Hugging Face publication: two ready-to-upload artifacts

Model repository

Intended ID: ayjays132/PhillNet-Recall. This is a custom-library memory-policy checkpoint, not an AutoModelForCausalLM checkpoint or a hosted text-generation API. The model card deliberately omits a misleading text-generation pipeline tag.

The source ZIP is the complete model repository. Extract it and enter the PhillNet-Recall directory. A dry run needs no token and makes no network request:

python scripts/verify_release.py
python scripts/publish_hf.py

Authenticate locally, not in a chat message, then create a new private repo:

python -m pip install '.[hub]'
hf auth login
python scripts/publish_hf.py --publish

To create it publicly, explicitly pass --public --publish. The script refuses existing repositories and does not change their visibility. It checks the logged-in personal namespace, manifest paths, file hashes and sizes, then uploads only those files. Live SQLite stores, credentials and unlisted files are not uploaded.

If a repository already exists, this script stops. Review its existing files, visibility and revision before using a separately authorized Hub update process. No automatic overwrite, history purge, or account permission change is performed.

Public synthetic demo Space

Extract PhillNet-Recall-Space-1.2.0.zip beside the model directory. It contains a Dockerfile, Space metadata, the installable wheel, the UI and its server, plus a manifest. The app executes four fixed synthetic demonstrations. It accepts no personal memory input and creates a fresh in-memory database for each request.

python scripts/publish_hf.py --target space --space-dir ../PhillNet-Recall-Space
python scripts/publish_hf.py --target space --space-dir ../PhillNet-Recall-Space --public --publish

Intended Space ID: ayjays132/PhillNet-Recall-Demo. The default visibility remains private unless explicitly changed on creation. Creating a model repo does not create a running service; the Docker Space is a separate artifact. Neither repo was published by this release build. The Docker image and live Hub rendering were not tested. The app's actual Python routes and all four demonstrations were tested.

Loading the published policy

Install the supplied wheel and Hugging Face Hub support first. After publication:

from phillnet_recall import RecallMemory, RecallPolicy
policy = RecallPolicy.from_pretrained(
    'ayjays132/PhillNet-Recall', revision='YOUR_REVIEWED_COMMIT_SHA')
memory = RecallMemory('local.sqlite3', policy=policy, tenant='me')

Remote downloads contain data files only; remote Python and native libraries are not executed. trust_remote_code=True is neither needed nor used. Remote download execution was not verified here. Local save_pretrained / from_pretrained are tested. A repository name is not a substitute for pinning and reviewing a revision.

Post-upload verification

Confirm card images render, compare the repository commit to the uploader receipt, download the data at that commit into a clean environment, and rerun the local installation and example. Keep live memory databases out of the model repository. Use authenticated private storage for production memory. The demo is synthetic, not a multi-user memory-hosting product or a Hugging Face endorsement.

Official references: https://huggingface.co/docs/hub/model-cards ; https://huggingface.co/docs/huggingface_hub/guides/upload ; https://huggingface.co/docs/hub/spaces-sdks-docker . Reviewed 2026-11-11.