# Publishing ALMA code and weights

## Current terminology update

The current release uses public segmentation terminology throughout. Four
segmentation configs were renamed. The QL2 segmentation checkpoint now uses
a public provenance identifier; all tensors are identical, but its file hash
changed. Upload the refreshed folder and synchronize the config directory
so obsolete config filenames are removed from the current hosted revision:

```bash
hf upload gdslab/alma ./release_artifacts . --delete="configs/*"
```

This follows the CLI's folder synchronization behavior: local configs are
uploaded and obsolete remote configs are removed. Existing repository history
is retained. After the upload, use its new commit hash with
`scripts/set_weight_urls.py`, refresh/upload the manifest, and then commit/push
the code changes. The user completed this upload at commit
`aa51bb453ec2aac74e74e1d84e2d0903625575e7`; all six URLs are now pinned to
that revision. Upload the refreshed manifest/docs before pushing GitHub code.

- Code: https://github.com/gdslab/alma
- Model weights: https://huggingface.co/gdslab/alma
- Code license: MIT, with upstream attribution in LICENSE and NOTICE.
- Weight license: MIT, authorized by the authors; see WEIGHTS_LICENSE.

Six verified weights (about 1.44 GB) are staged in `release_artifacts/`.
This directory and downloaded weights are excluded from Git. Originals remain
in the research repositories. Run the commands below yourself; authentication
and uploads are not performed by the preparation step.

## 1. Validate inside a GPU allocation

```bash
cd /scratch/gautschi/goel65/ALMA
conda activate pointm2ae
ALMA_WEIGHTS_DIR=release_artifacts python -m unittest discover -s tests -v
```

Confirm the CUDA test passes rather than skips. Record the actual result in
the audit/change log. Full training and paper-metric reproduction have not
been rerun; do not infer those from the smoke test.

## 2. Refresh upload metadata

The canonical HF model card is version-controlled at
`model_cards/huggingface.md`. Refresh its staged copy after edits:

```bash
cp model_cards/huggingface.md release_artifacts/README.md
cp LICENSE WEIGHTS_LICENSE NOTICE CITATION.cff CODE_CHANGE_LOG.txt release_artifacts/
cp checkpoints/manifest.yaml release_artifacts/manifest.yaml
cp -r configs model_cards docs release_artifacts/
(cd release_artifacts && sha256sum -c SHA256SUMS)
```

All six checksums must report OK. This does not rewrite weight files.

## 3. Upload to Hugging Face

Use a machine/node with internet access and an account with write access to
the `gdslab` organization:

```bash
pip install -U huggingface_hub
hf auth login
```

If the model repository does not exist, create it as public:

```bash
hf repos create gdslab/alma --repo-type model
```

Skip creation if it already exists. Upload the prepared folder:

```bash
hf upload gdslab/alma ./release_artifacts .
```

Commands follow the [official HF CLI guide](https://huggingface.co/docs/huggingface_hub/guides/cli).
Keep the full 40-character commit hash from the upload result or the model
repository's commit history.

## 4. Pin and verify download links

Replace `COMMIT_HASH` with that actual weight-upload commit hash:

```bash
python scripts/set_weight_urls.py --repo-id gdslab/alma --revision COMMIT_HASH
cp checkpoints/manifest.yaml release_artifacts/manifest.yaml
hf upload gdslab/alma release_artifacts/manifest.yaml manifest.yaml
```

The manifest-only upload creates another commit; weight URLs should remain
pinned to the earlier commit containing the verified weights. Never insert
a made-up commit or replace the revision with `main`.

Use a fresh directory so existing local files cannot bypass download testing:

```bash
ALMA_VERIFY_DIR=$(mktemp -d /tmp/alma-download-verify.XXXXXX)
for regime in QL1 QL2; do
  for kind in encoder mae seg; do
    python scripts/download_weights.py "ALMA-${regime}-Indiana-${kind}" --out-dir "$ALMA_VERIFY_DIR" || break 2
  done
done
```

Require six `verified=` messages. A download/checksum failure must be resolved
before publishing the code release. The verification directory holds about
1.44 GB and can be removed after verification.

Record the weight commit, completed verification, and manifest change in
CODE_CHANGE_LOG.txt and the workspace's global audit log before committing.
The initial upload commit was `5307569f57e16b59eca53484c42304d8ddd8c38e`.
The terminology update is uploaded at
`aa51bb453ec2aac74e74e1d84e2d0903625575e7`, and the manifest is repinned.
Fresh public-download verification remains to be completed.

## 5. Publish the GitHub repository

Initial local state on 2026-09-30: no commits, no remote, branch `master`.
Create an empty public GitHub repository named `gdslab/alma` if needed.
Do not generate another README, license, or gitignore.

Inspect remote history before the first push:

```bash
git ls-remote https://github.com/gdslab/alma.git
```

For an empty remote and the initial local state:

```bash
git branch -M main
git remote add origin https://github.com/gdslab/alma.git
git add .
git diff --cached --stat
git diff --cached --name-only
```

Review the staged files: no weights, datasets, credentials, or outputs should
be present. Then:

```bash
git commit -m "Release ALMA pretraining and segmentation"
git push -u origin main
git tag -a v0.1.0 -m "ALMA initial public release"
git push origin v0.1.0
```

If a remote or commits already exist, inspect them and preserve their history
before proceeding; do not force-push or reset the remote. Authenticate with
your GitHub account using your established Git credential setup.

## Completion

Check both public repository pages, license display, cross-links, all six
weight files, and the pinned manifest. From a fresh GitHub checkout, install
the package and download the weights using the manifest; run the GPU checks
with that checkout to confirm the public release path works.

CITATION.cff contains the title and authors. Add manuscript DOI/venue when
available. For an optional archival DOI, follow
[Zenodo's GitHub release instructions](https://help.zenodo.org/docs/github/archive-software/github-upload/).
Git-ignored weights are not included in a code archive; archive them separately
if a DOI for the model files is needed.