model card: what this is, and what it deliberately does not contain

README.md

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+---
+license: other
+tags:
+  - research-artifacts
+  - sampling
+  - boltzmann
+library_name: pytorch
+---
+
+# drift-sampler artifacts
+
+Recorded training checkpoints for an ongoing research project on **one-step
+samplers for unnormalized Boltzmann targets** `p(x) ∝ exp(-E(x))`.
+
+**68 files, 3.2 GB.** This repository is a storage backend, not a released
+model. On its own it does very little: the weights are only meaningful together
+with the training/inference code, which is not public.
+
+## What this is
+
+Checkpoints from three eras of the project, laid out exactly as the code repo
+expects them:
+
+| prefix | count | what |
+|---|---|---|
+| `checkpoints/blessed/` | 34 | 2026-06, RTX 5090 workstation |
+| `checkpoints/hpc_2026-07/` | 30 | 2026-07-29 → 08-01, NUS Hopper H200 benchmark suite |
+| `checkpoints/aldp/` | 4 | 2026-07-31 alanine-dipeptide pipeline run-through |
+
+They are kept because they are **not reproducible**: the trainer never set
+`deterministic=True`, and these runs degrade late, so the best weights exist
+only in an intermediate checkpoint that re-running does not recover.
+
+## What this is NOT
+
+- **No code.** No model definition, no drift kernel, no training loop.
+- **No method configuration.** The checkpoints carry no `hyper_parameters`
+  block — none of the kernel scales, drift coefficients, or objective weights
+  that define the method are present. Only tensors, optimizer state, and
+  PyTorch-Lightning bookkeeping.
+- **No datasets.** These are target-density benchmarks; there is no training
+  data. The energy function is the only access to the target.
+- **No paper.** The method is unpublished.
+
+Consequently a `state_dict` here tells you tensor shapes and little else.
+
+## Usage
+
+Fetched by the (private) code repository's own tooling, which verifies every
+file against a sha256 index:
+
+```bash
+python scripts/artifacts.py pull
+python scripts/artifacts.py verify
+```
+
+Direct download works too, but the paths are only meaningful inside that repo.
+
+## Provenance and contact
+
+Produced on the NUS Hopper cluster (PBS, H200) and an RTX 5090 workstation.
+Per-file provenance — which run produced which weight, and which reported
+number it corresponds to — lives in the code repository, not here.
+
+This is research work in progress. If you want to use or cite any of it, please
+get in touch first.