Giorgio Gilestro
38bb252c20
Enable running the LLM tier on Imperial's HPC (scheduler: PBS Pro / qsub). - hpc/probe.pbs: 10-min 1-GPU reconnaissance job resolving the two unknowns the RCS docs omit -- compute-node internet access and the L40S driver's CUDA version -- plus TMPDIR/disk and available python/cuda modules. - hpc/llm_merge.pbs: scaled run on an L40S (48 GB), offline HF-cache wired, runs configs/llm/merge_hpc.yaml. - configs/llm/merge_hpc.yaml: Qwen2.5-7B-Instruct (fits the L40S) to reduce the noise that left the 0.5B prototype's overall-exceeds sign marginal. - hpc/README.md: the git-based workflow (login-node uv env + model pre-download -> qsub -> rsync results back), confirmed PBS/GPU directives, and the code TODOs for the definitive run (multi-seed, more families, directed/dilution-resistant merge). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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| configs | ||
| figures | ||
| hpc | ||
| paper | ||
| results | ||
| src | ||
| tasks | ||
| tests | ||
| .gitignore | ||
| CLAUDE.md | ||
| Makefile | ||
| pyproject.toml | ||
| README.md | ||
| uv.lock | ||
The Lamarckian Society — Layer 1 (analytical core)
A parametric population-genetics model of knowledge transmission across generations of
learning agents. Knowledge transmission is modelled literally as a Wright–Fisher
process (not by analogy): a model's knowledge is a distribution p_t over K discrete
items; a fixed true distribution p* has a rare tail; each generational step is
"sample from the parent (drift) + mix in fresh real samples (grounding/immigration) +
refit." Model collapse is the loss of rare alleles under drift.
See paper/blueprint.md (the normative build spec),
paper/the-lamarckian-society-v5.md (the perspective paper), and
paper/results-summary.md (a summary of all results).
Reproduce
Environment is a uv venv built from the committed, hash-pinned uv.lock — that
lockfile is the single source of truth for "it runs" (Layer 1 is pure NumPy/SciPy and
bitwise-reproducible from a seed; no container needed).
# one-time: install uv (https://astral.sh/uv)
curl -LsSf https://astral.sh/uv/install.sh | sh
uv sync # build .venv from uv.lock
make test # correctness + scientific-validation tests (the spine of trust)
make layer1 # run experiments E1–E6
make figures # regenerate figures from committed results
Layout
src/knowledge/ Layer 1 package (imported as `knowledge`)
configs/layer1/ one YAML per experiment (E1..E6)
figures/ plot_EX.py — read results.parquet only
tests/ test_correctness.py + test_scientific_validation.py (analytic checks)
paper/ blueprint.md, perspective paper, figure_manifest.md
results/ written artifacts (gitignored; hashes tracked in manifest.json)