Clarity pass over the main text (36-item audit), Discussion rewrite and cut,
acknowledgements, Souly et al. as ref 62, lettered SI panels, model section
moved under Results; plus the untracked curriculum/society/compose/smol
configs, runners, figures, stats and tests that the SI already cites.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Y64o8FKP7rCuXzC48pxpMm
Re-ran the specialist-merge experiment at a capable base (Qwen2.5-7B-Instruct,
200 tests/family) on one L40S GPU of Imperial's CX3 HPC (8 min walltime). The
two caveats the 0.5B prototype left marginal are now resolved:
- "exceeds every parent overall" is clean: both merges 0.87 vs best specialist
0.77 (+10 pts), and above every specialist on every family.
- dilution vanishes: at 0.5B averaging diluted the lists-specialist
(0.43->0.26); at 7B the merge beats it (0.62>0.57). Dilution was a
small-model artefact -- a capable base composes rather than dilutes, which
softens E4's "merge, don't average" once the parents are strong.
The figure title is now data-driven (reports ">" for 7B, "~" for 0.5B).
Adds the hpc/ smoke job script and the llm_merge walltime trim. Results synced
to results/llm_merge_hpc/ (parquet gitignored per the reproducibility contract).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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>
