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> For other versions, use one of these documentation indexes:
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## Release 1.0.0

### Summary

Initial release of the NVIDIA NIM for RFD4-Proteina, a generative protein
design model developed by the Baker Laboratory in collaboration with NVIDIA.

### Key Features

* Seven design tasks: `generation`, `motif_scaffolding`,
  `enzyme_design`, `small_molecule_binder`, `antibody_design`,
  `partial_diffusion`, and `inverse_folding`. Refer to
  [Overview](/nim/bionemo/rfd4-proteina/overview#supported-tasks) for what each does.
* Two input dialects: a self-contained annotated mmCIF, or a condition
  spec plus its referenced structure files.
* Three execution backends: `oss_eager` and `oss_torch_compile` (the
  upstream reference implementation, uncompiled or `torch.compile`-compiled),
  and `bioir`, an NVIDIA-accelerated trunk built on the BioNeMo Inference
  Runtime. `bioir` is the default; selectable per deployment via
  `NIM_BACKEND`. Refer to
  [Configuration Alternatives](/nim/bionemo/rfd4-proteina/configuration-alternatives).
* Three named recycle schedules, spelled as the checkpoint's own
  configuration: `thinking_low` (110 trunk passes), `thinking_medium` (185,
  the default) and `thinking_high` (295), all at 60 integration steps. A
  `sampling.recycle_schedule` block accepts custom `[steps, depth]` bands for
  deployments that want to explore beyond them, bounded by
  `NIM_MAX_TRUNK_PASSES`. Refer to [Recycle Schedules](/nim/bionemo/rfd4-proteina/prerequisites#recycle-schedules).
* Reward-guided beam search: an optional search block turns a request
  into a beam search over the generation trajectory, branching and pruning
  candidates against a weighted combination of metrics rather than returning
  one unguided sample. A search runs one replicate at a time
  (`sampling.batch_size: 1`); refer to [Replicates and Batching](/nim/bionemo/rfd4-proteina/prerequisites#replicates-and-batching).
* Batched replicates: `sampling.num_replicates` is evaluated in a single
  forward pass by default, and `sampling.batch_size` trades that back for
  per-design seed replay or for a lower memory ceiling. The replicate count
  carries no fixed limit, since what a GPU can serve depends on the token
  count; an oversized request is answered with `out_of_memory` (HTTP 413)
  naming the fields to change. Refer to [Replicates and Batching](/nim/bionemo/rfd4-proteina/prerequisites#replicates-and-batching).
* Design metrics selected by tag, including TMol-based interface energies
  (`ligand_interface`). A tag the running deployment cannot satisfy is
  rejected at request time rather than silently producing no output. The
  `antibody` tag is a refolding tag despite not being named `*_refolding`,
  and like the others needs a mounted oracle; refer to
  [Oracles](/nim/bionemo/rfd4-proteina/oracle).
* Mountable refolding oracle: an operator-mounted structure-prediction
  oracle can score each design and report eleven in-silico pass criteria
  (unconditional, PPI, antibody, DNA, motif-scaffold, internal-coordinates,
  small-molecule-binder, AME, AME-no-ligand, DISCO, developability) as part of
  the response's aggregated metrics. No oracle is built in; refer to
  [Oracles](/nim/bionemo/rfd4-proteina/oracle).

### Model Checkpoints

This release serves the following checkpoints.

| Component   | Checkpoint                                      |
| ----------- | ----------------------------------------------- |
| Flow model  | `chk_epoch_00000169_step_000000340000-ema.ckpt` |
| Autoencoder | `2026_09_04_release_ae_z16_400k-ema.ckpt`       |

> **Note**
>
> These checkpoints are not generally available. They are pre-release
> checkpoints, available only through this NIM, and are not part of the Baker
> Laboratory's public RFD4-Proteina release. Results from this release can
> differ from the public model.

Every response reports both names in its reproducibility metadata.

### Known Issues

* **No refolding oracle is built in.** Every `*_refolding` metric tag is
  rejected with `422` and all eleven in-silico pass criteria are unavailable
  until an operator mounts a `reward.py`; a reward-guided search can only
  weight the non-oracle metrics until then. This is a deliberate design
  choice rather than a defect -- refer to [Oracles](/nim/bionemo/rfd4-proteina/oracle) -- but it does
  mean in-silico success rate is not reportable out of the box.
* **The first request against each token-count bucket is slow.** The NIM
  reports ready as soon as its weights load and does not pre-warm; the first
  request of a given size pays a compilation cost once per bucket per
  container start, measured between 1.9x and 9.8x steady state. To avoid it,
  send one request per bucket you expect to use before serving traffic. Refer
  to [Cold-Start Behavior](/nim/bionemo/rfd4-proteina/performance#cold-start-behavior).
* **A reward-guided search cannot run batched replicates.** `search` with
  `sampling.num_replicates > 1` requires `sampling.batch_size: 1`, because a
  batch leaves no way to attribute a beam's survivors to the replicate they
  came from. Refer to [Replicates and Batching](/nim/bionemo/rfd4-proteina/prerequisites#replicates-and-batching).
* **The public RFD4-Proteina checkpoints are not supported.** The
  checkpoints in the Baker Laboratory's public release (`2026_10_15_*`) use
  a new model architecture, and this release cannot load them. Support is
  planned for a later release. Refer to [Using Your Own Model Checkpoints](/nim/bionemo/rfd4-proteina/configuration-alternatives#using-your-own-model-checkpoints).

### Roadmap

A future release will support the Baker Laboratory's public RFD4-Proteina
checkpoints (`2026_10_15_*`), with their expanded residue vocabulary.