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| - **No paper.** The method is unpublished.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