on Hugging Face](https://huggingface.co/nvidia/RFD4-Proteina-Diffusers) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/9dbcd74f-6cd7-4625-8a43-b13ac25d7e05) - [Its page on Mindket](https://mindket.com/p/9dbcd74f-6cd7-4625-8a43-b13ac25d7e05)