Giorgio Gilestro
809e45a5e0
First step from toy models toward real language models, on one 16 GB GPU. New src/llm/ package: procedural task families + exact-match verifier (tasks.py), batched eval (evaluate.py), LoRA specialisation (specialise.py, manual answer-only SFT), weight-space merge via peft add_weighted_adapter (merge.py: soup = averaged deltas, ties = sign-reconciled union), runner (experiment.py, kind llm_merge). Base Qwen2.5-0.5B-Instruct (Apache-2.0); three disjoint hard families (lists/strings/arith); one LoRA specialist each (~90s total). Result (seed 1), reported honestly: - STRONG/robust: the merges are the ONLY models competent across ALL families -- worst-family ~0.25 vs <0.16 for every single specialist (the Fisher-Muller "generalist assembled from specialists" signature, in real LoRA weights). - MARGINAL: "exceeds every parent overall" is only marginal at this scale (soup 0.64 vs best specialist 0.63; ties 0.61 below it). - CAVEAT VISIBLE: averaging dilutes peaks (lists specialist 0.43 -> merge 0.26) -- Layer-1's "merge, don't average" (E4) appearing in real weights. The pipeline works end-to-end; the balance/retention half reproduces; the strict overall-exceeds and soup-vs-ties distinction need scale (bigger base, more/cleaner families, seeds, a dilution-resistant / offspring-selected merge) -- the HPC step. Env: Python 3.14 + transformers 5.13 works; note transformers-5.x apply_chat_template returns a dict. make env-llm / make llm; adapters under gitignored models/llm/, base in the HF cache. figures/plot_llm_merge.py, README, tests/test_llm.py (+3, 125 green). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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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)