code/README.md

Restoring TD-PRM on a new server

This HF dataset repo holds everything needed to bring the project back except what can be downloaded again or is too large to keep: no model weights, no model checkpoints, no environment, no dataset text.

in this repo	what it is
code/v1/tdprm, code/v2/tdprm, code/v3/tdprm	the three code versions (v3 = current; mirrored every sync)
tdprm_results.tar	all of $TDPRM_HOME minus weights/envs/dataset text: runs/ (metrics, logs, traces, configs), evals/ (every final test incl. samples), states/ (fitted spectral / OpenJev / JevK5 calibration states), rollouts/ (base samples, token ids only), queue/ (plans, queue state, job scripts, logs), report/ (gates, probes, services.sh), prmbench/ (features, results), runs_aborted/, data/manifest/ and filter summaries, env.sh; plus the status pages, the step-credit report and the session notes at the archive root
README.md	the live status page (math / ProverQA)

code	version	adds
v1	original pipeline	sampling, spectral/JEV states, latent credit arms, PRM baselines, GRPO (TRL + vLLM), gpuq queue, live reports, HF sync
v2	data pipeline v2	credit/v2.py, rl/pool.py (skip-solved, hard retest slots), report/pilot_v2.py
v3	current	credit/v3.py, rl/provider_v3.py, states/reader.py (HC count-back over a transition table), step-credit evaluation (prmbench/), Biology/MedQA, Countdown/K&K and commonsense domains, zero-sum process reward, checkpoints

Job scripts pin their code version (TDPRM_REPO): v1 at /home/colligo/tdprm, v2 at /home/colligo/tdprm_v2_repo, v3 at /home/colligo/tdprm_v3_repo.

One-shot restore

Requirements: Linux, 8x A100 80 GB (any NVIDIA GPU with driver >= 580 for the CUDA 13 wheels), ~250 GB free for models + data + results (checkpoints of new runs need ~3 GB (Qwen) / 6.4 GB (Llama) each), an HF account that has accepted the Llama-3.2 licence.

curl -LsSf https://astral.sh/uv/install.sh | sh && export PATH="$HOME/.local/bin:$PATH"
uvx --from huggingface_hub hf auth login
uvx --from huggingface_hub hf download JoshuaW1997/tdprm-results code/v3/tdprm/scripts/restore_from_hf.sh \
    --repo-type dataset --local-dir /tmp/tdprm_restore
bash /tmp/tdprm_restore/code/v3/tdprm/scripts/restore_from_hf.sh

Defaults: TDPRM_HOME=/mnt/localssd/tdprm, code under CODE_ROOT=/home/colligo (other locations work: the script rewrites the absolute paths in env.sh and the job scripts). Steps it runs:

1. downloads this repo; copies the three code trees into place;
2. extracts the results archive into $TDPRM_HOME, un-gzips the files the sync compressed (SYNC_GZIPPED.txt), puts the status pages / reports / session notes in $CODE_ROOT;
3. creates the exact environment with uv from code/v3/tdprm/requirements.lock.txt (Python 3.12, torch 2.13 cu130, vLLM 0.29, TRL 1.15, transformers 5.17);
4. downloads all models (scripts/download_models.sh: Qwen2.5-1.5B-Instruct, Llama-3.2-3B-Instruct, Qwen2.5-Math-PRM-7B, Qwen2.5-Math-7B-PRM800K, VersaPRM, Qwen3-4B (v3 reader), JevK5, OpenJev);
5. re-prepares every dataset from its public source (tdprm.data.prepare) and rebuilds the base-model pass-rate training pools (data/train/*.parquet, which cannot be re-sampled identically) from data/manifest/ -- ordered ids and per-record content hashes. data_manifest restore must end with N/N data files match the manifest; a mismatch means an upstream dataset changed (the listed files show which);
6. downloads ProcessBench / PRMBench (tdprm.prmbench.data).

Then start the services listed in $TDPRM_HOME/report/services.sh (queue schedulers, status pages, this HF sync), each in its own detached session. Queue jobs that were running when the project was paused show as running: use python -m tdprm.queue.gpuq --plan <plan> retry <job> to re-run them.

Not restored (by design)

• runs/*/checkpoints/ (best / final policies): re-evaluating a finished run on a new test set needs a retrain.
• Model weights and the Python environment (downloaded / rebuilt above), dataset text (re-prepared above).
• Prompts and gold answers inside traces and rollouts (every record keeps its prompt id).