---
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- **No paper.** The method is unpublished.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.